Update Dataset (PR → snapshot → steps → grapher)

Use this skill to run a complete dataset update with Claude Code subagents, keep a live progress checklist, and pause for user approval only when something needs attention.

Paired skill — keep in sync. [/review-data-pr](../review-data-pr/SKILL.md) is the reviewer-side counterpart of this skill: it verifies the outcomes of the author-side steps defined here. Whenever you add, remove, or change a workflow step in this file, check whether review-data-pr/SKILL.md needs a matching reviewer-side check (and add it in the same commit if so). The reverse also holds — see the mirror note there. The creation-side skills [/create-dataset](../create-dataset/SKILL.md) and [/create-snapshot](../create-snapshot/SKILL.md) belong to the same family: they point into this file's canonical sections (§5b-bis sanity bounds, §5c harmonization audit, §6b metadata quality, §6c metadata checklist + link verification, §6d scheduled issues, and the snapshot-related Guardrails), so when one of those sections changes, check whether the create skills need a matching edit in the same commit too.

The two announcements are owned elsewhere. [/data-updates-comms](../data-updates-comms/SKILL.md) owns the internal Slack form (step 9) and [/data-update-announcement](../data-update-announcement/SKILL.md) owns the public /latest post (step 9b). Both steps here are delegation stubs on purpose — put wording, format, and style guidance in those skills, never back into this file.

Inputs

• <namespace>/<old_version>/<name>
• Get <new_version> as today's date by running date -u +"%Y-%m-%d"
• A bare <shortname> (no namespace/version) is also valid — it's what owid-issues reminder bodies use. Resolve it to <namespace>/<oldversion>/<shortname> via the DAG: rg "/<shortname>:?$" dag/ -g "*.yml" | grep -v "^dag/archive" — the :? matters because active entries are YAML keys ending in : (a $-anchored pattern without it only matches dependency lines), and archived entries must never be resolution targets. Take the latest active version; ask the user if the short name is ambiguous across namespaces. Several space-separated short names (/update-dataset <shortname1> <shortname2>) mean a grouped update of related datasets: run the full workflow for each, on one shared branch/PR.

Read the dataset's scheduled-issue body before running etl update (step 6d locates the update-*.yml workflow — peek at it now, not only at 6d): it often carries grouped-update instructions naming companion datasets that must bump together, e.g. a deflator ("US PPI New Office Construction also needs to be updated alongside to adjust for inflation"). Also check the target's garden DAG entry for same-vintage dependency datasets (a data://garden/... dep sharing the old version date is usually a companion). Seed etl update with every companion snapshot so the chains move in one run — updating only the main dataset leaves the derived series (e.g. inflation-adjusted) with a silent NaN tail for the new months.

Optional trailing args:

• branch: The working branch name (defaults to current branch)

Assumptions:

• All artifacts are written to workbench/<short_name>/.
• Persist progress to workbench/<short_name>/progress.md and update it after each step.
• Persist reusable update facts to workbench/<short_name>/update-context.yml as they are discovered. This is the canonical context artifact for the PR description, review handoff, and data-updates-comms.

Progress checklist (maintain, tick live, and persist to progress.md)

• Parse inputs and resolve: channel, namespace, version, shortname, oldversion, branch
• Clean workbench directory: delete workbench/<short_name> unless continuing existing update
• Run ETL update workflow via etl-update subagent (help → dry run → approval → real run)
• Add yourself to dataset.owners in the new garden .meta.yml (don't reorder; preserve existing names and markers)
• Catalog # NOTE: / # TODO: comments carried over from the old step files into notestocheck.md
• Detect any sanitychecks functions and their log-control flags; append to notesto_check.md
• Create or reuse draft PR and work branch
• Update snapshot and compare to previous version; capture summary
• Meadow step: run + fix + diff + summarize
• Garden step: run + fix + diff + summarize
• Surface new indicators: check meadow + garden diffs (and snapshot columns when meadow hardcodes a column subset) for new-version columns absent from the old; warn user + PR body, flag new-but-dropped columns, route rename pairs to the step-7 mapping
• Review sanity_checks output (enable log flag, re-run, scan log, revert flag) — if none found and the garden step does non-trivial logic, recommend adding them; if present but missing value bounds (positive / [0,1] / [0,100] per indicator type), suggest those too (see 5b-bis)
• Country harmonization audit: validate .countries.json against canonical regions (flag provider regions not yet in the regions dataset → /add-provider-regions), audit .excluded_countries.json, scan garden log for missing/unused/unknown warnings
• Region-provider drift: if this dataset's aggregates are in regions.yml (defined_by: <provider>), check whether the new version changed the provider's region set or country membership; if so, update regions.yml and re-propagate via /add-provider-regions
• Grapher step: run + verify (skip diffs), or explicitly mark N/A
• Re-evaluate each catalogued # NOTE: / # TODO: against fresh data; delete resolved workarounds + comments together, or record status in PR body
• Check metadata: typos, Jinja spacing, style guide compliance
• Verify indicator-metadata coverage, dataset.updateperioddays, snapshot DVC datepublished and citationfull year (etl update copies both verbatim — bump to the producer's real release date / year, or to date_accessed / current year if the source doesn't publish one), and that all URLs resolve (HEAD-check) and every #fragment matches a real anchor in the target page (anchor pass, see 6c)
• Always suggest the optional adversarial data & metadata review (/adversarial-data-review) — verify metadata claims against the producer's fetched documentation and cross-check values against independent sources. Surfacing this offer to the user is mandatory every run (even when you recommend skipping it); only the run is opt-in — it's heavy (~25–45 web calls), so skip by default and run on user opt-in or visible red flags (see 6c-bis)
• Scheduled-issue workflow check (owid-issues): locate the dataset's update-*.yml (exact / fuzzy / group match), verify cron vs the observed release cadence + updateperioddays, filename convention, and that the issue body says to run /update-dataset <short_name>; auto-fix body/title, ask before cron changes or new workflows — commits go straight to owid-issues main (see 6d)
• Commit, push, and update PR description
• Run indicator upgrade on staging and persist report
• Run the hardcoded-time-bounds audit (check-hardcoded-years) after all remaps — numeric minTime/maxTime/timelineMin/MaxTime/map.time pins on every surface carrying the new indicators (charts, MDim/explorer views, narrative charts, article time= links), graded against the new data's latest time; a pin below it means the update is invisible on that surface — propose "latest" fixes with user sign-off (see step 7)
• Always suggest the optional empty-entity audit (check-empty-entities) after all remaps — it sweeps every surface (charts, map columnSlug, MDim views, explorer views, narrative charts, gdoc country= references). Surfacing this offer to the user is mandatory every run (even when you recommend skipping it); only the run is opt-in — it can consume many tokens on widely-charted datasets, so run on user opt-in or when the remap touched many views; regressions vs production = fix, pre-existing = document (see step 7)
• Run the referencing-prose audit (standard) — read the prose of every surface the sweep found (articles, data insights, key-chart blurbs) for quantitative claims this update invalidates; an unbounded claim ("has grown 1,300-fold", "now accounts for X%") goes stale; a time-bounded one ("by late 2025 it had reached $62B") does not on an append-only update, but does if this update revised the period it names. Read the sweep's coverage gaps too — a "nothing stale" verdict covers only the surfaces swept. Hand the recomputed numbers to content — never edit published prose yourself (see step 7)
• Update update-context.yml with published chart count and 1–3 chart views for the public announcement
• Render Slack announcement via data-updates-comms, save to workbench, post @codex review as a separate PR comment, and notify user to post it to #data-updates-comms
• Draft the public-facing "Data update" post for OWID /latest via data-update-announcement (Mode A) — two versions, user picks, then the Google Doc in /Data updates, and hand the user the link (not added to the PR). The skill declines when we posted about this data less than six months ago; a declined post is a completed item — record the eligible date, don't override
• Address Codex review comments (fix valid ones + resolve all threads)
• Run downstream-dependency check (rg "<namespace>/<oldversion>/<shortname>" dag/ -g "*.yml" | grep -v "^dag/archive"); for each consumer outside the dataset's own chain, decide with the user whether to bump in this PR or document under "Downstream dependencies" for a follow-up PR (see "Downstream dependency check" section below for details)
• Run the silent-breakage check whenever downstream consumers were repointed in this PR: confirm the buildkite/etl-automated-staging-environment PR check is green (red = a consumer crashed on staging, and the report under-reports until it's fixed; .venv/bin/etlr --modified --continue-on-failure --private is the optional local equivalent for small fan-outs), then triage the data-diff report — every red "− lost N data point(s)" entry in its Top-changes list and every 🔴-tier dataset (see "Silent-breakage check" section) and run the full-report audit probes (structural / World / raw-country / >30% / wipe-vs-edge per loss)
• Ask the user whether to remove the old version; if yes, remove+archive its DAG entries now and relocate the new entries into the old slot, but KEEP the old step files until review sign-off — the consecutive-version review diffs them from disk; deleting the files is the final commit before merge (see "Removing the old version & reordering the DAG") — don't forget this step
• Hand off the QA links to the user (Anomalist + Chart Diff on the staging branch, plus the data-diff report) — this is the final step

Persistence:

• After ticking each item, update workbench/<short_name>/progress.md with the current checklist state and a timestamp.
• Append every generalizable lesson to workbench/<shortname>/lessons.md the moment you hit it, not at the end. One entry per lesson: what you expected, what actually happened, and the general rule — plus which skill file should own it. Reconstructing these from memory after a long session loses the specifics that make a lesson usable (the exact column name, the threshold that separated signal from noise, the check that would have caught it earlier), and the ones worth keeping are exactly the ones that cost time mid-run. Fold them into the skills when the user asks, following [feedbackskilllessonswriteback](../../../CLAUDE.md) conventions: phrase each as a general pattern with the incident as a trailing one-liner, and re-read the target skill first — a long-running branch can be several skill revisions behind master, so the section you remember may already have been rewritten by someone else's session.

Checkpoints — when to pause

Default: keep going. Run through the full workflow without stopping unless one of the conditions below is met.

Stop and ask the user when:

• A step fails and the fix is ambiguous (multiple reasonable approaches, or you're unsure of the correct one)
• Data structure changed significantly (columns removed/renamed, large row count drops, schema changes that may affect charts)
• Country harmonization has new unmatched countries that need manual decisions
• The snapshot requires a manual download or credentials you don't have
• Indicator upgrade had imperfect matches (< 100% similarity) that need human review
• Anything that could silently break charts or lose data

Don't stop for:

• Routine assertion count updates (just update them and note in the summary)
• Clean step runs with only row increases
• Expected warnings (SettingWithCopyWarning, known unmapped territories)
• Straightforward filename/version reference updates

When you do stop, present a concise summary of the issue and what options exist.

When the update isn't a drop-in version bump

Some updates carry structural changes that make the standard rename-only flow the wrong tool. Recognise them up front and adjust the workflow.

Triggers — any of these means you're in restructure territory, not a version bump:

• short_name changes (producer rebranded the dataset).
• File format/schema changes (wide → long, different file extension with a different column set, new dimensions).
• Policy/indicator set changes substantially (splits, dropped composites, newly added areas).
• Score semantics change (e.g. binary → continuous with subnational coverage).

Workflow adjustments:

1. Skip etl update. The rename-only flow copies the old step files into a new folder — useless when the schema is different. Author the new step chain by hand, using the old version as inspiration but not as a starting copy.
2. Add the new chain to the DAG before removing the old. Leave both chains active while you build and validate v2; remove the v1 entries only once v2 is on staging and the chart remap is queued or done.
3. Decide on naming convention upfront. Ask the user whether to preserve v1 short_names where they map cleanly, or to adopt the source's fresh naming scheme. Fresh naming is cleaner but means the auto-Indicator-Upgrader can't help.
4. Hand-curate the v1 → v2 indicator mapping. When shortnames change entirely, the auto-upgrader has nothing to match on, but the Indicator Upgrader also matches on title — so if v2 titles are descriptive (full sentences rather than the bare shortname), you can hand the user a table of v1 title → v2 title pairs and they can drive the chart remap from there. Generate this table from the v1 meta.yml + the v2 grapher catalog.
5. Before remapping a chart onto a successor indicator, diff the chart's config against the new indicator's shape. Three things silently break on a v1 → v2 remap and none of them fails a build: (a) the chart's pinned selectedEntityNames may not exist in the successor (v1 steps often computed regional aggregates the producer doesn't publish — check the retired step's aggregation method via git show <archive-marker-sha>:<path> and rebuild the aggregates in garden the same way, e.g. population-weighted means); (b) pinned yAxis min/max sized to the old index's range can clip the new one; (c) the subtitle/FAUST may describe the old construction ("combines 18 policies") and become factually wrong. When the producer publishes several related successor indices, their codebook usually says which is the primary measure and which is a transparency baseline — cite that framing when picking the chart's indicator. (LGBTI: the v1 rights-index chart pinned World + six continents, an axis max of 13 against a successor peaking at 18.7, and a v1-specific subtitle — all three needed changes.)
6. Defer the Slack and /latest announcements until charts have been remapped. Both posts depend on charts.publishedcount and charts.selectedviews from the v2 chain. Drafting them before the remap gives the wrong count (zero) and no representative views. Tell the user to ping you when the chart remap is done, then run steps 8 / 9 / 9b.

For the long-format with dimensions sub-case specifically (e.g. one row per (country, year, <dim1>, <dim2>)), use the modern OWID pattern:

• Meadow + garden: tb.format(["country", "year", <dim1>, <dim2>, ...], sort_columns=True).
• Aggregations: paths.regions.addaggregates(tb, indexcolumns=[...full key...], regions=REGIONS, aggregations={...}).
• Grapher: pass long tables through unchanged; the framework auto-expands them into per-cell variables.
• Metadata: variables are keyed by the long-column name, with <% if <dim> == "X" and <dim2> == "Y" %>...<% endif %> Jinja blocks inside title, description_short, display.name. Grep this repo for tb.format(["country", "year" with more than two index entries to find current reference examples.
• Jinja coverage: after building the grapher dataset, verify every active (dim1, dim2) cell renders a non-empty value — read every column from the built grapher dataset and assert metadata.title is non-empty. A dimension combination with no matching <% if %> branch ships an untitled indicator.

Workflow orchestration

1. Initial setup

- Check if workbench/<shortname>/progress.md exists to determine if continuing existing update - If starting fresh: delete workbench/<shortname> directory if it exists - Create fresh workbench/<short_name> directory for artifacts

1. Run ETL update command (etl-update subagent)

- Inputs: <namespace>/<oldversion>/<shortname> plus any required flags - Pick the URI that matches what's actually changing: - If the source data is changing — new source files, modified extractor, anything that affects the snapshot output — run from the snapshot URI with --include-usages. This bumps the whole chain (snapshot → meadow → garden → grapher) to the new version together: `` etl update snapshot://<ns>/<oldv>/<short>.<ext> --include-usages ` - **Foundational / widely-used datasets (e.g. wb/*/incomegroups, regions, population): add --direct-only.** Plain --include-usages follows usages transitively and would try to version-bump every downstream consumer (incomegroups has ~85 across 15 dag files). --direct-only restricts the bump to steps sharing the dataset's own namespace/version/shortname, i.e. just its chain. Caveat: --direct-only excludes sibling steps with a different shortname that belong to the same chain (e.g. incomegroupsaggregations, which the grapher step also depends on) — pass those as extra seed steps so the grapher doesn't end up mixing a new-version garden with an old-version sibling. Dry-run and confirm the proposed set is exactly the chain before executing: ` etl update snapshot://<ns>/<oldv>/<short>.<ext> data://garden/<ns>/<oldv>/<sibling> --include-usages --direct-only --dry-run ` - If only garden logic / metadata is changing and the source data is unchanged, run from the garden URI. This bumps garden and grapher only; snapshot and meadow stay on the old version. - Either way, run etl update once. Don't call it separately per channel — that leaves stale version references in the DAG (e.g., new garden pointing to old meadow). - Perform help check, dry run, approval, then real execution; capture summary for later PR notes - After running, always verify the dag file: grep for the old version and confirm all internal references between the new steps point to the new version (e.g., garden depends on new meadow, not old meadow). - **etl update writes the new entries in the flat DAG form — convert them to the nested (compact) form now**, while you're in the file, rather than leaving it until archiving (otherwise the flat block tends to survive the whole update unnoticed). See the example and loaddag()` parse-check under "DAG archiving & reordering" step 4.

1a-bis) Add yourself to dataset.owners in the new garden .meta.yml

You've just become a contributor to this dataset, so add your canonical OWID name to its owners: list. Don't reorder — keep the existing primary first; append yourself at the end. Skip if you're already there.

Your canonical name must match an entry in the schema enum (schemas/dataset-schema.json). Resolve it from git config user.name via etl.owners.resolve_owner; if that returns None, add a mapping in etl/owners.py and a row in the schema enum before continuing.

Edit the YAML in place, preserving comments and the existing # review / # backport / # fasttrack markers on other entries.

