Creative Signal

Correlate creative attributes with hook rate (3-second views ÷ impressions) and hold rate (thruplay ÷ 3-second views) across every video ad that delivered in the window. Scripts pull metrics into a local SQLite store, tag each creative once (Gemini gemini-3.1-flash-lite + ffmpeg features), and compute per-attribute lift with explicit confidence. The agent reads signals.json and authors brief.md — judgment on top, never re-derived math.

Arguments: $ARGUMENTS — optional window (default last14d; valid last7d, last14d, last30d), or reconfigure, or backfill.

v1 makes no revenue or ROAS claim. Hook and hold are attention proxies. State this in every brief.

Data Architecture

<skill-dir>/                          # this folder — Base directory injected when the skill loads
├── .venv/                            # created by onboarding; every script runs via .venv/bin/python
├── scripts/                          # run.py orchestrates the rest
└── references/                       # onboarding.md, taxonomy.md, gemini-prompt.md

~/.meta-ads-intel/                    # shared home with meta-ads-intel (one auth, one brand context)
├── config.json                       # account_id, currency, brand targets — shared, read-only here
├── brand-context.md                  # product/audience/hooks — shared, read for the brief
├── creative-signal.env               # GEMINI_API_KEY=… (mode 0600)
├── creative-signal.db                # SQLite: ad×day metrics + tag-once cache (mode 0600)
├── creatives/                        # CLI video snapshot — REPLACED on every --keep-video pull; never rely on it
└── creative-signal/
    └── runs/<until>/
        ├── signals.json              ── agent reads ──
        ├── run-status.json           ── agent reads ──
        └── brief.md                  ── agent writes ──

The store is the durable artifact. Videos are transient; the assethash → tags row in creativetags is what survives.

Process

0. Mode Gate

Check the four prerequisites first, one command each; do not guess. Then route — the first match wins.

Prerequisites:

• <skill-dir>/.venv/bin/python exists
• ~/.meta-ads-intel/config.json has a valid account_id
• ~/.meta-ads-intel/creative-signal.env contains GEMINIAPIKEY (or the env var is set)
• the store is non-empty: <skill-dir>/.venv/bin/python <skill-dir>/scripts/store.py status reports metric_rows > 0

1. Any prerequisite missing → ONBOARDING MODE: read references/onboarding.md and follow it completely. Onboarding ends with the first analysis run and STOPS. Onboarding is idempotent and skips satisfied phases, so entering it with a partial setup is safe; a reconfigure or backfill argument is ignored until setup is complete (an empty store with everything else present is the one case where backfill and onboarding coincide — onboarding's Phase 6 handles it).
2. $ARGUMENTS contains reconfigure → read references/onboarding.md → "Reconfigure Mode". STOP when it prints "Config updated".
3. $ARGUMENTS contains backfill → read references/onboarding.md → "Phase 6: Backfill" only (loads more history into an existing store), then STOP.
4. Otherwise → ANALYSIS MODE: continue to Step 1.

1. Load Context

Read ~/.meta-ads-intel/brand-context.md (product, audience, proven hooks). If missing, proceed and note "brand context unavailable — recommendations are attribute-level only" in the brief.

Read references/taxonomy.md — every attribute the scripts can emit, its source, and how to interpret it. Do not invent attributes that are not listed there.

2. Run the Pipeline

<skill-dir>/.venv/bin/python <skill-dir>/scripts/run.py --window $ARGUMENTS

Omit --window when $ARGUMENTS is empty (defaults to last14d). The run is idempotent: sync fetches only the gap plus a trailing 7 days (Meta revises recent days), videos download only for ads that still need one, Gemini is called once per new assethash. A second run minutes later does nothing but recompute correlation.

Steps inside run.py: sync → assets → deterministic → gemini → correlate. A bad video or a missing Gemini key degrades to a warning; the run still produces signals.json.

Runtime: first run after backfill tags every video (≈3 s + 9k tokens per ad; 200 ads ≈ 10 min). Steady state ≈ 30 s.

Exit codes:

• 0 — read outdir, signalsfile, and warnings from the JSON on stdout.
• 3 — store empty. Tell the user, then run references/onboarding.md → Phase 6 (backfill). Do not loop.
• 1 — read the single stderr line. Another pull instance is running means /meta-ads-intel holds the lock; the script already retried for 5 minutes — ask the user to wait for it and rerun. API access blocked / Session has expired are auth problems: report and stop; suggest meta-ads setup. Anything else: report verbatim and stop.

Read run-status.json from out_dir. Surface these before analysis:

• steps.sync.new_ads > 0 — new creatives entered the account this window; say how many.
• steps.assets.unavailable non-empty — ads whose video could not be fetched (deleted, or over the CLI's 500-ad cap); they contribute metrics but no attributes.
• steps.gemini.skipped true — no attributes from Gemini this run; the brief covers deterministic features only. Say why (reason).
• steps.gemini.failed > 0 — creatives Gemini could not parse after retries; rerun with --retry-failed later via scripts/tag_gemini.py.
• steps.correlate — neligible of nads, and the strong/directional/anecdotal counts. These are the headline numbers of the brief.