1b) Check for outdated practices (check-outdated-practices skill) - After etl update creates new step files, run the /check-outdated-practices skill on every new step file — including helper modules that etl update doesn't generate but you copied by hand (e.g. omms.py), since those carry legacy patterns too - The skill reads the extension as the source of truth for the full pattern set (the geo.add aggregation/population helpers are flagged, not just geo.harmonizecountries) — don't rely on a remembered subset - Fix any findings before proceeding — this avoids propagating legacy patterns into new versions - geo.harmonizecountries → paths.regions.harmonizenames is mechanical and safe. geo.addregionstotable → paths.regions.addaggregates changes the aggregation core — prove equivalence with a controlled A/B test, not a diff against the old feather. Build the new garden both ways against the same current catalog (swap the call, rebuild, save output; revert, rebuild, save output) and diff the two. Do NOT conclude "the helper shifts aggregates across all years" from a new-vs-old-feather diff — that conflates the helper with upstream-dataset drift (see step 5). In practice the two helpers are equivalent bar tiny historical edge cases (e.g. one region-year's population residual); if so, modernize. addaggregates also auto-resolves income groups from the DAG, so it's the right tool when you later need WB income-group aggregates.

1c) Catalog # NOTE: / # TODO: comments in the copied step files (don't resolve yet) - Run rg -n "#\s*(NOTE|TODO|FIXME|HACK|XXX):" snapshots/<namespace>/<newversion>/ etl/steps/data/{meadow,garden,grapher}/<namespace>/<newversion>/. - Filter out generic boilerplate (e.g. # NOTE: To learn more about the fields, hover over their names. at the top of .meta.yml). - Save the remaining actionable items to workbench/<shortname>/notesto_check.md — one entry per annotation, recording file path, line number, which step it lives in (meadow/garden/grapher), and what the workaround does. - Don't act on them yet. Resolution requires fresh data and happens after each step's run — see step 6a.

1d) Detect sanity-check logic in the copied step files Sanity checks live in two different forms — detect both:

- Function form — def sanitycheck… / sanitycheck…( call sites. Often gated by a module-level boolean flag (DEBUG, SHOWSANITYCHECKLOGS, LONGFORMAT) that defaults to False to keep normal runs quiet. Examples: etl/steps/data/garden/wb/.../worldbankpip.py (SHOWSANITYCHECKLOGS), etl/steps/data/garden/wid/.../worldinequalitydatabase.py (DEBUG + LONGFORMAT), etl/steps/data/garden/lis/.../luxembourgincomestudy.py (no flag; prints unconditionally via tabulate). - Inline comment form — # Sanity check / # Sanity checks / # sanity check marking an inline assertion block that isn't wrapped in a dedicated function. Very common: etl/steps/data/garden/emdat/.../naturaldisasters.py, etl/steps/data/garden/emissions/.../nationalcontributions.py, etl/steps/data/garden/irena/.../renewablecapacitystatistics.py. These usually have no log flag — the block simply runs on every step execution and either passes or raises.

Run a combined sweep: ``bash rg -n -i "def sanitycheck|sanitycheck\(|#\s*sanity check" \ snapshots/<namespace>/<newversion>/ \ etl/steps/data/{meadow,garden,grapher}/<namespace>/<newversion>/ ``

Append a "Sanity checks" section to workbench/<shortname>/notesto_check.md listing each hit — for each, record: file path + line number, which form (function vs. inline comment), the name of any log-control flag (function form only), and a one-line description of what's being asserted (read the surrounding 5–10 lines).

Don't act yet — the review happens in step 5b once the garden step has been run on the new data.

1. Create PR and integrate update via subagent (etl-pr)

- Inputs: <namespace>/<oldversion>/<shortname> - Create or reuse draft PR, set up work branch, and incorporate the ETL update outputs

1. Snapshot run & compare (snapshot-runner subagent)

- Inputs: <namespace>/<newversion>/<shortname> and <old_version>

Hand-maintained snapshots + editorial data edits. Some snapshots have no urldownload — the .py prompts for --path-to-file and the docstring says the data was "provided by email" / curated by hand. When the update is a small editorial correction (the user gives you the facts directly, e.g. "country X did Y in year Z"), you can produce the new snapshot yourself: copy the previous version's data file (data/snapshots/<ns>/<oldversion>/<file>), change only the specific cells, and assert in a quick script exactly which rows/cells changed (and that all others are byte-identical) before running etls ... --path-to-file <edited>. Then update the .py docstring to document the edit and bump the .dvc datepublished / citationfull year (ask the user whether it's a new producer release or an OWID-applied edit — see step 6c). Verify the user's stated facts against the existing data first — some may already be encoded from a prior release (in this update, one of the two reported events was already in the live snapshot; only the other was a genuine change). Tell the user what's already present rather than blindly re-adding it.

1. Meadow step repair/verify (step-fixer subagent, channel=meadow)

- Run, fix, re-run; produce diffs - Save diffs and summaries - Watch for meadow input checks keyed on absolute row/column positions. Producers quietly restructure their files (e.g. this session, the WB dropped the legend rows above the first country, shifting the row count 239→234 and moving "Afghanistan" from row 10 to row 5). Data extraction that keys off content (drop rows without an id, then melt) survives, but hardcoded tb.loc[N] / exact-row-count asserts break — update them to the new positions/counts and drop a # NOTE so the next maintainer re-checks. The break is the check doing its job; don't loosen it into uselessness.

1. Garden step repair/verify (step-fixer subagent, channel=garden)

- Run, fix, re-run; produce diffs - Save diffs and summaries

Diff against a freshly-rebuilt old version, not the stale feather on disk. The old version's data/garden/.../ feather was built whenever it last ran — possibly against an earlier snapshot of a shared upstream dataset (population, regions, incomegroups). A fresh build of the new version uses the current upstream, so a naive new-vs-old-feather diff shows differences in every population-weighted cell across all years and regions — pure upstream drift that has nothing to do with your change. Before trusting any diff, rebuild the old version on the current catalog and diff against that: ``bash .venv/bin/etlr data://meadow/<ns>/<oldversion>/<short> --private --force --only .venv/bin/etlr data://garden/<ns>/<old_version>/<short> --private --force --only `` The apples-to-apples diff should collapse to just your intended change. Mention the drift separately in the PR (Chart Diff on staging will show it, because the live data is also stale relative to current upstream). This bit me twice in one update — don't skip it.

When NaN can appear on one side, don't let .fillna(False) hide it. In a cell-by-cell diff, (a - b).abs() <= tol evaluates to NaN when exactly one side is NaN; a downstream .fillna(False) then silently drops that real one-sided change. Treat "one side NaN, other side not" as a difference explicitly.

Spot-check derived diff columns before quoting them. A pct-change column built via an assign/sort_values chain on merged Tables can silently misalign against its own rows. Before a revision magnitude goes into the PR body or an announcement, recompute it in a fresh plain-pandas frame straight from the raw old/new files and eyeball one row (old value, new value, pct) for arithmetic consistency — a mismatch on a single row means the whole column is garbage. (This produced "median +8.2%" where direct recomputation gave +14.3% in a real update; the wrong number was already in the PR body before an adversarial re-check caught it.)

Surface new indicators. Collect columns present in the new version but absent from the old — read both the meadow diff (step 4) and the garden diff, because the pipeline may drop columns between the two. etl diff prints additions as green + Column lines, but the ranked HTML report scores them at severity 0 — they sort last and will not surface on their own; read the text/JSON output, or compare column sets directly (set(dsnew[t].columns) - set(dsold[t].columns) per table). Classify each find: - New in meadow and in garden → genuinely new indicator: warn the user and list it in the PR body under a "New indicators" heading — it appears on no chart until someone curates it, needs the same metadata coverage as the rest (see 6c), and may deserve a mention in the update announcement (step 9). - New in meadow but missing from garden → the pipeline drops it (explicit column selection or .drop(columns=...) in garden): surface it as "the source now ships X — currently dropped" and let the user decide deliberately whether to keep it. - Paired + Column / − Column → usually a rename, not an addition — route it to the indicator-upgrade mapping (step 7) instead.

Long-format tables hide additions from the column diff. When a table keys its series by a dimension column (indicator, metric, question, …) with a stable value column, a new series adds rows, not columns — the column-set diff stays empty. Also diff the distinct combinations of the identifying dimensions, not each dimension's values separately — a new series can be a new tuple of existing values (e.g. sex=male and metric=count both existed before, but the (male, count) series is new): set(tbnew.resetindex()[dims].itertuples(index=False, name=None)) - set(tbold.resetindex()[dims].itertuples(index=False, name=None)) where dims is the list of identifying dimension columns — built tables keep them in the index after Table.format(...), hence the reset_index() (with a single dimension this reduces to a value-set diff). The grapher step expands dimension values into separate variables, so the step-7 shortName pairing surfaces these too — but catch them here, before staging.

New companion files at the source are new-indicator candidates too. Every check above diffs within the file we snapshot — a producer adding a separate file to the release (a pre-built index, a summary panel, a codebook-derived extract) is invisible to all of them. When listing the source's files (release page, OSF/Zenodo file API), diff the file inventory against the previous cycle — the baseline is the previous snapshot's .dvc companion-files # NOTE: when the host has no file-history API (/create-snapshot step 6 writes it; refresh it in the new .dvc either way) and treat each new data file as a candidate: ingest it (usually a second snapshot + its own table in the same steps), or record the deliberate skip in the PR body. (Velasco LGBTI: the revision added a composite-index CSV — the successor of a retired dataset that a live chart still ran on — and the column diff could never see it.)

If the meadow step itself selects a hardcoded column subset (tb[[...]], usecols=, .drop(columns=...)), a new source column never reaches any diff — grep the meadow step for such selections and, when present, compare the raw snapshot column set old-vs-new (snap.read_*().columns) to catch additions dropped at the door.

5b) Review sanity-checks output (only if step 1d catalogued any) Handling depends on the form catalogued in step 1d.

Function form with a log-control flag (e.g. SHOWSANITYCHECKLOGS, DEBUG): 1. Flip the flag to True at the top of the garden step file. 2. Re-run the garden step, capturing output: ``bash .venv/bin/etlr data://garden/<namespace>/<newversion>/<shortname> --private --force --only \ > workbench/<shortname>/sanity_checks.log 2>&1 ` 3. Review the log: scan for AssertionError, error, warning, dropped, outliers flagged by country/year, unexpected totals. Surface actionable findings in the PR description under a "Sanity-check findings" collapsed section. 4. Revert the flag to its original value (usually False) before committing. Verify with git diff` that the garden file has no unintended changes.

Function form with no flag, or inline # Sanity check(s) comment blocks: 1. Read each catalogued block (pull 5–15 lines of context around the hit) to understand what invariant is being tested. 2. Important: a sanity check can enforce its finding either by raising (assert, raise) or by logging (paths.log.warning, .critical, even .fatal). Logging variants do NOT fail the step — so "step 5 passed" is not proof that every invariant held. If the block uses logging, re-run the step and scan stdout/stderr for the relevant keywords; don't trust the exit code alone. 3. For non-trivial invariants (monotonicity, totals, bounds), also spot-check qualitatively against the fresh garden output via a short .venv/bin/python snippet. 4. Record any anomalies under "Sanity-check findings" in the PR description. No log artifact to keep here since the step's own output is the evidence.

In either form: if sanity_checks raise AssertionError on the new data, stop and decide with the user whether the assertion needs a threshold bump, whether upstream data genuinely broke, or whether the invariant is obsolete. If the check only logs, treat a new/expanding set of warnings the same way — they're the signal the sanity check was written to produce.

Watch for silent-delete patterns. Some sanitychecks functions also mutate the table — e.g. worldbankpip's sanitychecks drops rows that fail invariants and reports the count via the log-control flag. With the flag off the deletions still happen; the reviewer just never learns which rows disappeared. When reading a sanity_checks function, scan for drop, filter, tb = tb[...] — anything that removes rows — and list every deletion in the PR body, not just the warning counts. If the deletion seems newly applicable to upstream fixes (e.g. the row should no longer be anomalous in the new release), that's a candidate for removing the workaround entirely.

5b-bis) Recommend sanity checks when the garden step lacks them (or lacks value bounds) Runs after the garden step is built. Two triggers: - Step 1d catalogued no sanity checks and the garden step does more than a straight load-and-format (harmonization, column drops/renames, aggregations, derivations) — recommend adding sanitycheckinputs / sanitycheckoutputs. A Codex review will flag this anyway; better to handle it during the update. - The step has checks but you can spot missing value-bound coverage of the kinds below — suggest the additions.

Ground every threshold in the built data before writing it. Run min/max/nunique/value_counts per indicator on the built garden table first — never write plausible-sounding bounds. The classic trap: "% of GDP" indicators look like percentages but legitimately exceed 100 (IMF PFMH: gross debt up to 495%, expenditure 595% in crisis years; UK's post-Napoleonic 260% debt is on a published chart). A blind 0–100 assert fails the build on day one.

Suggested checks by indicator type (propose the applicable ones, not all):

Implementation conventions: constants at the top, run() first, check functions below run(); sanitycheckinputs(tb) right after loading meadow, sanitycheckoutputs(tb) right before paths.createdataset(...); plain assert with messages that name the offending values. Reference example: [imf/2026-06-12/publicfinancesmodernhistory.py](../../../etl/steps/data/garden/imf/2026-06-12/publicfinancesmodern_history.py).

**sanitycheckoutputs runs before tb.format(...), so column names are still as the code produced them — format() lowercases/underscores them afterward.** If the step builds columns like f"{col}{status}pop" where status is "Legal"/"Illegal"/"missing", the pre-format columns are statusLegalpop (mixed case), not statuslegalpop. Select such columns case-robustly (e.g. [c for c in tb.columns if c.endswith("_pop")] then filter on c.lower()), or you'll get a KeyError that only surfaces at runtime. Keep update-specific facts (e.g. "country X is Legal in year Y") out of the committed checks — verify those via the garden diff at update time so the checks stay valid across future releases.

Negative-test the checks: after the step passes on real data, simulate each failure mode (rename a column, corrupt a flag, push a value out of bounds) and confirm the matching assertion fires — a check that never trips is untested code.

5c) Country harmonization audit Run after the garden step completes (and after 5b if it ran). Verifies that the country entities reaching the garden output are canonical, and that the mappings/exclusions consumed by paths.regions.harmonizenames(...) are well-formed. Output: workbench/<shortname>/harmonization_audit.md.

Modern API. Garden steps should be calling paths.regions.harmonizenames(tb, countrycol=..., countriesfile=..., excludedcountriesfile=...) — the wrapper in etl/datahelpers/geo.py:1874. If you find a step still using the deprecated geo.harmonize_countries(...) directly, step 1b's /check-outdated-practices should already have flagged it; treat that as a separate cleanup. The audit below is API-agnostic — both call sites end up emitting the same three warning strings. Some garden steps don't use the harmonizer at all and instead assign country inline in Python (no .countries.json involved); for those, the JSON checks below have nothing to look at — the garden-output check in step 5 is what catches non-canonical entities, so always run it.

Source of truth. Canonical names come from two datasets, both consulted by the harmonizer:

- data/garden/regions/2023-01-01/regions — countries, continents, and OWID-defined aggregates. The runtime authority is paths.regions.tbregions["name"]. This is built from etl/steps/data/garden/regions/2023-01-01/regions.yml plus a merge with regions.codes.csv and field defaults — don't parse the YAML in isolation or you'll miss the legacy entries and produce false positives. - data/garden/wb/<latest>/incomegroups — the four World Bank income-group aggregates (High-income countries, Upper-middle-income countries, Lower-middle-income countries, Low-income countries). OWID treats the latest version of this dataset as the official one, so the audit must resolve the version dynamically (don't pin a date — it goes stale when WB publishes a refresh). The names live in the classification column of the incomegroupslatest table.

The audit's "canonical" set is the union of these two. A .countries.json entry looks like "Source name": "Target name" — the audit checks that every target name (the value the source gets harmonized to) appears in either dataset. Anything else is flagged.

1. Capture a fresh garden log: ``bash .venv/bin/etlr data://garden/<namespace>/<newversion>/<shortname> --private --force --only \ > workbench/<short_name>/harmonization.log 2>&1 ``

2. Scan the log for the three harmonization warnings. These are emitted by etl/datahelpers/geo.py (excluded list) and lib/datautils/owid/datautils/dataframes.py (mapping warnings) — the wording is stable: ``bash rg -n "missing values in mapping\.|unused values in mapping\.|Unknown country names in excluded countries file:" \ workbench/<shortname>/harmonization.log ` For each warning, the entity list follows on subsequent lines (because harmonizecountries() is called with showfull_warning=True` by default). Capture them.

3. Validate .countries.json target names against canonical regions + income groups. Each entry maps a source name (key) to a target / harmonized name (value); this check looks at the values. For each garden step in this update: ```python import json from pathlib import Path from owid.catalog import Dataset

# Resolve the canonical regions dataset dynamically (latest built version). # Don't pin a date — when the regions step version advances, a hard-coded path # would validate against a stale catalog and flag valid targets as non-canonical. regionsdirs = sorted(Path("data/garden/regions").glob("*/regions")) if not regionsdirs: raise RuntimeError( "No data/garden/regions/<version>/regions built locally — the audit can't " "run without the canonical regions catalog. Build it first with " ".venv/bin/etlr data://garden/regions/<latest>/regions --private." ) tbregions = Dataset(str(regionsdirs[-1]))["regions"] canonicalregions = set(tbregions["name"].dropna().astype(str))

# Add OWID's official income-group aggregates to the canonical set, if available. # OWID treats the latest incomegroups version as official. This artifact is # often not built locally during a non-income-groups dataset refresh — degrade # gracefully (warn and skip) rather than aborting the audit. igdirs = sorted(Path("data/garden/wb").glob("*/incomegroups")) if igdirs: dsig = Dataset(str(igdirs[-1])) canonicalincome = set(dsig["incomegroupslatest"]["classification"].dropna().astype(str).unique()) else: print( "[WARN] No data/garden/wb/<version>/incomegroups built locally — " "skipping income-group enrichment. The four WB income-group aggregates " "(High/Upper-middle/Lower-middle/Low-income countries) may surface as " "'not in canonical' until you build that dataset." ) canonicalincome = set()

canonical = canonicalregions | canonicalincome

mapping = json.loads(Path("etl/steps/data/garden/<namespace>/<newversion>/<shortname>.countries.json").readtext()) notincanonical = sorted({v for v in mapping.values() if v and v not in canonical}) print("Targets not in OWID's canonical regions or income groups:", notin_canonical)

# Provider regional groupings the source ships (e.g. "Europe (WB)", "Asia and the Pacific (ILO)") # surface here when OWID hasn't added that provider's regions yet. Flag the "(Provider)"-suffixed ones: import re providerregioncandidates = [v for v in notincanonical if re.search(r"\([\w .&-]+\)$", str(v))] print("Look like provider regions not yet in the regions dataset:", providerregioncandidates) `` A non-empty notincanonical` list means the mapping points at entities that aren't registered in either the regions catalog or the income-groups dataset. This isn't automatically a bug — it's a heads-up. Stop and decide with the user before proceeding — same pattern as the global "Checkpoints — when to pause" section at the top of this skill. Common causes (in order from "fix" to "accept"): typo, retired alias used as canonical, casing/whitespace mismatch, or a custom aggregate the source defines that OWID has no equivalent for. For typos/casing — fix the JSON.

If the unmatched targets are a provider's regional grouping that OWID doesn't define yet — the providerregioncandidates above ("(Provider)"-suffixed names like "Europe (WB)", "Sub-Saharan Africa (ILO)"), or the source ships a region/subregion column feeding these — don't just accept them as outside-the-system. The proper fix is to add that provider's regions to the regions dataset with the /add-provider-regions skill (separate PR): they then become canonical, merge with regions/population infrastructure, and get a {provider}_region map indicator, instead of living outside the system. Surface this to the user and offer to run /add-provider-regions. Only for genuinely one-off, non-geographic groupings the regions system shouldn't own (BRICS, G7, G20) — accept and note in the PR description that those live outside the canonical system. For a real new historical region — add an entry to regions.yml in a separate PR.

4. Audit .excludedcountries.json. The file is optional; skip if it doesn't exist: ``python excludedpath = Path("etl/steps/data/garden/<namespace>/<newversion>/<shortname>.excludedcountries.json") if excludedpath.exists(): excluded = json.loads(excludedpath.readtext()) suspiciouscanonical = sorted(set(excluded) & canonical) # Also surface continents and aggregates separately for review aggregates = set(tbregions[tbregions["regiontype"].isin(["continent", "aggregate"])]["name"].dropna().astype(str)) suspiciousaggregates = sorted(set(excluded) & aggregates) print("Excluded entries that ARE canonical regions:", suspiciouscanonical) print("Excluded entries that are continents/aggregates:", suspiciousaggregates) print("Full excluded list for review:", sorted(excluded)) ` suspiciouscanonical is the actionable signal: each entry is a known country/region that we are dropping. Sometimes this is intentional (e.g. dropping "World" rows because the source double-counts them) — surface, don't auto-fix. Pause and ask the user if the list is non-empty. The full list is dumped so the LLM can also eyeball it for entities that aren't in canonical` but look like real countries (typos, alternative names) we should be mapping rather than dropping.

5. Audit garden output entities. Always run this check, regardless of whether .countries.json exists or is populated — JSON mappings describe inputs to the harmonizer, but the entities that actually reach Grapher are whatever sits in the country column/index of the built garden tables. Inline country assignments (e.g. hardcoded tb["country"] = "England and Wales") and post-harmonization mutations both bypass the JSON check entirely; this is the only step that catches them. ```python from pathlib import Path

from owid.catalog import Dataset

gardendir = Path("data/garden/<namespace>/<newversion>/<shortname>") dsgarden = Dataset(str(garden_dir))

entities: set[str] = set() for tname in dsgarden.tablenames: tb = dsgarden[tname] # country can live in the index (after .format()) or as a regular column. if "country" in tb.index.names: entities.update(tb.index.getlevel_values("country").dropna().astype(str).unique()) elif "country" in tb.columns: entities.update(tb["country"].dropna().astype(str).unique()) # tables with no country column are silently skipped (e.g. reference tables)

outputnotincanonical = sorted(entities - canonical) print("Garden output entities not in OWID's canonical regions or income groups:", outputnotincanonical) `` Same triage rules as the JSON-targets check (Python check #3): typo / casing / alias / legitimately custom aggregate. A non-empty list means at least one entity that ships to Grapher isn't registered in either the regions catalog or the income-groups dataset. Stop and decide with the user before proceeding. Common fixes: typo or casing → patch the inline assignment (or .countries.json`, whichever is the source) so the value matches the canonical name; alias → switch to the canonical name; legitimate custom aggregate → accept and note in the PR description that the entity lives outside the canonical system.

6. Write findings to workbench/<shortname>/harmonizationaudit.md with six sections, populated only when non-empty. Each section must list every flagged entity, not just a count — counts alone aren't actionable, the user (or you) needs to read the actual names to judge whether each is intentional. For long lists (>20 entries) group by pattern when the grouping is obvious (e.g. ILO's " (ILO)"-suffixed regions vs. international orgs vs. derived "World ..." aggregates) so the reviewer can scan categories instead of one flat list. Sections: - ## Missing in mapping — countries in source data not in .countries.json (from log warning #1) — list each missing source name - ## Unused mappings — .countries.json entries the data never used (warning #2) — list each unused source→target pair - ## Unknown excluded entries — .excluded_countries.json entries not present in source data (warning #3) — list each - ## Targets not in OWID's canonical regions or income groups — target names from .countries.json that aren't registered in either dataset (Python check #3) — list each target name and the source names that map to it - ## Excluded entries matching canonical regions — possible over-exclusion (Python check #4) — list each - ## Garden output entities not in OWID's canonical regions or income groups — distinct country values found in the built garden tables that aren't in canonical regions or income groups (Python check #5) — list each entity

7. Surface in PR. If any section was populated, add a collapsed "Harmonization audit" section to the PR description (after the per-step sections) with the same listings, not just a summary. Empty sections can be omitted.

When you report progress to the user during the workflow, never just give a count — always include the list (or grouped categories) so they can judge in one glance.

Checkpoint summary: - "Targets not in OWID's canonical regions or income groups" or "Garden output entities not in OWID's canonical regions or income groups" or "Missing in mapping" non-empty ⇒ stop, decide with user. - "Excluded entries matching canonical regions" non-empty ⇒ stop, ask whether each exclusion is intentional. - "Unused mappings" or "Unknown excluded entries" non-empty ⇒ surface in PR description; not a blocker.

5d) Region-provider drift — if this dataset defines OWID regions, propagate the change The harmonization audit (5c-3) catches providers not yet in regions.yml. This check is the counterpart: a provider whose aggregates are already in regions.yml can change its regions on a version bump (new/removed/renamed regions, or shifted country membership), and regions.yml won't update itself.

- Does this dataset own regions? grep "definedby: <provider>" etl/steps/data/garden/regions/2023-01-01/regions.yml (e.g. ilo1/ilo2, maddison, wid, wb, who). No matches ⇒ skip this step. - Re-extract the provider's region→country mapping from the new version — from the same source the regions were originally derived from (a region/subregion column, region entities, or a table-of-contents tier; see /add-provider-regions Step 1) — and diff it against regions.yml (definedby: <provider> members), set-equality per region, plus new/removed/renamed regions. If the new version no longer carries the composition, the provider's section in the world-region-map-definitions article links to the defining source — use it to re-derive the provider's existing regions (regions newly introduced by this update won't be documented there yet — get those from the source or ask the user). - If it drifted: stop and decide with the user (composition changes move every region-aggregated value downstream), then update regions.yml and re-propagate via /add-provider-regions — rebuild garden regions, refresh the {provider}_region grapher indicators + metadata, regenerate owid-grapher regions.data.ts (runRegionsUpdater), and update the article section. - If unchanged: note it and move on.

1. Grapher step run/verify (step-fixer subagent, channel=grapher, add --grapher)

- Skip diff

6a) Re-evaluate # NOTE: / # TODO: items from step 1c against fresh data Now that meadow, garden, and grapher have run on the new data, go back to workbench/<shortname>/notesto_check.md and decide each item's fate. For each entry:

- Identify what the workaround does (read the surrounding code). - Load the affected step's output with owid.catalog.Dataset (or inspect the raw snapshot) and compare corrected vs. uncorrected values. Cross-check the producer's release notes / changelog if available. - If the upstream issue is fixed → delete the workaround and its # NOTE: / # TODO: comments in the same commit, then re-run the affected step (use --force --only, add --grapher for grapher) so downstream artifacts pick up the change. - If the workaround is still needed → leave it and add a one-line status under a PR-description section titled "Not covered in this PR" (e.g. "Sierra Leone ×1000 correction still required — raw value in the 2026 file is still ~1/1000 of plausible"). These are deliberately deferred items the next updater should re-check. Delete the whole section if its last item gets resolved mid-PR. - If you're uncertain → keep it, flag it in the PR description, and ask the user.

Do this before step 6b (metadata checks) so any re-runs triggered by comment-removal happen before the metadata sweep, not after.

6b) Metadata quality checks — run after all ETL steps are built Run all five checks on the newly built garden and grapher datasets so every issue surfaces together. Each skill writes results to the terminal; fix what comes up before moving on.

- Typos — /check-metadata-typos scoped to the current step. Run on each of the new .meta.yml files (garden first, then grapher). Accept or skip each suggested fix. - Jinja spacing — /check-metadata-spacing on the built garden and grapher datasets. Catches template artifacts like doubled spaces or stray newlines that only appear after Jinja rendering. - Style guide — /check-metadata-style on the grapher step. Audits user-facing fields (title, subtitle, descriptionshort, display.name, presentation.) against OWID's Writing and Style Guide. Rules live in .claude/skills/check-metadata-style/STYLEGUIDE.md, so no Notion access is usually needed — the skill checks the file's Last synced from Notion date and refreshes it from Notion (in a separate PR) when it is more than two months old. - Clarity for a general audience — read every user-facing field with non-specialist eyes. The other three skills enforce structure and style; this one judges whether the text is understandable*. - Dimension sweep (dimensional indicators only) — a sentence written for one breakdown renders on all the sibling views, where the view's own filtering can make it false: a caveat that the data doesn't control for X is wrong on the variant grouped by X, a scope word like "all employees" overclaims on a variant filtered to a subgroup, a sentence about a toggle is wrong on views that exist for only one choice of that dimension. Render the text per dimension value (the grapher channel already has it flattened) and read each output as a reader of that chart. Prefer qualifying the wording so it holds everywhere over adding a Jinja branch. Full version: check 6 of the canonical suite in /owid-metadata-generation.

### Clarity checklist (do manually, no skill yet)

OWID readers are not domain experts. Walk each indicator's user-facing fields and flag anything that requires inside knowledge to parse.

Flag and rewrite when you find: - Acronyms or technical terms that aren't expanded the first time they appear - Sentences that only make sense if you already know the data source - Quantitative claims with no unit context (e.g. "burned area" without "in hectares" surfacing somewhere in the user-facing text) - Inconsistent terminology between indicators in the same dataset (e.g. "wildfires" in one, "vegetation fires" in another) - Domain phrases that have a plain-English equivalent (e.g. "anthropogenic emissions" → "human-caused emissions") - Methodology-attribution claims ("following guidance from <agency>…") — open the cited link and confirm it actually says that. Agencies revise methodology, so a claim that was defensible when written can be stale now, and the 6c link check only proves the URL resolves, not the claim (real case: metadata cited BEA guidance for deflating data centers with the office PPI; BEA's 2025 annual update had switched them to an industrial+warehouse composite, and the cited page never contained the guidance at all) - Scope qualifiers present in the origin title but absent from user-facing text — if the source is "Private Construction" / adults-only / market-exchange-rate-only, descriptionshort and descriptionkey must say so; the citation line alone doesn't reach readers - Text that repeats what the reader has already read — a descriptionkey bullet restating another bullet, descriptionshort, or the title at the same level of detail. Unpacking description_short in the first bullet (full definition, how it's measured, what's included) is exactly what the panel is for; saying it again in other words is padding. Merge it into the bullet that already covers the ground, or drop it

When a phrasing is ambiguous, propose a concrete rewrite — don't just flag it.

If any skill rewrites a .meta.yml, re-run the affected step so the built catalog reflects the edits. Add --grapher when the affected step is on the grapher channel — without it the local catalog is updated but staging stays stale, so the step 7 indicator upgrade sees the old text. ``bash # garden / meadow: .venv/bin/etlr <channel>/<namespace>/<newversion>/<shortname> --private --force --only # grapher: .venv/bin/etlr grapher/<namespace>/<newversion>/<shortname> --grapher --private --force --only `` Then re-run the relevant check to confirm zero remaining violations.

6c) Indicator metadata coverage, dataset block, and link verification The other quality checks catch content issues; this step catches missing fields and broken URLs before they reach review.

Snapshot DVC freshness. etl update clones the previous snapshot's .dvc content verbatim except for dateaccessed. Always re-check datepublished and the year in citationfull / attribution under snapshots/<ns>/<newversion>/*.dvc — they will otherwise silently ship the old version's values. Set datepublished to the producer's real release date when discoverable; otherwise copy dateaccessed. Bump the year in citation_full and attribution to match.

- Citation year vs datepublished year. After setting both, check whether the year inside citationfull / attribution matches datepublished's year. If they differ, confirm it's intentional before shipping: it's legitimate when the producer labels the release by edition rather than publish date (e.g. UN IGME's "2025 report" published 2026-03-17 → citationfull (2025), date_published 2026), but otherwise it's a stale citation. When the gap is deliberate, leave a one-line note for the reviewer so they don't re-flag it.

- Last-Modified header as date_published source. When the producer's page states no release date (common on fully JS-rendered sites like the IMF Datamapper), the download URL's HTTP Last-Modified header is a defensible source — it's the server's own timestamp for the file, not an inference. Corroborate it against a release-named filename (e.g. …Dec 2025.xlsx + Last-Modified: Fri, 12 Dec 2025) and note the provenance when reporting to the user. - Stale producer description on a JS-rendered page. If the .dvc meta.origin.description is producer text that may have changed but the page is an SPA shell (static HTML empty, WebFetch 403s, Wayback archives only the shell), don't burn time probing API endpoints and don't rewrite the producer's text to match the data — the blurb can legitimately lag their own releases (FPP shipped 153 countries while the page said 151). Ask the user to paste the page text from their browser, then diff it against the existing .dvc text and apply only the substantive changes. Clipboard pastes flatten typography (curly quotes → straight, en-dashes → hyphens) — keep the existing typographic punctuation unless the words themselves changed.

Mandatory fields per indicator. For every indicator in the garden .meta.yml, confirm these are set (either on definitions.common or per-indicator):

Conditional: if processinglevel: major, every indicator with that level MUST also have descriptionprocessing.

Not mandatory (skip if you don't need them): presentation.titlepublic, presentation.titlevariant, presentation.attribution.

Dataset block. Garden .meta.yml MUST include updateperioddays: ``yaml dataset: updateperioddays: <N> ` This controls the auto-update cadence. Even when the rest of the dataset: block is empty, never strip updateperioddays` — leave the block in place with just that field.