3. Read signals.json

Structure (spec §10):

{ "run":      { "window", "since", "until", "account_id", "n_ads", "n_eligible", "n_tests",
                "min_impressions", "model", "shopify_enabled", "confidence_rules" },
  "ads":      [ { "ad_id", "ad_name", "campaign_name", "impressions", "video_view", "thruplay",
                  "hook_rate", "hold_rate", "attributes": {...}, "flags": [...], "eligible" } ],
  "signals":  [ { "attribute": "first3s_content=face", "metric": "hook_rate",
                  "n_group", "n_rest", "mean_with", "mean_without", "lift_pct",
                  "effect_size", "p_value", "q_value", "confidence" } ],
  "warnings": [ "..." ] }

signals is pre-sorted: strong → directional → anecdotal, then by |effect_size|. Read it top-down.

Attribute labels: categorical key=value (tested with-vs-rest); numeric key>=<median> (median split); booleans key=true. Every signal exists twice at most — once per metric (hookrate, holdrate).

Per-ad flags explain exclusions: untagged (no cached tags), novideoview (image ad — no hook/hold possible), partialretention (holdrate unavailable — retention fields missing for part of the window; happens for days pulled before CLI 0.19), belowminimpressions (under 1000). Only eligible: true ads entered the tests.

4. Interpret

Confidence is the load-bearing field. Read run.confidence_rules and apply:

Rules that keep the brief honest:

• If n_eligible < 20, no signal can be strong — open the brief with that fact and frame everything as hypotheses.
• qvalue is the Benjamini-Hochberg-adjusted p across all ntests. If a strong signal has qvalue > 0.10, say it may be a multiple-comparison artefact. Always state ntests in Confidence & Caveats.
• liftpct is relative (0.375 = +37.5%). Report meanwith vs mean_without as percentages alongside it so the absolute gap is visible.
• Hook and hold answer different questions. A hook signal is about the first 3 seconds (first3scontent, hooktext, timetofirstcut, energyfirst3s, brandingfirst3s). A hold signal is about the body (cutcount, avgshotlen, sound_mode, emotion, transcript length). Do not attribute a hold signal to an opening choice.
• tempobpm and other audio-lane features are only meaningful when soundmode includes music. Gate on it.
• Numeric splits: cut_count>=7 means "ads at or above the median of 7 cuts". Say "more cuts than the median" rather than quoting the threshold as a target.
• A signal on formatstyle=static or subject=textgraphic with a tiny n_group usually means one outlier ad. Check ads for that group before reporting.
• Mirror pairs: a two-valued attribute is tested once. facespresent=true lifting hook by +20% implies facespresent=false at −20%; do not list both as separate findings.

Read ads to ground every reported signal in concrete examples: for each strong or directional signal, name the top 2 ads by the metric inside the group and 1 outside it. Quote hook_text verbatim where present — it is the most actionable attribute and is never tested statistically.

5. Write brief.md

Write <out_dir>/brief.md. Structure, in order, headers verbatim:

1. Window & coverage — window dates, neligible of nads ads, why the rest were excluded (from flags counts in warnings), tags source (run.model), and whether Gemini ran. One paragraph.
2. What works — strong signals first, then directional. One bullet per signal: attribute in plain words, metric, lift with means, n, confidence, two example ads with hook_text where it exists.
3. What doesn't — signals with negative lift, same format. Include the mirror reading of a positive finding only if it names a different actionable choice.
4. Why — 2–4 sentences connecting the signals to the brand context and the audience. This is the only interpretive section; label inference as inference.
5. What to try next — up to 3 recombination briefs. Each: the attribute combination to produce (drawn only from strong/directional signals), a hook line modelled on the best-performing hook_text, the format, and which metric it targets. Tie to products/audience from brand-context.md.
6. Confidence & caveats — always present, always these points: hook/hold are proxies with no revenue link claimed in v1; ntests tests were run and qvalue is the adjusted p; small groups; ads excluded and why; anything in warnings not already covered.

Numbers in the brief come from signals.json and run-status.json only. Never compute a rate, mean, or lift yourself. If a number the brief needs is absent, say it is absent.

Length target: 400–800 words. Fewer signals, shorter brief — do not pad an anecdotal-only run.

6. Return

Print: window, neligible/nads, counts by confidence, the single highest-value finding in one line, and both paths (signals.json, brief.md).

If this is the first analysis after onboarding, offer the optional daily pre-warm scheduler once (references/onboarding.md → "Optional: daily pre-warm"). Never offer it again in later runs.

Rules

• Run every script through <skill-dir>/.venv/bin/python. System python3 lacks google-genai.
• Never read ~/.meta-ads-intel/creatives/ directly — it is a snapshot the CLI replaces. Read ads[].attributes from signals.json instead.
• Never read ~/.meta-ads-intel/data/ — that belongs to meta-ads-intel.
• Never call meta-ads intel fetch-daily --keep-video yourself. run.py decides when a video pull is needed; a manual call re-downloads every video and wipes the snapshot.
• Never re-run correlation, recompute means, or count ads[] by hand. All counts are in run-status.json and run.*.
• Never make a revenue, ROAS, purchase, or CPA claim from this skill's output. Redirect budget questions to /meta-ads-intel.
• GEMINIAPIKEY is read from the env or creative-signal.env by the scripts. Never echo it, never write it into a run dir or brief.
• Spend data is sensitive: run.py sets umask 077. Do not copy outputs outside ~/.meta-ads-intel/ unless the user asks.