Link verification. Run a HEAD request on every URL in the new .dvc and .meta.yml files (all channels — meadow .meta.yml files matter when they exist). Anything non-2xx is a signal, not a guaranteed break — always double-check before acting: ``bash for url in $(rg --no-filename -No "https?://[^\"' ]+" snapshots/<namespace>/<newversion>/ etl/steps/data/{meadow,garden,grapher}/<namespace>/<newversion>/ \ | sed -E 's/[).,;:>]+$//' \ | sort -u); do printf "%s %s\n" "$(curl -sI -L -o /dev/null -w '%{http_code}' --max-time 15 -A 'Mozilla/5.0' "$url")" "$url" done ` The --no-filename flag prevents rg from prepending path: to each match (otherwise the for-loop tries to curl path:url and every check returns 000). -A 'Mozilla/5.0'` sometimes coaxes a real response out of Cloudflare-fronted hosts, but it doesn't always work — see the next note.

curl non-2xx ≠ broken. Cloudflare-fronted sites (notably ourworldindata.org) can return 404 to curl on URLs that work fine in a browser, depending on edge-node routing, IP geolocation, and cached state. Before treating a 4xx as a real failure:

1. Re-check with WebFetch (the built-in tool). It uses a different code path and a Mozilla/5.0 UA that Cloudflare usually accepts. A 200 with a coherent page body is authoritative — trust it over curl. 2. If WebFetch also fails, sanity-check the Wayback Machine. web.archive.org is itself not fetchable by WebFetch — query the availability API with curl instead: curl -sG "https://archive.org/wayback/available"; --data-urlencode "url=<url>" (the --data-urlencode matters: interpolating a raw URL truncates the lookup at the first & and can false-clear a broken link). A capture is historical evidence, not a live-link check — read the snapshot timestamp: a capture from the last few days supports the bot-blocking hypothesis; an older capture proves nothing about today (the producer may have retired the URL since). When curl and WebFetch both fail, never clear the link on Wayback evidence alone — report it with the capture date and ask the user to open the URL in their browser (the converse also holds: hosts like BLS block both curl and WebFetch while serving browsers fine, so a double failure doesn't prove the link is dead either). 3. No automated check is decisive — a suspected-broken link always ends at flag-and-ask. curl, WebFetch, and Wayback only accumulate suspicion: a missing Wayback capture is non-evidence (plenty of live URLs were simply never archived), and a double curl+WebFetch failure can be bot-blocking (see step 2). The only decisive test is the user opening the URL in their browser — so report the evidence trail (statuses, capture date or absence) and ask before rewriting any external link in metadata. Replacing a working link with a "safer" alternative because of a false-positive is worse than leaving the original. Apply the same restraint here as the global "Checkpoints — when to pause" section.

Fix any genuinely-non-2xx hit on urlmain, urldownload, license.url, or URLs referenced from description / description_key before continuing. The sed strips trailing markdown/punctuation chars (), ., ,, ;, :, >) so URLs inside [text](url) aren't reported as broken because of a stray closing paren.

Anchor fragments — HTTP 200 can't validate them. Servers ignore everything after #, so https://ourworldindata.org/poverty#key-insightsfdd (broken anchor) returns the same 200 as the real #key-insights. For every checked URL that carries a fragment, run a second pass against the page body: ``bash for url in $(rg --no-filename -No "https?://[^\"' ]+" snapshots/<namespace>/<newversion>/ etl/steps/data/{meadow,garden,grapher}/<namespace>/<newversion>/ \ | sed -E 's/[).,;:>]+$//' | sort -u | rg '#'); do page="${url%%#}"; frag="${url##}" case "$frag" in =|/|'!'*) continue;; esac # non-DOM fragments — see below # grep -F: fragments may contain regex metacharacters (#section.2) — ERE would false-OK id="section-2" if curl -sL --max-time 20 -A 'Mozilla/5.0' "$page" | grep -qF -e "id=\"$frag\"" -e "id='$frag'" -e "name=\"$frag\"" -e "name='$frag'"; then printf "OK %s\n" "$url" else printf "NO-ANCHOR %s\n" "$url" fi done ` - Skip non-DOM fragments (the case list): anything containing = (scroll-to-text #:~:text=, Google Sheets #gid=…, PDF #page=…) or / (hash-route state, whether slash-prefixed #/dashboard or not — e.g. FAOSTAT's #data/FBS), plus #!… hashbangs — none correspond to an element id and all would false-alarm. Real anchors are heading slugs and never contain = or /. - NO-ANCHOR is a signal, not proof — same epistemics as the curl false-404 note above. Two false-positive modes: the page renders its anchors client-side (the raw HTML lacks the id), or curl got a Cloudflare challenge page instead of the real body. Confirm before flagging: check the fetched body is the real page (grep for </html> and a plausible <title>), then ask WebFetch whether the page has a section/heading matching the de-slugged fragment (key-insights → "Key insights") — OWID heading anchors are slugged headings, so heading-text presence is the authoritative check. - On a confirmed missing anchor the reader still lands on the right page, just at the top — fix the fragment (grep the body's id="` values for the nearest real anchor) or drop it. Apply the same restraint as above: flag and ask before rewriting — the section may exist under a different slug, or the page may not have rebuilt yet.

Verification. After editing, re-run the affected step (with --grapher if grapher) so the catalog reflects the changes. Then confirm presentation.attributionshort actually landed: ``python from owid.catalog import Dataset ds = Dataset("data/grapher/<ns>/<v>/<shortname>") tb = ds["<table>"] print(tb["<col>"].metadata.presentation.attributionshort) # must NOT be None ` Or after the staging upload: `bash make query SQL="SELECT shortName, attributionShort FROM variables WHERE catalogPath LIKE '%<ns>/<v>/<shortname>%'" ``

6c-bis) Adversarial data & metadata review (always suggest it — only the run is optional) You MUST surface this option to the user on every update, without exception. Even when you judge it unnecessary and recommend skipping, still tell the user it exists and let them decide — do not silently omit it. The heaviness of the check governs whether to run it, never whether to mention it.

Offer to run [/adversarial-data-review](../adversarial-data-review/SKILL.md) on garden/<namespace>/<newversion>/<shortname>. Step 6c verified that the links resolve; this step actually reads the producer's documentation behind them and cross-checks the data against independent sources online — it's the only step that can catch a mistake made by the source itself (unit slips, wrong-year values, stale pre-revision numbers).

**The run is optional because it's the heaviest check in the workflow** — fetching methodology docs plus per-value web searches runs ~25–45 web calls and can consume a lot of tokens and time. Skip the run by default; run it when the user opts in, and actively recommend running it when the update shows red flags that only this step can chase down: large unexplained value churn in the diffs, an in-place source revision, a producer new to us, or editorial claims riding on specific values.

Scope for an update: focus the metadata claim review on new/changed text, and the value cross-checks on the newly added data (latest wave/year) plus that skill's standard anchors; deep-review the top-viewed indicators + anomaly-flagged ones per its prioritization step. Apply its routing table: metadata fixes → edit and re-run the step; confirmed source errors → <shortname>.corrections.yml; unconfirmed suspicions → list under "Not covered in this PR" for the reviewer. Save the report path (ai/adversarial-review-<shortname>-<date>.md) in update-context.yml and summarize any 🔴/🟡 findings in the PR body. Run this before 6d/commit so the fixes land in this PR.

6d) Scheduled-issue workflow check (owid-issues) Every recurring data update is driven by a scheduled GitHub Actions workflow in the owid/owid-issues repo (.github/workflows/update-*.yml) that periodically opens a "Data update" issue. The conventions live in the Notion page "Scheduled data issues": schedule anything updated at least once per year but less than daily; filename update-{namespace}-{shortname}.yml; a cron schedule: trigger + imjohnbo/issue-bot creating the issue. This step runs now because 6c just established the two cadence facts the cron must match — dataset.updateperioddays and the producer's actual release rhythm (source.releasedate / next_release in update-context.yml).

Locate the workflow. The filename convention is loosely followed in practice, so search in widening circles — a miss on the exact name proves nothing: 1. Use the local checkout ~/owid-issues if present (git -C ~/owid-issues pull first); otherwise clone it (gh repo clone owid/owid-issues ~/owid-issues). Don't fall back to a gh api …/contents filename listing — the checks below need file contents (content grep for group workflows, cron/body/assignees parsing), and the commit step needs a working tree anyway. 2. Exact conventional name update-<namespace>-<shortname>.yml → fuzzy filename match (hyphen/underscore swaps, dataset-title words — e.g. update-gallup-ai-indicator.yml covers gallup/aiindicator) → content grep (rg -il "<short_name>|<namespace>|<title words>" ~/owid-issues/.github/workflows/). 3. Group workflows count. One workflow may cover a family of related datasets (e.g. update-climate.yml → /climate-update; the quarterly update-war-ucdp-preview-q*.yml set; an "… + OMM" title covering a derived chain). If a group workflow covers this dataset, verify that workflow — don't create a per-dataset duplicate.

If found, verify three things: 1. Frequency + timing. Parse the cron: line. The implied period must be consistent with updateperioddays and with the release cadence observed this update. Check the timing within the cycle too: the issue should fire shortly after the producer's expected publication window, never before (existing precedents: Gallup 0 8 15 /3 — mid-month, just after the wave publishes; OECD health expenditure 0 0 8 7 * — right after the early-July release). If this update revealed that the cadence or window shifted, propose a new cron with a # comment in the YAML explaining the timing choice (matching the existing style) — cron changes need user sign-off before committing. 2. Naming. The filename should be update-{namespace}-{shortname}.yml. Deviations → flag in the report only; don't rename (churn, and behavior doesn't depend on the filename). Exception: a file missing its .yml/.yaml extension is genuinely broken — GitHub Actions silently ignores it — fix that without asking. 3. Actionable issue body. The body should name the dataset (no version — versions go stale) and tell the next updater to activate the Claude skill: ```yaml title: "Data update: <dataset title>" body: | Update the <dataset title> dataset (<namespace>/<shortname>`).

To run the update with Claude Code, run:

`` /update-dataset <shortname> ` `` For group workflows, keep it a single command listing every member dataset — /update-dataset <shortname1> <short_name2> — or point at the family skill (e.g. /climate-update). If the body lacks the /update-dataset pointer or references a renamed path, refresh it — body/title fixes are auto-applied and reported afterwards, no need to ask. Also check assignees:` still points at the dataset's current owner; flag a mismatch, don't auto-change.

If not found: per the Notion rule, any dataset with updateperioddays roughly in [2, 366] should have a scheduled issue (err on the side of scheduling too much). Propose creating one: copy an existing workflow as template (imjohnbo/[email protected] shape; keep close-previous: false and its WARNING comment), cron shortly after the expected release window, assignees: = the GitHub handle of the human directing this update (team table in CLAUDE.md), filename per the convention, title/body per the template above. If this update touched several related datasets, propose one grouped workflow rather than several. Creating a new workflow needs user sign-off.

Committing. Commit in ~/owid-issues straight to main — the standing exception to the branch-first rule; no branch, no PR — with an emoji+🤖 message (e.g. 🔨🤖 Point <shortname> update issue at /update-dataset), and push. Record the outcome (workflow file, cron, verdict, changes made) in progress.md and set source.scheduledissue_workflow in update-context.yml. Nothing about this lands in the etl PR body beyond the existing tracking-issue link.

1. Indicator upgrade (optional, staging only)

- First upload the new grapher dataset to the staging DB (required before the upgrader can detect it): ``bash STAGING=<branch> .venv/bin/etlr data://grapher/<namespace>/<newversion>/<shortname> --grapher --private ` Then confirm the variables actually landed in MySQL — data://grapher/... --grapher sometimes only builds the feather without upserting (observed: 0 rows in variables afterward). If the count is 0, run the separate grapher:// step, which does the MySQL upsert: `bash # verify STAGING=<branch> .venv/bin/python -c "from etl.config import OWIDEnv; print(OWIDEnv.fromstaging('<branch>').readsql(\"SELECT COUNT(*) n FROM variables WHERE catalogPath LIKE %(p)s\", params={'p':'%<namespace>/<newversion>/<shortname>%'}).n[0])" # if 0, force the upsert: STAGING=<branch> .venv/bin/etlr grapher://grapher/<namespace>/<newversion>/<shortname> --grapher --private ` - Then run the automatic upgrader: `bash STAGING=<branch> .venv/bin/etl indicator-upgrade auto ` - auto matches on the variable name, so editing an indicator title in the same PR defeats it. It reports "No perfect matches found (no variables with identical names)" and saves nothing, even when shortName is unchanged on both sides — a one-character title edit is enough (a straight-to-curly apostrophe fix from /check-metadata-style did it once). That output looks like a matching problem but is a naming one, so don't read it as "nothing to remap": compare the two datasets' name and shortName columns to confirm, then use the explicit mapping below. - auto can detect nothing for a legitimate version bump ("No dataset migrations detected. Nothing to do."). Don't conclude there's nothing to remap — fall back to an explicit mapping: pair old/new variable ids by shortName across the two dataset ids, store with WizardDB.addvariablemapping(...), preview with cliupgradeindicators(dryrun=True), then apply (mechanics under "Indicator Upgrader CLI for one-shot chart remaps" in Guardrails). Map all indicators, not just charted ones — the full mapping is also what gives Anomalist's upgrade detectors complete coverage (see Final QA). The new-version shortNames left unpaired by this mapping are exactly the new-indicator list from step 5 — cross-check the two; a mismatch usually means a missed rename. - **The mapping only covers oldversion → newversion; charts stranded on older versions stay stranded. Previous cycles leave charts behind, and the upgrader cannot see them. After the main upgrade, sweep every version of the dataset, not just the one you bumped from: `sql SELECT d.catalogPath, COUNT(DISTINCT c.id) AS charts, COUNT(DISTINCT CASE WHEN c.publishedAt IS NOT NULL THEN c.id END) AS published FROM charts c JOIN chartdimensions cd ON cd.chartId=c.id JOIN variables v ON cd.variableId=v.id JOIN datasets d ON d.id=v.datasetId WHERE d.catalogPath LIKE '<namespace>/%/<shortname>' GROUP BY 1 ` Don't filter to published charts — drafts count too: the upgrader remaps them as well, a draft left on ghost variables can be published later with stale data, and its variable references block archiving the old version exactly like a published chart's. Scope the predicate to the namespace, and escape literal underscores in every substituted component — the namespace as well as the short name (LIKE 'worldbank\wdi/%/fertility\rate') — short names recur across namespaces (fertilityrate exists under both demography and gapminder), a suffix-only LIKE '%<shortname>' sweeps unrelated datasets into the results, and an unescaped is a single-character wildcard in MySQL LIKE, so it can quietly match a similarly named dataset or namespace too; since the follow-on mapping pairs variables by shortName alone, either slip would redirect another dataset's charts onto your new variables. Anything on an older version is remappable the same way (one WizardDB.addvariablemapping per old dataset id, paired by shortName). This matters beyond tidiness: the flagship chart for the very indicator an update exists to fix can be sitting on a version from months earlier and receive none of the new data. Whatever remains after the sweep is charts whose indicators have no shortName twin in the new version — which means renamed or retired, not automatically retired: a rename in an earlier cycle breaks the twin too, so first check the unpaired new-version shortNames and the producer's release notes for a successor, and map any true rename by hand (same WizardDB.addvariablemapping mechanics, paired semantically instead of by shortName). Only the genuinely retired ones need a replace-or-retire decision and are the reason those old versions can't be archived. (WDI 2026-07: 27 charts recovered across three older versions, including gdp-per-capita-worldbank at ~455 views/day, stranded five months on 2026-02-27; 8 remained on retired indicators.) - Set GRAPHERUSERID alongside STAGING.** cliupgradeindicators(dryrun=False) asserts on it (AssertionError: GRAPHERUSERID is not set!) only after computing the whole mapping and listing the affected charts, so a missing value fails late and looks like a mapping bug. The value is the human's own grapher user id — the repo's main .env has it; SELECT id, fullName FROM users confirms which is theirs (other .env. files may carry a colleague's). - CRITICAL: After the upgrader finishes, always verify it actually worked by querying staging: `bash mysql -h "staging-site-<branch>" -u owid --port 3306 -D owid -e "SELECT COUNT() FROM chartdimensions cd JOIN variables v ON cd.variableId = v.id WHERE v.catalogPath LIKE '%<namespace>/<newversion>%'" ` If the count is 0, the upgrade did not run — re-run it. - The auto-upgrader only remaps grapher charts — NOT ETL-defined explorers or MDims. Explorers (export://explorers/...) and multidims (export://multidim/...) reference indicators by catalog path and are rebuilt by running their export steps, which the indicator-upgrader never touches. If the dataset has any (check the DAG: rg "export://(explorers|multidim)/./<shortname>" dag/ -g ".yml"), they'll still point at the old variables on staging until you re-run them: `bash STAGING=<branch> .venv/bin/etlr export://explorers/<ns>/latest/<short> export://multidim/<ns>/latest/<short> ... --export --private ` Verify none still reference the old version (both queries should return empty): `bash # explorers mysql -h "staging-site-<branch>" -u owid -P 3306 -D owid -e "SELECT DISTINCT ev.explorerSlug FROM explorervariables ev JOIN variables v ON ev.variableId=v.id WHERE v.catalogPath LIKE '%<ns>/<oldversion>%'" # mdims mysql -h "staging-site-<branch>" -u owid -P 3306 -D owid -e "SELECT DISTINCT mdp.slug FROM multidimxchartconfigs mx JOIN variables v ON mx.variableId=v.id JOIN multidimdatapages mdp ON mdp.id=mx.multiDimId WHERE v.catalogPath LIKE '%<ns>/<oldversion>%'" ` - Also verify narrative charts. Narrative-chart configs can pin a variableId in their own patch (not inherited from the parent chart), and that id can date from a version older than the one this update started from — left stale by a previous cycle. The auto-upgrader only carries oldversion → newversion mappings, so it can never remap those, and the chartdimensions count above can't catch them either: narrative-chart variable ids live only inside chartconfigs. The upgrader warns about this case ("was NOT remapped: it pins indicators from a version of the upgraded dataset that the mapping does not cover") — watch its output for that warning. But the upgrader only visits narrative charts whose parent chart was affected by the mapping; a stale narrative chart whose parent no longer uses any mapped indicator is never visited and stays silent. So always run this catch-all scan over all narrative-chart configs: `python # STAGING=<branch> .venv/bin/python — scan narrative chart configs for variables on ANY old version import json from etl.config import OWIDENV oldvars = set(OWIDENV.readsql( "SELECT v.id FROM variables v JOIN datasets d ON d.id = v.datasetId " "WHERE d.catalogPath LIKE %(p)s AND d.catalogPath NOT LIKE %(new)s", params={"p": "%/<shortname>", "new": "%<newversion>%"})["id"]) df = OWIDENV.readsql("SELECT nc.id, nc.name, nc.parentChartId, JSONEXTRACT(cc.config, '$.dimensions') AS dims " "FROM narrativecharts nc JOIN chartconfigs cc ON cc.id = nc.chartConfigId") stale = [(r["id"], r["name"], d["variableId"]) for , r in df.iterrows() if r["dims"] for d in json.loads(r["dims"]) if d.get("variableId") in oldvars] print(stale) # must be empty ` If any are found, remap them with an explicit mapping via the upgrader's own CLI helpers (load by parent chart id; cliupgradeindicators won't reach them because it finds charts via chartdimensions): `python from sqlalchemy.orm import Session import etl.grapher.model as gm from apps.indicatorupgrade.upgrade import pushnewnarrativechartscli from etl.config import OWIDENV with Session(OWIDENV.engine) as session: ncs = gm.NarrativeChart.loadnarrativechartsbyparentchartids(session, [<parentchartid>]) ncs = [nc for nc in ncs if nc.id in {<stalencids>}] errors = pushnewnarrativechartscli(ncs, {<oldvarid>: <newvarid>}) ` - Pick <newvarid> by matching the PARENT chart, not by shortname. A stale narrative pin is often a legacy, pre-dimensional variable — an old PPP year, or a flat shortname like headcount215 — with no shortname twin in the new dataset, so the version-bump mapping (and any shortName join) can't reach it. A narrative chart is just a framing of its parent, so the correct target is whatever indicator the parent chart uses now (after the regular-chart upgrade has run). Read the parent's current variableIds and map each stale pin to the parent's equivalent by indicator role: `python import json pc = OWIDENV.readsql("SELECT cc.config FROM charts c JOIN chartconfigs cc ON c.configId=cc.id WHERE c.id=%(i)s", params={"i": <parentchartid>})["config"].iloc[0] parentvarids = [d["variableId"] for d in json.loads(pc)["dimensions"]] # the targets to map onto ` Different narrative charts (and even one chart's multiple pins) can come from different stale versions, so build the mapping per parent, not with one global dict. In a real run this meant e.g. a $2.15/2017-PPP legacy pin → the parent's current $3/2021-PPP variable. - pushnewnarrativechartscli takes the mapping directly (no WizardDB.addvariablemapping needed) — pass {staleid: parentvarid}. - **The auto upgrade only visits narrative charts when ≥1 regular chart is upgraded in the same run. Re-running etl indicator-upgrade after the regular charts are already remapped is a silent no-op for narratives — call pushnewnarrativechartscli directly instead. - Verify with a fresh API read after the update:** AdminAPI(OWIDENV).getnarrativechart(id)["configFull"] re-reads the just-rematerialized full config (the PUT recomputes and stores it); re-using a rendered-config value fetched before the update (or from a still-open SQL session's snapshot) will mislead you into thinking nothing changed. Note that configFull is itself the stored materialized full — fresh right after a save, but it goes stale again whenever the parent is edited later (see below). - Watch for stale FAUST overrides. These charts often also pin an old subtitle/footnote (e.g. "$2.15 per day" / "2017 prices") that no longer matches the parent. pushnewnarrativechartscli migrates the indicator but only warns about the text — the fix is to reset the flagged field to the parent's exact text to restore inheritance (identical values drop out of the patch). Always ask the user before changing FAUST: it's reader-facing editorial text (Footnote, Axis, Unit, Subtitle, Title), so confirm the reset/rewrite first — never fold a FAUST change silently into the indicator upgrade. (Use findstalefaustoverrides(patch, parentconfig, mapping) to list exactly which fields are stale; set only those to the parent's value and PUT via AdminAPI.updatenarrativechart. Leave numeric display overrides like tolerance alone unless asked — they may be intentional.) Then re-run the scan and confirm it's empty. - The upgrader's stale-FAUST warnings only cover narrative charts visited in that run (those whose parent was in the mapping). After the upgrade, run findstalefaustoverrides across every narrative chart carrying the dataset's variables, and pair it with a plain grep of the patches for old unit/base-year strings — the similarity check misses notes whose wording diverged from the parent, and pinned notes can be several releases stale, not just one (ODA: five narrative notes said "constant 2022 US$" when the parents were already on 2024; a sixth was caught only by the base-year grep). - Editing a parent chart's FAUST does not immediately reach its narrative children. Saving a parent does not cascade: a child's rendered config is rematerialized only when the child is saved, so after a parent edit it keeps serving the old inherited text (stored config and render) even though the field isn't pinned in the child's patch. To force re-derivation, re-save the child's stored patch unchanged (AdminAPI.updatenarrativechart(id, patch)). This is safe because the endpoint treats any payload as a full config and recomputes both stored configs against the current parent — patch = diff(payload, parent), full = merge(parent, patch) (updateNarrativeChart in grapher's adminSiteServer/apiRoutes/narrativeCharts.ts) — and a stored patch always carries $schema + dimensions plus the always-persisted props (entity/time/tab/chart-type selection), so pinned fields stay pinned and everything else re-inherits from the parent. Do not "refresh" by fetching configFull and PUTting it back: in this scenario that fetches the stale materialized full, and diffing it against the new parent pins the old inherited text into the child's patch as an override — permanently freezing exactly the text you meant to refresh. (PUTting a full config is the right pattern only when you mean to change the resolved view — the indicator remaps in apps/indicatorupgrade/upgrade.py and apps/chartsync/cli.py edit the merged config first, then PUT it.) The re-save is required, not optional: nothing rematerializes the child on its own — the site render reads the same stored full — so waiting and re-checking never fixes it. Equally, never conclude from reading the child's stored config or render that the parent edit didn't take; it did, the child just hasn't been re-derived yet. - All three step-7 audits below share one surface sweep. find-chart-references enumerates every surface carrying the dataset's indicators (charts, MDim views, explorer views, narrative charts, article links/embeds, data insights, static viz, key-chart slots) and classifies each reference as embed / render / link. Run it once with --dataset-id <new grapher dataset id> --transitive --json <out> --gaps-json <gaps> and let all three audits read that list, instead of each re-deriving its own joins. The second file is the sweep's own list of what it could not see (also printed as COVERAGE GAP: lines); an empty result on any of those surfaces means unknown, not nothing references it, so every audit's outcome names them. The referencing-prose audit reads the same list — in particular each gdoc row's surfaceid, which is the Google Doc id — so it never needs its own join. It is also the tool to reach for outside these audits, whenever the question is "what would this change touch". - Hardcoded-time-bounds audit (standard — run after all remaps). A chart pinned to maxTime: <old year> keeps opening at the old year after the update ships — the new data is on the indicator but invisible by default, and its edit history shows a human bumping the pin every cycle. Run the check-hardcoded-years skill in dataset mode: it sweeps the same surfaces as the empty-entity audit (charts, map tabs, MDim views, explorer views, narrative charts, article time= embeds/links) for numeric minTime/maxTime/timelineMinTime/timelineMaxTime/map.time pins and grades each against the new indicators' latest time (metadata fetches share the empty-entity audit's cache when both run). It's cheap enough to run unconditionally. A pin below the new max = the update is invisible on that surface — propose setting it to "latest" before merge, with user sign-off (reader-facing default-view change): charts get fixed on staging so the edit rides Chart Diff, MDim/explorer pins get fixed in their ETL YAMLs + export re-run, article pins go to content follow-up. A pin equal to the new max goes stale next cycle — propose the same fix now. Deliberate pins (year in title/subtitle/slug, narrative charts, single-year comparisons) are listed, not fixed — they're coupled to FAUST text. - Referencing-prose audit (standard — run after all remaps). The other two audits check whether a surface still renders correctly. This one checks whether the words around it are still true. Our own metadata prose is covered separately (see the "Grep metadata prose for numbers carried from the old release" guardrail); this is about published content that cites the dataset — article paragraphs, data-insight bodies, and above all titles, which carry the most-read numbers and are the easiest to forget. Take the surface list from findreferences.py --json (the sweep above), never from a hand-rolled postsgdocslinks join. Two of the surfaces that matter most don't appear in that table at all: a data insight holds its chart in front matter (content->>'$."grapher-url"'), and a featured metric lives in featuredmetrics. A join on postsgdocslinks will report "2 references" where the script reports 6, and the one it silently drops is the data insight whose title carries the headline number. The script is the right source, not an exhaustive one: it states the surfaces it structurally cannot see (--gaps-json, or the COVERAGE GAP: lines), and two of them bite this audit in particular — a chart nested inside an article layout container may leave no postsgdocslinks row, and a data insight that records its chart anywhere other than grapher-url is invisible. Carry those gaps into the outcome: "no stale claims" is a verdict on the surfaces swept, never on the site, so record it as "none in the N surfaces swept; not swept: …". When a surface you know cites the dataset is missing from the list (an article the content team named, a post the chart's own page links to), read it by hand and say so. For each gdoc-backed row, fetch the doc by the row's surfaceid (the Google Doc id) and read both postsgdocs.markdown (the body) and postsgdocs.content->>'$.title' — the title is stored in the content JSON, not in markdown, so a body-only read misses the headline. Then re-derive every quantitative claim against the new data. The distinction that decides whether there is anything to do: - Time-bounded claims survive appended periods — but not revisions of the periods they name. "In early 2023 it was around $4 billion; by late 2025 it had grown to $62 billion" is untouched by a new quarter, because it names the quarters it compares. It is not untouched by a revision: if this update changed the value or the date of a named period (a restatement, a corrected label — this update moved four quarter dates), recompute it like any other claim. The exemption is for append-only updates; check the diff for touched periods before granting it. - Unbounded or "latest-implied" claims do. "has grown 1,300-fold", "now accounts for over 90%", "the biggest single segment" all silently re-point at whatever the newest data is. Recompute each one. Check the title separately from the body (it comes from a different field, see above) — a body can be correctly hedged while the title states the bare multiple, and the title is what readers see in feeds and social cards. A static image changes the remedy, not the finding — and the sweep's kind cannot tell you which you have. kind=embed means the surface holds the chart by slug (a data insight's front-matter grapher-url, an announcement's cta button); it says nothing about what the reader sees, and both of those can sit above a static picture. Read the doc body instead — postsgdocs.content->'$.body[*].type': an image block is a static picture, a chart block is a live grapher that picks new data up on its own. Where the picture is static, prose and image must agree — editing one sentence desyncs them and is worse than leaving both consistent-but-dated — so the honest options are "refresh image and text together" (a separate static-viz job) or "leave it, and record why". Say which you are recommending. Featured metrics and key-chart slots carry no prose, so they can't go stale this way; list them only so the surface count is honest. Route findings to content follow-up with the recomputed number attached, and never edit published prose yourself — it is reader-facing copy owned by whoever wrote it. Record the outcome (fixed / deliberately left, with the reason) in the PR's open items, so the next updater doesn't re-flag it — and say whether the update was append-only or revised named periods, since that decides whether bounded claims were in scope. (NVIDIA: a data insight's "1300-fold" title was right at publication and 1,562x after the update, while a sibling post bounded to "late 2025" needed nothing; a hand-rolled join found 4 of the 6 references.) - **Empty-entity audit (always suggest it after all remaps — only the run is optional). You MUST surface this option to the user on every update, without exception** — even when you recommend skipping the run, still tell the user it exists and let them decide; the token cost governs whether to run it, never whether to mention it. An upgrade can leave a view pinned to entities that have no data in the new indicators — it renders as an empty chart with no error anywhere. The check-empty-entities skill audits charts (entity selections + map columnSlug), MDim views, explorer views, narrative charts, and published-gdoc country=` references, and grades every finding against production. It sweeps every surface the dataset touches, so the run can consume a lot of tokens on widely-charted datasets — skip the run by default, run on user opt-in, and actively recommend running it when the risk is real: many charts remapped, hand-curated (non-auto) mappings, a restructure, or indicators whose country coverage shrank. When it runs: a selection that had data on production and lost it on staging is a regression from this update — fix it before merge (remap the view or restore the entities); a gap identical on production is pre-existing — it still needs fixing (chart-config edit or content follow-up on the gdoc), just not necessarily in this PR, so list it in the PR body with a planned fix.

1. Update context for public announcement

- Maintain workbench/<shortname>/update-context.yml as the canonical record of facts discovered during the update. Do not wait until the end if a fact is already known; append/update as each step completes. - At minimum, record: ``yaml dataset: namespace: <namespace> oldversion: <oldversion> newversion: <newversion> shortname: <shortname> title: <public dataset title, if known> producer: <producer, if known> source: releasedate: <snapshot origin datepublished, if known> nextrelease: <best-effort, or null> urlmain: <source page, if known> citationfull: <citation, if known> scheduledissueworkflow: <owid-issues update-*.yml filename, or null (from 6d)> coverage: yearmin: <garden min year> yearmax: <garden max year> countries: <distinct countries/entities> includesregions: <true/false> sparserecentyearnote: <note, or null> charts: publishedcount: <published chart count> sizequalifier: <handful|moderate|large|massive> explorers: <list of published explorer slugs using this data, or []> mdims: <list of MDim slugs using this data, with published flag, or []> selectedviews: - title: <chart title> slug: <chart slug> rationale: <why this represents the dataset> announcement: priorposts: <list of {slug, type, published} for OWID posts already covering this data, or []> lastpublicpost: <date of the most recent announcement or data-insight, or null> eligiblefrom: <lastpublicpost + 6 months, or null if never posted> decision: <drafted | declined (cooldown) | n/a> updatesummary: snapshotdiff: <short summary or artifact path> meadowdiff: <short summary or artifact path> gardendiff: <short summary or artifact path> notablechanges: [] sanitycheckfindings: [] resolvedworkarounds: [] editorialcontext: whyitmatterssnippets: [] caveatsnippets: [] interestingupdatesnippets: [] ` - Query the staging DB for published charts using the new dataset (filter on c.publishedAt IS NOT NULL). Draft/unlisted charts must not be counted in the announcement: `sql SELECT c.id, cc.slug, cc.config->>'$.title' as title, cc.config->>'$.type' as type, cc.config->>'$.hasMapTab' as hasMapTab FROM charts c JOIN chartconfigs cc ON cc.id = c.configId JOIN chartdimensions cd ON cd.chartId = c.id JOIN variables v ON cd.variableId = v.id WHERE v.catalogPath LIKE '%<namespace>/<newversion>%' AND c.publishedAt IS NOT NULL GROUP BY c.id ` - Charts are not the only surface — also count the explorers and MDims that use this data. Many datasets feed published OWID explorers and multi-dimensional collections, which the grapher-charts query above misses entirely. Run the export steps first (step 7) so these point at the new variables, then query both: ``sql -- Explorers (note isPublished — only published ones count for the announcement) SELECT DISTINCT ev.explorerSlug, e.isPublished FROM explorervariables ev JOIN variables v ON ev.variableId = v.id JOIN explorers e ON e.slug = ev.explorerSlug WHERE v.catalogPath LIKE '%<namespace>/<new_version>%';

-- MDims (note published flag — drafts are published=0) SELECT DISTINCT mdp.slug, mdp.published FROM multidimxchartconfigs mx JOIN variables v ON mx.variableId = v.id JOIN multidimdatapages mdp ON mdp.id = mx.multiDimId WHERE v.catalogPath LIKE '%<namespace>/<newversion>%'; `` Chart-based explorers can also attach via explorercharts.chartId (join through chartdimensions) rather than explorervariables — check that table too if the variable-based query comes up empty but the DAG shows an explorer step. Record published explorers/MDims under charts.explorers / charts.mdims in update-context.yml, and fold them into the "How many charts did this update affect?" answer (e.g. "10 published charts, 3 explorers, plus 3 draft MDims"). Only count published surfaces (isPublished=1 / published=1) toward the public announcement; note unpublished ones for QA. - Map the published chart count to sizequalifier: 1–9 = handful, 10–49 = moderate, 50–199 = large, 200+ = massive. - Pick 1–3 selected_views` using these criteria (in order of preference): - Map views — immediately visual, readers can find their own country - Charts with punchy, standalone headlines — titles that make a clear claim work best for social sharing - Global trend charts (StackedArea / World) — show the big picture over time - Skip: population-weighted variants (harder to read quickly), within-regime breakdowns (too niche), country-specific views - Add snippets for the editorial prompts from source metadata, garden/grapher metadata, resolved sanity-check/workaround notes, and non-routine PR changes. Keep these as snippets/facts, not polished Slack prose.

1. Slack announcement

- Run the data-updates-comms skill with workbench/<shortname>/update-context.yml as input. data-updates-comms is the canonical owner of the Slack form wording, copy-paste format, editorial framing, search URL, and any standalone fallback gathering. Do not duplicate that rendering logic here. - Save the rendered draft to workbench/<shortname>/slack-announcement.md. - If data-updates-comms reports missing mechanical fields, gather them, update update-context.yml, and re-render rather than inventing values. Ask the user if a missing field requires judgment. - Do not put the announcement in the PR at all — no embed and no pointer. The draft stays as the workbench/<shortname>/slack-announcement.md file (the user copies from there); comms drafts are internal and are kept out of the public data-update PR. - Post @codex review as a separate PR comment (not in the PR description) to trigger an automated code review. Use: ``bash gh pr comment <prnumber> --body "@codex review" ` - At the end of the update, tell the user, with a markdown link to the saved file so they can click through to open it: "Slack announcement drafted at [workbench/<shortname>/slack-announcement.md](workbench/<shortname>/slack-announcement.md). Please review and post it to #data-updates-comms." Always render the path as a markdown link […](…)`, not as inline-code — the chat UI renders it as clickable that way. (Slack can't be auto-posted — the user posts it.)

9b) Data update post (for OWID /latest) - Run the /data-update-announcement skill in Mode A, with workbench/<shortname>/update-context.yml and workbench/<shortname>/slack-announcement.md as input. That skill is the canonical owner of the CMS format, the house style, the CTA link rules, the Google Doc creation and styling, and the handoff wording. Do not duplicate any of it here. - This is separate from the Slack announcement (step 9) — that one is a 10-field form for the internal channel; this one is a mini-blog-post for OWID readers, published on https://ourworldindata.org/latest. - The skill drafts two versions for the user to choose between, saves the chosen one to workbench/<shortname>/data-update.md, and only then creates the Google Doc. Don't create the Doc yourself, and don't shortcut the sign-off — the Drive API can't edit or delete a Doc once created. - If the skill reports a missing fact, gather it (snapshot .dvc, garden .meta.yml, urlmain), persist it back to update-context.yml, and re-run rather than inventing a value. - "No post" is a valid outcome, not a blocked task. The skill checks what OWID has already published about this data and stops when the last announcement or data insight is less than six months old — a routine refresh of a frequently-updated dataset often lands inside that window. When it declines, record the prior post(s) and the eligible date under announcement: in update-context.yml and in the PR's open items, and move on; don't override it to get a draft. The Slack post (step 9) is unaffected — that one runs every update. - Do not put the post in the PR at all — no embed and no pointer. Like the Slack draft, it stays in workbench/.

1. Codex review: address comments and resolve threads

- Codex's delivery channel depends on the verdict — poll both. A clean pass arrives as an issue comment ("Didn't find any major issues") from chatgpt-codex-connector[bot], with zero inline comments and no formal review object. A review with findings arrives as a formal review ("💡 Codex Review") with inline comments, and no issue comment. A watcher polling only one channel waits forever on the other outcome — treat a hit on either as completion. - Wait ~60 seconds after posting @codex review, then poll both channels: ``bash gh api repos/owid/etl/issues/<prnumber>/comments | python3 -m json.tool # clean-pass summary lands here gh api repos/owid/etl/pulls/<prnumber>/comments | python3 -m json.tool # findings land here as inline comments ` - Codex posts in one of two places — always check both. When it finds issues, it leaves inline review comments (the endpoint above) with resolvable threads. When it finds nothing, it posts a single top-level PR (issue) comment instead — no inline comments, no threads — e.g. "Codex Review: Didn't find any major issues. Keep it up!". So if the inline-comments endpoint is empty, check the issue comments before concluding Codex hasn't run yet. A third shape exists: a findings review whose finding lives only in the review body (no inline comments, no resolvable threads) — list gh api repos/owid/etl/pulls/<n>/reviews and read each new review's body; polling only the two comment endpoints misses it (there is no thread to resolve — reply via a normal PR comment instead): `bash # clean-pass summaries land in the issue comments: gh api repos/owid/etl/issues/<prnumber>/comments \ --jq '.[] | select(.user.login | test("codex";"i")) | .body' # review-body-only findings (the third shape) — no thread, no issue comment: gh api repos/owid/etl/pulls/<prnumber>/reviews \ --jq '.[] | select(.user.login | test("codex";"i")) | .body' ` A "no issues" / 👍 comment from chatgpt-codex-connector[bot] means the review is done and there's nothing to address — don't keep polling for inline comments that will never come. - Fetch open review thread IDs via GraphQL: `bash gh api graphql -f query='{ repository(owner:"owid", name:"etl") { pullRequest(number:<prnumber>) { reviewThreads(first:20) { nodes { id isResolved comments(first:1) { nodes { body } } } } } } }' ` - For each unresolved Codex comment: - If valid: apply the fix, commit, push, then resolve the thread: `bash gh api graphql -f query='mutation { resolveReviewThread(input:{threadId:"<threadid>"}) { thread { id isResolved } } }' ` - If not valid / not applicable: reply explaining why, then resolve the thread: `bash gh api repos/owid/etl/pulls/<prnumber>/comments/<commentid>/replies -f body="<explanation>" gh api graphql -f query='mutation { resolveReviewThread(input:{threadId:"<thread_id>"}) { thread { id isResolved } } }' `` - If neither the inline-comments endpoint nor the issue-comments endpoint shows a Codex post after 60 s, wait another 60 s and retry (up to ~5 min total). Codex can take 5–10 min — a clean review often arrives only as the top-level "no issues" comment.

Committing and pushing

Commit and push incrementally as you go — after each step that produces code changes. Don't wait until the end. Use descriptive commit messages with appropriate emojis (the one auto-prepended by etl pr for the chosen category + 🤖 for AI-written code).

Verify the branch immediately before every commit (git branch --show-current). The session shares its checkout with the user's IDE — a branch switch there silently moves your shell too, and a commit then lands on whatever branch the IDE left behind. The failure is quiet: git push -u origin <branch> pushes the (empty) local PR branch, reports "Everything up-to-date", and the PR stays empty. Recover by cherry-picking the stray commit onto the right branch (don't force-move the other branch's pointer).

Keep the PR-body draft under workbench/<short_name>/ (or re-fetch it with gh pr view <num> --json body --jq .body before each edit) — not in the session scratchpad. The body gets re-edited throughout the update, and scratchpad files don't survive session resumes.

At the end of the workflow, update the PR description with:

• A tracking-issue link as the first line of the Summary — e.g. Tracks: owid/owid-issues#NNNN. Most data updates have a corresponding owid-issues ticket; try to find it by searching the title or <short_name> first, and ask the user for the issue number if you can't locate one rather than skipping the link silently.
• A summary of key changes at the top
• Collapsed <details> sections only for the pipeline steps that changed in a non-obvious way. Skip any step that's just the boilerplate generated by etl update — don't add a placeholder like "unchanged from boilerplate". The Summary already explains the why; per-step sections are only for the how, when the how isn't obvious from the diff.

A long-lived branch silently regresses shared charts

A branch that sits open while another dataset update merges to master will serve that dataset's old version on its staging server, because staging builds from the branch. Any chart combining both datasets then differs from production on two axes: yours (intended) and theirs (a regression). Approving such a diff syncs the branch's stale config back to production and reverts the other team's upgrade on a published chart.

It doesn't announce itself — CI is green, the chart renders, and the diff looks like the change you expected. So whenever a dataset update lands on master while your PR is open, or a chart diff shows a change you can't attribute to your own work, compare every dimension of the affected charts against production, not just the one you touched:

# for each chart id: list (shortName, dataset version) per dimension on staging and on prod

The fix is to merge master in, rebuild, and remap only the affected charts' foreign dimensions. Two scoping notes: chart-diff only covers charts your branch's datasets touch, so charts using only the other dataset are stale on staging but never sync — leave them, they're not yours to fix and repointing them adds review load. And remap by editing those chart configs directly rather than via a variable-level mapping, which would sweep in other charts sharing the same variables and pull them into the review queue. (WDI 2026-07: an ODA update merged mid-PR; two foreign-aid-*-vs-gdp-per-capita charts would have reverted ODA to a seven-month-old version.)

Downstream dependency check

After completing the update, check if any other datasets depend on the old version of the updated dataset:

rg "<namespace>/<old_version>/<short_name>" dag/ -g "*.yml" | grep -v "^dag/archive"

Filter out the old dataset's own DAG entries (snapshot → meadow → garden → grapher chain). Any remaining references are downstream dependents that still point to the old version.

If downstream dependents exist, decide with the user whether to bump them in this PR or defer to a follow-up:

• Tell the user which datasets depend on the old version.
• Follow-up PR (default for a big fan-out): add a "Downstream dependencies" section to the PR description (not collapsed) listing the dependents, to be repointed in a separate PR. This mirrors the historical two-PR pattern for foundational datasets (e.g. income_groups: chain-update PR, then a "🐝 Update all datasets to latest …" bulk-bump PR).
• Version-bumping a derived step inside the follow-up PR is a mini dataset update — run it as one. When repointing makes a derived/OMM step's version predate its data (CLAUDE.md's versioning-hygiene rule) and the owner opts to bump it, use .venv/bin/etl update data://garden/<ns>/<oldversion>/<shortname> --include-usages --direct-only — it works fine for snapshot-less derived steps — rather than git mv + a hand-edited DAG, and treat the bump's charts like any update's: new variable IDs mean an indicator upgrade on staging (auto detects nothing for a legit version bump — use the explicit shortName-paired mapping), then the step-7 audits (check-hardcoded-years, check-empty-entities) scoped to those charts. A hand-rolled git mv can reach the same end state, but it silently skips the audits and leaves no workbench trail. (WDI 2026-07 follow-up: gdp_historical was bumped by hand; the audits, run after the fact, found nothing — by luck, not by construction.)
• Bump in this PR (if the user wants it self-contained): repoint every downstream ref and remove/archive the old chain in the same PR. Mechanics that bit this session:

- Bulk-replace with a negative-lookahead so a prefix match doesn't corrupt sibling shortnames — e.g. re.sub(r"garden/wb/<oldv>/incomegroups(?!)", "garden/wb/<newv>/incomegroups", text) leaves incomegroupsaggregations alone. - **Remove the old own-chain block from dag/main.yml before the bulk sweep, or the sweep turns the old definition into a duplicate of the new key. Relocate the new block into the old slot (nested form) as part of the same edit. - Downstream datasets keep their own version and variable IDs** — only their dependency on the updated dataset changes — so no chart remapping is needed for them; their aggregates just recompute against the new data (visible in Chart Diff). The indicator upgrade (step 7) still only concerns charts that use the updated dataset's own variables. - This is the only case where "Removing the old version" happens in the same PR — otherwise the old chain must stay until the follow-up repoints its consumers.

Silent-breakage check (downstream builds + value diff)

A foundational-dataset update can leave a downstream step building cleanly while quietly dropping data — a region whose aggregate can no longer be computed goes NaN, a reclassified country disappears, a join stops matching. Nothing raises; the feather is written; the gap only surfaces on a chart weeks later. Two existing commands cover it — no bespoke tool:

1. Do all downstream consumers still build? Staging answers this on every push — run the command locally only when you want the answer before pushing:

.venv/bin/etlr --modified --continue-on-failure --private              # add --dry-run to list scope first

--modified detects the steps changed vs origin/master and expands to their full transitive downstream via the branch DAG (same machinery as chart-diff), runs them in dependency order, skips dependents of failed steps, and ends with a failure summary + non-zero exit. Staging runs the same check on every push: its bake (ops/templates/owid-site-staging/etl-build.sh) is etl run garden grapher explorers --modified --grapher --private --continue-on-failure with PREFER_DOWNLOAD=1, and --continue-on-failure re-raises the first failure at the end — so any consumer crash turns the buildkite/etl-automated-staging-environment PR check red. That makes the local run optional fast feedback, not the primary net. The caveat that matters: while that check is red, the data-diff report under-reports — dependents of the failed step are skipped, stay stale in the catalog, and diff as unchanged. Always confirm the check is green before trusting the report. Use --workers N to parallelize a big local fan-out.

Size the fan-out first (--dry-run lists the scope). Small (≲50 steps): the local run gives you the crash check in minutes, before burning a staging cycle. Foundational-dataset scale (hundreds of steps — an income-groups bump is ~560): skip the local build and rely on the staging check — locally it costs ~35 min and ~7 GB of data/ to duplicate what the bake does anyway (if you do run it, delete the builds afterwards; data/ is regenerable cache). Skip the local etl diff at that scale too — another ~25 min and a JSON in the hundreds of MB; owidbot's hosted report on the PR is the same comparison for free.

2. How much did their outputs change? The data-diff report answers this directly — it ranks everything by anomaly score (BARD, the metric Anomalist uses) and makes data loss unmissable, so you read its verdicts instead of scanning the diff yourself:

• On the PR (zero effort): open owidbot's data-diff HTML report (https://catalog.ourworldindata.org/diffs/<sanitizedbranch>/data-diff.html, easiest via the full report link in owidbot's PR comment — the path keeps the branch name's dots and underscores and replaces only characters outside [A-Za-z0-9.-] (e.g. /) with - — unlike the staging subdomain, which does replace ./_) — it compares the staging build (new dependency) against production (old dependency). For a dependency bump the consumers' code is unchanged, so this is a clean old-dep-vs-new-dep comparison.
• Locally, after step 1: .venv/bin/etl diff REMOTE data/ --changed --include garden --output-html data-diff.html.

How to read it, in order:

1. "Top changes — what to watch": red "− lost N data point(s): labels…" entries lead both the Datasets and Indicators lists — each one is a coverage loss (the silent-breakage signature) and must be triaged: legitimate churn, or a silent drop? Below the losses come the biggest value changes with their median anomaly scores.
2. Tier strip + coverage chips: 🔴 datasets (score ≥ 15% or any coverage loss) need review; 🟡 a skim; 🟢 is rounding-level noise. A red − N row(s) removed: … chip on a dataset row always means data points disappeared, regardless of how small the share.
3. Filters: the tier dropdowns isolate 🔴 datasets/indicators; 📝 metadata-only separates pure metadata edits from value changes.

Full-report audit — deciding whether code changes are needed. For a dependency bump, "churn or bug?" is answerable mechanically, because only the dependency changed and consumer code didn't. Load the report JSON (DiffReport.from_json) and run these probes over all changed datasets, not just the Top-changes lists — each should come back empty or confined to dep-derived entities; anything else is a code-change candidate:

1. Structural: new/removed tables or columns anywhere → a dep bump must never do this.
2. "World" rows changed → some step computes World by summing the dep's groups (anti-pattern: World should come from the source or the full country set).
3. Raw-country rows changed → consumer code entangled with the dep beyond aggregation. (In bilateral datasets, verify every changed pair has an aggregate side; the country names that show up in samples are often just other dimension values — disaster types, element codes, age groups — so check dim columns, not string looks.)
4. Any indicator with >30% of rows changed → more than the aggregate rows moved.
5. Wipe vs edge, for every coverage loss: does the lossy entity still hold rows in the new build? Losing a few % at sparse edges (an aggregate-year-item combo emptying because members moved out, or dropping below a coverage threshold) is churn; an entity losing all its rows or going all-NaN is the bug signature — see the must-have trap below.

The samples cap at ~100 rows/diff, so pair the probes with the mechanism argument (only the dep changed) rather than treating them as a row-by-row proof.

The must-have trap (real case: population + unaids, FY2027 income groups). Some steps pin specific member countries as required for a group's aggregate. When the producer reclassifies a pinned country out of its group, the entire aggregate nulls for all years — a total-entity wipe, not edge churn. Two nets catch it: the geo must-have guard raises ValueError at build time (so the build check above fails loudly), and probe 5 is the backstop. The fix is updating the pinned list to the new membership — so whenever bumping income groups, check the repointed consumers' pinned member lists up front. Beware the rationalization risk: a wiped income-group aggregate looks like the expected churn the rest of the report is full of, and on a foundational dataset (population) it also announces itself as a dataset-count explosion — hundreds of consumers suddenly changed. Run probe 5 on every loss, however plausible the churn story.

Don't diff against stale local builds — a consumer built at an unknown earlier time (or downloaded from the catalog) conflates code drift with the dependency change and produces false positives; REMOTE/production is the trustworthy baseline because CI built it from master.

Complementary, not a replacement: data-diff sees every dataset (including ones with no charts), while [Chart Diff](#final-qa-hand-off--anomalist-chart-diff-and-data-diff) shows how the same changes land on actual published charts and Anomalist flags per-country anomalies in the new data — the final QA hand-off (last checklist step) covers all three. Use data-diff to find which datasets/indicators to worry about, then Chart Diff to judge what readers would actually see.

• When you deferred consumers to a follow-up PR: the checks above belong to that follow-up PR; here just confirm the "Downstream dependencies" list is complete (.venv/bin/etlr --modified --dry-run shows the affected set).

Removing the old version & reordering the DAG

After the ETL update, etl update appends the new version entries to the bottom of the main DAG file while the old version's entries stay in their original slot. Always ask the user whether to remove the old version — but never skip this checklist item, and when the user agrees, always do the reorder too.

Timing: dag entries now, files only after the PR review is done. Reviewers compare consecutive versions with the compare-previous-version VS Code extension, which is purely filesystem-based: it diffs the open step file against the same-named file in the nearest lower YYYY-MM-DD sibling folder, and never reads the dag. So split the removal in two:

• Dag entries: remove and archive right away (the usual edit dag → commit → etl archive-dag → commit sequence). etl archive-dag reconstructs from the committed dag history and doesn't care whether the step files exist, and the version tracker only warns about archived steps missing code — archived steps that still have code are fine (that's why the periodic ":bomb: Delete archived step/snapshot code" cleanup commits exist).
• Old step files (.py, .meta.yml, .dvc): keep them until the reviewer signs off, then delete them as the final commit before merge. Don't modify them in the meantime — the extension diffs them verbatim, and any edit pollutes the consecutive-version comparison.

The Final QA hand-off (Anomalist / Chart Diff / data-diff) is unaffected by either half (Anomalist's upgrade detectors need the old grapher dataset in the local catalog, not the step files or dag entries).

Workflow when the user agrees:

1. Delete the old version's dag entries. Remove its entries (snapshot → meadow → garden → grapher) from the main DAG file (e.g., dag/poverty_inequality.yml). Its files (etl/steps/..., snapshots/...) stay on disk until review sign-off (see "Timing" above); delete them in the final commit before merge. The archive dag (dag/archive/*.yml) is not edited by hand — etl archive-dag reconstructs it from git history, recording each removed step with the commit where it was last active (for recovery via git checkout).

- Commit the removal BEFORE running etl archive-dag. The tool reconstructs from committed git history — an uncommitted working-tree removal is invisible to it, and it will instead pick up whatever earlier removals are already committed. Sequence: edit dag → commit → etl archive-dag → scope → commit the archive. (The old step files stay on disk until review sign-off — see "Timing" above; deleting them is a separate final commit.) - Branch runs are squash-safe (since #6412). When run on a feature branch, the recovery marker points at the merge-base with origin/master (a master commit that survives the squash-merge), branch-only transient steps (created and reverted within the PR) are skipped automatically (archivedag.skipbranchonlystep in the log), and re-runs don't churn markers that are already valid — only markers whose SHA is unreachable from master get replaced. No post-merge fixup pass is needed; committing more to the PR after archiving is fine. - **etl archive-dag reconciles the entire archive, not just your dataset. If the archive was stale, one run can append hundreds of unrelated lines (e.g. this session pulled in ~180 lines across climate.yml/education.yml/etc. plus marker comments) — noise that doesn't belong in a data-update PR and that Codex will question. Keep the commit scoped: after running it, git checkout -- dag/archive/ to drop the unrelated files, then re-add only** your dataset's block to dag/archive/main.yml (copy the exact entry archive-dag generated, including its # archived; last active in <sha> on <date> marker). The block you keep is genuine tool output, so it stays consistent with future full regenerations.

1. Move the new entries into the old slot so the dataset stays grouped with its neighbours and section comment. The new entries should not remain at the bottom of the main DAG.
2. Preserve the original section comment (same indentation as the old block) above the new entries.
3. Prefer the nested (compact) DAG format. etl update emits the flat form (each step a separate top-level key with a flat dep list); the loader (etl/daghelpers.py:parsedagyaml) also accepts the nested form, where the chain is declared inline and flattens to the same graph. The nested form is the team's preferred style and is usually what the archived old block already used:

``yaml data://grapher/<ns>/<v>/<short>: - data://garden/<ns>/<v>/<short>: - data://garden/regions/2023-01-01/regions - data://meadow/<ns>/<v>/<short>: - snapshot://<ns>/<v>/<short>.csv ` Convert the relocated new entries to nested while reordering, so the active and archived blocks match. Verify it parses with .venv/bin/python -c "from etl.daghelpers import loaddag; load_dag()"` (a malformed nesting raises).

1. Verify: rg "<namespace>/<oldversion>/<shortname>" dag/ -g ".yml" | grep -v "^dag/archive" returns nothing, and rg "<namespace>/<newversion>/<shortname>" dag/ -g ".yml" shows the entries only in the main file (under the section comment), not at the bottom.
2. Run make check and commit with 🔨🤖 Remove old <name> entries and reorder DAG.

Expect a Codex false-positive on the archive edit. Because this step touches dag/archive/.yml, Codex often flags it ("avoid updating archived DAG entries" — the AGENTS.md rule against editing archived files). This is expected: archiving is* the explicitly-requested workflow step, and the rule's own "unless explicitly asked" exception applies. Reply citing that and resolve the thread — don't revert the archive. A second recurring flag on this step: Codex warns that removing the old chain will "leave the published chart on archived variables" / "remap charts before versioning the grapher step". If the indicator upgrade already ran on staging (step 7) and the old-variable scan came back empty, reply with that verification (the remapped configs sync to production on merge) and resolve.

Final QA hand-off — Anomalist, Chart Diff and data-diff

This is the last step of the pre-review work — the old step files are still on disk at this point (deleting them is the final commit after review sign-off; see "Removing the old version & reordering the DAG"). Don't auto-run these — they're human-judgment tools. Hand off the three links so the user can review and click through:

• Anomalist — flags variables whose new values diverge from the old version beyond statistical thresholds. Catches accidental scale changes, base-year rebases that propagated the wrong way, and silent drops.

`` http://staging-site-<container_branch>/etl/wizard/anomalist ``

Check the upgrade detectors' coverage before handing off. Anomalist's upgrademissing / upgradechange detectors only compare old→new variable pairs from the wizard's variable-mapping table — and the indicator upgrader persists mappings only for charted indicators. If only some of the dataset's indicators are used in charts (the common case), the upgrade detectors silently skip the rest, and a partial mapping suppresses the shortName-inference fallback that would otherwise cover everything. Verify with WizardDB.getvariablemappingraw(): if it has fewer pairs than the dataset has indicators, rebuild the full mapping by shortName (old vs. new variables rows by datasetId) and re-run: ``bash STAGING=<branch> .venv/bin/etl anomalist --anomaly-types upgrademissing --anomaly-types upgradechange \ --dataset-ids <newdataset_id> --variable-mapping '<full json mapping>' --force ` Then spot-check the stored anomalies.dfReduced` rows include indicators beyond the charted ones.

The upgrade detectors also need the old grapher dataset in the local catalog (data/grapher/<ns>/<oldversion>/<short>) — the FileNotFoundError names the new dataset id, but the missing files are usually the old version's. If the old chain has already been removed from the DAG (so etlr can't rebuild it), fetch its files straight from the public catalog: ``bash mkdir -p data/grapher/<ns>/<oldv>/<short> && cd data/grapher/<ns>/<oldv>/<short> && \ for f in index.json <short>.feather <short>.meta.json; do \ curl -sL -O "https://catalog.ourworldindata.org/grapher/<ns>/<oldv>/<short>/$f"; done ``

• Chart Diff — shows side-by-side before/after thumbnails for every chart that uses an upgraded indicator. Catches visual regressions the schema-level checks miss (axis ranges, color steps, legend changes).

`` http://staging-site-<container_branch>/etl/wizard/chart-diff ``

• data-diff report — dataset/indicator-level value comparison of the staging build against production, ranked by anomaly score, with data-point losses leading its Top-changes list and coverage loss forcing the 🔴 tier (see "Silent-breakage check" for how to read it). Covers every dataset the update touched, including ones with no charts — the perspective Anomalist and Chart Diff don't have. Easiest access: the full report link in owidbot's data-diff PR comment. The direct URL keeps the branch name's dots and underscores and replaces only characters outside [A-Za-z0-9.-] (e.g. /) with - — unlike the staging subdomain, which does replace ./ (and it is not the truncated container name either):

`` https://catalog.ourworldindata.org/diffs/<sanitized_branch>/data-diff.html ``

etl approve needs both sides pointed at the right databases: STAGING for the branch, ENVFILEPROD for production — and the tunnel the latter expects is usually down. The command reads pending diffs from and writes approvals to the global environment, which only targets the branch staging DB when STAGING is set — run it without STAGING=<branch> (or STAGING=1 from the branch checkout) and it quietly operates on your local dev DB instead, finding no pending diffs or approving in the wrong place. The production side comes from ENVFILEPROD (ENVFILEPROD=.env.live STAGING=<branch> .venv/bin/etl approve …) — without it the command asserts immediately. .env.live typically points at 127.0.0.1:3310, an SSH tunnel that is often not running; the reliable path is Tailscale, where the production grapher DB is reachable directly as host prod-db on port 3306 with the same livegrapher credentials (the pattern add-ivs-indicators/scripts/indicatoradmintable.py already uses). Being on Tailscale redirects nothing by itself — OWIDEnv.fromenvfile() reads DBHOST/DBPORT straight from whatever file ENVFILEPROD names — so make the redirect real before running the commands below: either set DBHOST=prod-db / DBPORT=3306 in .env.live once (worth making permanent), or derive a one-off copy (gitignored by the .env.* pattern) and pass ENVFILE_PROD=.env.prod-db instead:

sed -e 's/^DB_HOST=.*/DB_HOST=prod-db/' -e 's/^DB_PORT=.*/DB_PORT=3306/' .env.live > .env.prod-db

Note the approval writes to the staging chart-diff table and only reads production, so the prod connection is read-only.

Bulk-approve the easy chart diffs with etl approve before handing the rest to the human. On a dataset with many charts (e.g. WDI has 400+), most pending diffs exist only because the update changed no values a human needs to eyeball — either the underlying data is byte-identical (a version bump minted new variable IDs but the values didn't change) or it changed by a negligible source-revision amount. Reviewing those by hand in Chart Diff is wasted effort; let etl approve clear them first:

ENV_FILE_PROD=.env.live STAGING=<branch> .venv/bin/etl approve --dry-run                       # exact data match only — safe default, see counts first
ENV_FILE_PROD=.env.live STAGING=<branch> .venv/bin/etl approve --dry-run --allow-small-changes  # also count tiny source revisions (see below)
ENV_FILE_PROD=.env.live STAGING=<branch> .venv/bin/etl approve --allow-small-changes            # apply for real once the dry-run counts look right

• Plain etl approve only approves a chart when every dimension's underlying data is byte-identical between staging and prod (it hashes each dimension's actual data, not the raw variable ID — so a version bump that changed no values still gets approved).
• --allow-small-changes additionally approves charts where the only remaining difference is a handful of small-magnitude value changes (typical source revisions) — tune with --tolerance-pct (default 1% relative change per point), --tolerance-abs-floor (default 1e-6, guards near-zero values), --max-changed-points (default 5 per dimension, above which it's sent to manual review regardless of magnitude), and --max-new-points (default 1000 per dimension — new-coverage points, e.g. a fresh year, are given a generous allowance since they're expected from a routine update, but an unexpectedly large coverage jump still gets sent to manual review). It still requires every other part of the chart's config (title, subtitle, everything but the dimension values) to be identical.
• --show-data-diff prints the actual before/after values for skipped charts (per dimension: y/x/size/color) instead of just a hash mismatch — useful to see why a specific chart didn't qualify, or to sanity-check whether raising --tolerance-pct would be safe. Combine with --chart-id <id> to inspect one chart.
• Whatever's left after etl approve is what's actually worth a human's attention in Chart Diff — genuine content changes, added/removed country-year coverage, or other config differences.

Important: derive <containerbranch> correctly. The staging hostname is not simply staging-site-<branch>. The container name is produced by getcontainer_name(branch) in etl/config.py:

1. Replace /, ., _ with - in the branch name.
2. Strip a leading staging-site- if present.
3. Truncate to the first 28 characters (Cloudflare DNS limit).
4. Strip any trailing -.

Branches over 28 chars therefore get clipped. Example: data-military-expenditure-2026 (30 chars) → container data-military-expenditure-20 → hostname staging-site-data-military-expenditure-20. The simplest way to get the correct value is to call the helper:

.venv/bin/python -c "from etl.config import get_container_name; print(get_container_name('<branch>'))"

Include owidbot's data-diff summary in the hand-off so the user knows the scale before clicking: pull the <summary><b>data-diff</b>: …</summary> line from owidbot's PR comment (gh pr view <num> --json comments) — e.g. ❌ 21 changed · 2 new · 4 identical · 16 skipped. If it reports removed datasets or errors, call those out explicitly.

Tell the user something like: "Final QA: please review Anomalist and Chart Diff in the Wizard, and the data-diff report for the dataset-level view — owidbot's summary: ❌ 21 changed · 2 new · 4 identical · 16 skipped. If anything looks off, let me know and I'll investigate."

These pages need a fresh staging build, so they're only meaningful after the PR's grapher upload to staging has completed and the staging server has rebuilt.

Close every hand-off with an explicit list of what's still open — in the PR body, not just chat (chat scrolls away; the PR is what the reviewer and future-you actually read). This is one of the two cases where the list is worth writing out formally rather than as a sentence; see .claude/docs/open-items.md for what tends to get dropped. An update generates far more loose ends than it closes; this workflow's usual danglers: content/gdoc edits and producer error reports (handed off), reader-facing config changes held for sign-off (awaiting a decision), audits offered but not run and anything the staging build didn't cover (nobody checked it) — plus a fourth category, deferred to a follow-up PR, where the two-PR pattern is in play (downstream consumers to repoint, old-version archiving, charts on retired indicators): that list is the follow-up PR's scope, so losing it means redoing the analysis.

Before closing out, confirm both optional heavier audits were suggested. Two checks are opt-in to run but mandatory to offer — the adversarial data & metadata review (/adversarial-data-review, step 6c-bis) and the empty-entity audit (check-empty-entities, step 7). It's easy to skip past them in a long session, which is exactly the failure this guard exists to catch. If you reach this hand-off and haven't yet surfaced either one to the user, do it now: name the skill, say briefly what it would catch and why you did or didn't recommend running it, and let the user decide. Never let the update finish having silently omitted the offer.

Guardrails and tips

• ENDYEAR / "as of" framing for status/event datasets. When a dataset records events (and derives a status time series) and its latest event year lags the release date, you face a choice: forward-fill the latest status to the release year, or stop the series at the last event year and note the "as of" date in metadata. Prefer the latter — forward-filling invents data points for years with no source information (and shifts an ENDYEAR-style constant ripples through the whole series). Keep the series at the last real year and add the currency note to descriptionprocessing and a descriptionkey bullet (e.g. "The legal status shown for each country reflects the situation as of <Month Year>."). Confirm the choice with the user; they may change their mind (in this update we forward-filled to the release year, then reverted to the last event year + an "as of" note).
• Converting a period label to a date: use the dates the producer states, don't re-derive their calendar. Non-calendar periods (fiscal quarters, ISO weeks, crop or school years, survey waves) come with real start/end dates in the producer's own releases. A rule inferred from a few examples — "the last Sunday of the month", "the first Monday" — will fit most periods and quietly miss the rest, and this error survives every schema, bound and sum check, because the values are all correct and only the dates they sit on move. So pin the derivation: keep a handful of period-end dates quoted from the producer and assert the code reproduces them, the way a codebook's worked examples serve as test vectors. (NVIDIA's 52/53-week fiscal year: a last-Sunday-of-the-month rule dated 4 of 50 quarters a week early.)
• OECD SDMX dataflow versions bump on new releases — a pinned URL goes 404/NoRecordsFound. The Data Explorer's "Developer API" links pin df[vs]/the REST path to a dataflow version (e.g. DSDSHA@DFSHA,1.0); when the producer publishes a new edition they may mint 1.1 and empty the old version, so last cycle's known-good URL returns NoRecordsFound. On that error, list versions with GET /public/rest/dataflow/<agency>/<id>/all and retry with the newest. For reader-facing links (urlmain, /latest posts) prefer the version-less explorer deep link (data-explorer.oecd.org/vis?df[ds]=DisseminateFinalDMZ&df[id]=<id>&df[ag]=<agency>), which always resolves to the latest release; in the snapshot's urldownload, pinning the version is fine (deterministic) — just expect to bump it each cycle.
• Re-test "manual upload" snapshots — the blocking may be inverse-UA. When a snapshot's docstring says the file is uploaded manually because "the website blocks the download request", verify that claim before carrying it into the new version. Some hosts (e.g. the IMF Datamapper) reject browser-like User-Agents with 403 while letting plain, honestly-identified clients through — the inverse of the usual bot-blocking — and the ETL downloader's default UA (DEFAULTUSERAGENT in etl/downloadhelpers.py) is browser-like, so the original author may have misdiagnosed an automatable source. Test both directions (plain requests vs. browser UA) against the direct file URL. If the plain UA works: set urldownload in the .dvc and pass useragent="owid-etl/1.0 (https://ourworldindata.org)"; (or similar plain UA) to snap.createsnapshot(...). Keep the snapshot .py script in that case — the script-less .dvc-only path (runsnapshotdvconly) calls createsnapshot() without a user_agent and would 403 — and say so in the docstring so nobody deletes it as "redundant".
• Manual-upload snapshots: also re-check for a stable download endpoint. Distinct from the inverse-UA case above: producers add direct links over time, so a snapshot that genuinely required a manual download last cycle may be automatable now. Check the producer's download page or API for a stable (ideally version-less) URL before carrying the manual flow forward; if one exists, convert the snapshot to a script-less urldownload .dvc. Multi-file archives stay script-less too: snapshot the archive itself and read the member file in meadow via snap.extracted(). If the bundle includes a codebook or series-metadata file, consider passing it through meadow as an extra table and attaching per-indicator descriptionfrom_producer in garden — inventory its fill rates first, and skip fields owned elsewhere (units, license, dataset-level boilerplate).
• "Constant YYYY" labels must match the actual base period — whose convention that is depends on who deflates. For a real series OWID computes, verify the rebasing base is the YYYY annual average — prefer the producer's own annual-average row (e.g. BLS's M13 period) — not a single month that happens to fall in YYYY: a January base labeled as the year is ~5% off in a fast-inflation year, passes every schema check, and ships a mislabeled level. State the rebasing explicitly in description_processing. (Real case: "constant 2021 US$" was rebased to January 2021; switching to the 2021 annual average moved the level +5% and made the label accurate.) For a constant-price series the producer supplies, do the opposite: confirm and document their stated base-period methodology (some legitimately define "constant YYYY" against a specific month, quarter, or chain-linked reference period while naming the year) — never rebase their values to an annual average to make the label fit.
• Metadata rewrites don't reach charts that pin their own FAUST. When an update changes methodology or scope wording in indicator metadata (grapherconfig.note, descriptionshort, deflator/source claims), query the authored layer (the chartconfigs row named by patchConfigId) for every affected chart (published and draft) to detect which FAUST fields are pinned: any chart whose patch overrides title/subtitle/note keeps the stale text no matter what the indicator now says. Propose the minimal text edit (usually one sentence), ask before changing (reader-facing FAUST), then apply via the staging admin API with the full config as the payload — fetch the rendered config (gm.Chart.config, or chartconfigs.config via charts.configId), edit the field, and pass that to apps.chartsync.adminapi.AdminAPI.updatechart; the server re-derives the stored patch by diffing against the inherited layer, so inheritance survives. Do not PUT the bare patch: for charts inheriting from an indicator grapherconfig, fields present only in the rendered config are silently dropped (in-repo callers like apps/indicatorupgrade submit chart.config, the resolved full config, for exactly this reason). Verify on the staging SVG render, and make sure the chart's diff gets approved in Chart Diff before merge: chart-sync copies a staging chart to production only when its diff is approved (apps/chartsync/cli.py checks diff.isapproved; pending diffs are skipped), so an unapproved FAUST edit silently stays behind on staging while the PR merges. After the production bake, spot-check the live chart SVG for the new wording. When many charts pin the same sentence with an embedded year (deflator base years especially), swap by literal substring (constant 2023 US$ → constant 2024 US$), never by year-regex — neighbouring years in the same note (a GDP PPP year, a methodology-break year) must not move. And if the pinned sentence will restale at every release, propose the durable fix alongside: move it into indicator metadata via a definitions variable (e.g. constant {definitions.inflationyear} US$) and clear the chart-level overrides in a follow-up.

Clearing a pinned FAUST field to "restore inheritance" is only safe if the field is actually in the inherited layer — check first. Chart FAUST inherits from presentation.grapherconfig only; presentation.titlepublic does not enter the chart-config inherited layer, so a chart whose sole override is title has nothing to fall back to, and clearing it leaves the chart titleless. Read the layer before touching the chart — variables.patchConfigIdETL → chartconfigs.config shows exactly which keys it carries. When the field is missing, the fix is two steps in order: add it to presentation.grapherconfig in garden, rebuild and upload (grapher:// for the MySQL upsert), confirm it now appears in the inherited layer, and only then clear the chart-level override by PUTting the full config with that field set to the inherited value (identical values drop out of the patch). Verify on the rendered SVG, not just the stored config. (NVIDIA Q2 FY2027: a curly-apostrophe title fix never reached the chart, whose inherited layer held only note and $schema.)

• Partial-year preliminary releases: sweep per-series max years and guard single-time views. When a producer's preliminary release adds a new year to only some series, check each charted indicator's latest year rather than the dataset's (metadata.json → dimensions.years). Stacked time-series charts self-protect — grapher drops a time point whose stack is incomplete — but a single-time discrete view (minTime: "latest" + StackedDiscreteBar) happily renders the partial year, and display.tolerance backfills the missing series with prior-year values, so the bar looks complete while silently mixing vintages (for shares, mixing denominators). Never conclude "the chart has all the data" from the render — check the series' own max years. Guard such views with timelineMaxTime: <last complete year>, not maxTime (a maxTime pin only moves the default; the slider still reaches the bad year), and remove the clamp at the detailed release. (ODA: the in-donor share chart rendered a "2025" bar stacking 2025 refugee/admin shares with tolerance-carried 2024 scholarship shares.)
• Audit blanket transformation rules per indicator before trusting them. Any garden rule that applies one transformation to a pattern-matched group of indicators — unit scaling by title keywords, sign conventions, currency or magnitude conversions — assumes the source stores the whole group in a single convention. Sources mix conventions, especially when different indicators come from different upstream providers. Scan each matched indicator's raw range to confirm it fits the assumed convention, split the rule where it doesn't, and guard both sides with sanity checks (inputs within the assumed convention; outputs within a data-grounded bound). "Matches the previous version" is not evidence of correctness — magnitude bugs are inherited from the old step; judge absolute plausibility against real-world values, not just old-vs-new equality. (This caught a long-standing 100× inflation: fraction-stored share indicators sharing a ×100 rule with ratios the source already stored in percent.)
• Programmatic metadata: still curate the charted indicator(s). When a dataset's indicator metadata is generated in code (titles/units inferred from source names), the indicators actually used in charts deserve an explicit per-indicator block in the garden .meta.yml — descriptionshort, descriptionkey, display — layered on top (YAML merges per-field, so code-set fields like descriptionfromproducer survive). Ground the bullets in the producer's codebook/methodology PDF rather than inferring from the data: the codebook yields the precise scope, the exact numerator/denominator, and caveats you won't guess (e.g. WWBI approximates the EEA 2004–2018 public sector from industry classifications). Leave a # NOTE: in the YAML explaining the programmatic/curated split for the next maintainer.
• Unguessable producer filenames: find the document index instead of brute-forcing URLs. When a producer's download filenames follow no stable convention, stop guessing after one or two 404s and look for the machine-readable index behind their JavaScript-rendered page — the page HTML itself is usually an empty shell, so curl/WebFetch on it finds nothing, but the index the page calls returns JSON with one entry per document (Q4 Inc investor sites, common for US-listed companies, expose it under /feed/). Record the endpoint as a # NOTE: in the snapshot so the next updater looks it up rather than repeating the search, and note that the same index answers "was any older file moved or renamed" — comparing its entries against the hardcoded URLs is a cheap moved-file check. It is not an in-place-revision check: a file replaced under the same URL looks identical in it, so for that use the index's per-document dates where it carries them, otherwise the file dates/hashes in the in-place-revision guardrail below. (NVIDIA renamed its quarterly PDF; six guessed patterns all 404'd.)
• Scraped chart embeds: the page's own data tables are the canonical source — embed CDNs lag. When a snapshot's data lives in a chart embedded on the producer's page (Datawrapper and similar), don't fetch the chart platform's CDN endpoint (datawrapper.dwcdn.net/<id>/<version>/dataset.csv): the latest published chart version can trail the page by a full release (observed with Gallup's AI indicator: the page's tables already carried the May 2026 survey wave while the chart CDN's newest version still ended at February 2026 — caught by Codex, not by the snapshot diff). Producer pages server-render each embed's data as an HTML fallback table (<noscript><table>), so parse that instead: whole-page pd.readhtml(io.StringIO(resp.text)), select the table whose columns exactly match the expected header, assert exactly one match. Related trap: the producer's visible "Updated" stamp and prose can lag their own data tables — trust the data, and when the newest rows have no stamped release date, datepublished falls back to date_accessed (document why in a .dvc comment).
• DAG consistency: After etl update, always verify that all new steps in dag/main.yml reference each other with the new version. A common bug is garden depending on old meadow or old snapshot — this silently loads stale data.
• Never return empty tables or comment out logic as a workaround — fix the parsing/transformations instead.
• Column name changes: update garden processing code and metadata YAMLs (garden/grapher) to match schema changes.
• Indexing: avoid leaking index columns from reset_index(); format tables with tb.format(["country", "year"]) as appropriate.
• Metadata validation errors are guidance — update YAML to add/remove variables as indicated.
• Mixed-type object columns at meadow: when pd.read_csv produces an object column that mixes strings and NaN (common for sparse text columns like sources/comments/punishments), the feather repacker rejects it. Cast those columns to pandas "string" dtype before tb.format(...).
• paths.regions auto-resolves DAG dependencies: paths.regions.addpopulation(tb) and paths.regions.addaggregates(tb, regions=[...]) pick up the population and incomegroups datasets directly from the DAG. Don't paths.loaddataset("population") and pass it through unless the helper specifically asks for the dataset — the parameter is unused.
• WB income-group aggregates: add the four classification names (High-income countries, Upper-middle-income countries, Lower-middle-income countries, Low-income countries) to your REGIONS list and add data://garden/wb/<latest>/incomegroups to the DAG. paths.regions.addaggregates(...) auto-resolves the classification.
• Detect structural placeholders dynamically: when a source ships "balanced panel" rows that are zero everywhere by design (status combos that exist only for completeness), detect them at runtime (groupby(...).max() == 0) and assert the count matches the codebook. A coding change in the source then surfaces as a test failure instead of silently shipping noise.
• In-place source revisions: compare file dates/hashes, not version labels. Some producers replace the published file (and codebook) without bumping their stated version and without a changelog. When checking whether a source has a new release, look at the hosting platform's file-modification dates or hashes (e.g. the OSF API's datemodified per file) against the previous snapshot's dateaccessed/md5 — an unchanged version label proves nothing. If you ingest such a revision, set date_published to the replacement date and leave a .dvc NOTE naming the behavior so the next updater re-checks. (Velasco LGBTI: 6,813 cells changed under the same "Version 2.0" label.)
• Category-state churn in combined categorical indicators. A source revision can add or remove the states a categorical indicator takes (a recode wave eliminating an enforcement state, a new cross-combination appearing). The net that catches it is metadata validation failing with "extra variables in YAML file / in table" on per-category regional variables. Fix all four surfaces together — the category map in step code, the sort: lists, the coding-description sentences, and the per-category regional count/pop YAML blocks — then re-run the phantom-category audit. Never keep a label the new data cannot produce.
• Guard silent catch-all buckets with an assert. A lookup that routes labeled source values into an existing fallback category ("requirement unknown", "other") degrades silently when the source adds a new label — the row lands in the fallback and nothing fires (unlike unmapped values that leak as new categories, which metadata validation catches). Assert observed labels ⊆ map keys, letting only genuinely-blank values reach the fallback. (LGBTI: two new requirement labels published 40 country-years as "requirement unknown"; Codex caught it, the build didn't.)
• Grep metadata prose for numbers carried from the old release. Validated fields are covered by checks; prose numbers in description_key/descriptions (country counts, category counts, year ranges) are not. After a revision that changes panel composition, rg the new .meta.yml for the previous release's counts. (LGBTI: "197 countries" survived in the dataset-level bullet after the Vatican was dropped.) The mirror of this check for published content that cites the dataset is the referencing-prose audit in step 7.
• **corrections.yml overrides on categorical columns must use the source's current vocabulary.** Assigning a retired label raises TypeError: Cannot setitem on a Categorical with a new category — pick the current-vocabulary value that maps to the same published output, and note the substitution in the entry.
• Codebook-vs-data inconsistencies: when the codebook documents one thing but the actual CSV shows another (placeholder claimed but non-zero rows present, etc.), preserve the data as-shipped and flag it in the PR description for the producer to confirm. Don't silently force the data to match the codebook.
• Grapher .meta.yml only when it adds something: the grapher step inherits everything via defaultmetadata=dsgarden.metadata, so drop the grapher .meta.yml if it only duplicates the garden values. Keep it only for genuine grapher-side overrides.
• processinglevel: major requires descriptionprocessing: keep processinglevel: minor as the common default and override to major only on indicators that have a descriptionprocessing field. Don't blanket-set major on the common block and then leave country-level proportions without their own processing note.
• Per-indicator description_processing reads better than a generic shared note: when an indicator is derived (combined-categorical buckets, regional aggregates, computed counts), spell out that indicator's derivation. Reusing named definitions for shared boilerplate is fine; just compose them into per-indicator sentences rather than dropping a single generic note across all indicators.
• descriptionkey in definitions.common propagates only to indicators without their own list: if you want a bullet to appear on every indicator, either keep it on common.descriptionkey and don't define per-indicator lists (it inherits), or prepend it explicitly to each per-indicator list (treats it as a "first bullet" pattern).
• Phantom-category audit on categorical indicators: after building categorical indicators, sweep every indicator and compare YAML sort: labels against the unique values that actually appear in the data. Phantom labels (declared in sort: or in a category map but never produced) clutter chart legends with empty buckets. Either drop them from sort: and description_key, or remove them from the map if they can never occur given the data shape. Re-run the audit on every data refresh — phantoms can reappear when a category is dropped upstream.
• NOTE: comments for the next maintainer when behaviour is data-conditional: when something in the code holds only because of the current data shape (e.g. "only 4 indicators have an EoE=0 row", "only Brazil 2025 is a transition-year artefact"), leave a # NOTE: comment near the relevant block asking the next data update to re-audit. Helps future maintainers spot which assumptions might decay before they bite.
• Indicator Upgrader CLI for one-shot chart remaps: when v1 → v2 shortnames change so much that the auto-upgrader can't match them, drive the remap manually. Write a small script that calls WizardDB.addvariablemapping(mapping={oldid: newid, ...}, datasetidold=..., datasetidnew=..., comments="...") with the explicit pairs, then run from apps.indicatorupgrade.upgrade import cliupgradeindicators; cliupgradeindicators(dryrun=True) to preview affected charts, and (dryrun=False) to apply. Mappings stay in the wizard DB until WizardDB.deletevariablemapping() is called, so a slug-collision failure can be recovered by fixing the slug and rerunning the upgrade — only un-upgraded charts get reattempted. The active staging DB is inferred from the current git branch.
• Drop-in vs restructure decision point: when the new dataset has a different shape (long vs wide, more policies, changed score semantics, dropped composite measures), etl update --rename is the wrong starting point — the structure of meadow/garden/grapher needs to follow the new shape, and the rename flow will only produce confusion. Spot this fork early at the snapshot/codebook stage, before running etl update. Scaffold the new chain via the [create-etl-steps](../create-etl-steps/SKILL.md) skill (wraps the wizard's cookiecutter templates) or launch the wizard UI with etlwiz and use its "ETL Steps" page — both produce a consistent meadow/garden/grapher skeleton to fill in. Once scaffolded, read the v1 scripts as a reference for the source-specific logic that's still relevant (column-rename maps, status/category normalisations, country harmonisation map, sanity checks, codebook-driven structural assertions) — don't copy the v1 structure blindly, but port the bits that still apply to the new schema.

When the update is review-heavy and you need iterative back-and-forth with a topic owner over staging, see the [report-indicator-changes](../report-indicator-changes/SKILL.md) skill for drafting the message.

Artifacts (expected)

• workbench/<short_name>/snapshot-runner.md
• workbench/<short_name>/progress.md
• workbench/<shortname>/notestocheck.md (one entry per carried-over # NOTE: / # TODO:, plus detected sanitychecks functions and their log-control flags)
• workbench/<shortname>/sanitychecks.log (only if step 5b ran)
• workbench/<shortname>/meadowdiffraw.txt and meadowdiff.md
• workbench/<shortname>/gardendiffraw.txt and gardendiff.md
• workbench/<shortname>/harmonization.log and harmonizationaudit.md (from step 5c)
• ai/adversarial-review-<short_name>-<date>.md (from step 6c-bis)
• workbench/<shortname>/indicatorupgrade.json (if indicator-upgrader was used)
• workbench/<short_name>/update-context.yml (canonical facts gathered during the update; consumed by data-updates-comms)
• workbench/<short_name>/slack-announcement.md
• workbench/<short_name>/data-update.md (public-facing post draft for OWID /latest, from step 9b)

Example usage

• Minimal catalog URI with explicit old version:

- update-dataset data://snapshot/irena/2024-11-15/renewablepowergeneration_costs 2023-11-15 update-irena-costs

Common issues when data structure changes

• SILENT FAILURES WARNING: Never return empty tables or comment code as workarounds!
• Column name changes: If columns are renamed/split (e.g., single cost → local currency + PPP), update:

- Python code references in the garden step - Garden metadata YAML (e.g., foodpricesfor_nutrition.meta.yml) - Grapher metadata YAML (if it exists)

• Index issues: Check for unwanted index columns from reset_index() — ensure proper indexing with tb.format(["country", "year"]).
• Metadata validation: Use error messages as a guide — they show exactly which variables to add/remove from YAML files.