Memory collector

Your richest memory source is sitting untouched on disk: every coding-agent session ever run on this machine. Transcripts full of decisions, gotchas, open questions, people, and the occasional quotable outburst — none of it queryable, all of it rotting in JSONL.

This skill is the collector: a budgeted, cursor-tracked harvest that mines those transcripts and plants durable knowledge into whatever memory stores the current environment exposes. It is storage-agnostic (capabilities discovered at run time, like [memory-gardener](../memory-gardener/SKILL.md)) and source-aware (it knows where coding tools keep their sessions). The extraction prompts ship in [prompts/](prompts/README.md), carried from EI's extraction pipeline by Jeremy Scherer (MIT).

The composition: the collector plants; the gardener prunes. Collection deliberately tolerates near-duplicates and overgrowth — the gardener's validate gate, dedup curator, and bloat-split exist precisely to tend what collection produces. Don't make the collector perfect; make the pair converge.

Ground rules — the safety contract

1. Sources are read-only. Never modify, move, or delete a transcript file.
2. Stores are additive. The collector creates and updates memory items; it

never deletes. Anything that looks delete-worthy is the gardener's job.

1. Budget every run. Default: 3 sessions (or ~150 messages) per run,

oldest unprocessed first. Stop at the budget; the cursor makes the next run continue cleanly.

1. Skip live sessions. A transcript modified in the last ~30 minutes (or

whose tool is plainly mid-session) gets skipped — half-written sessions extract badly. It will be there next run.

1. Never store secrets. Coding transcripts contain tokens, connection

strings, ARNs, and keys. If an extracted value is shaped like a credential, drop it. The shipped prompts already exclude these from quotes; apply the same bar to every field you store.

1. Conservative is the law. The shipped prompts are tuned so that *empty

results are the most common response*. Honor that — noise is worse than gaps.

1. Provenance is mandatory. Every stored item carries its source id (see

Phase 4). An item you can't trace back to a session is a rumor.

Phase 0 — survey

Transcript sources. The skill bundles dependency-free Node readers ([readers/](readers/README.md)) — run each with node readers/<tool>.mjs --list --since <cursor high-water mark> to discover what exists. Do not parse session stores by hand: the readers already encode the format traps (sidechain files, tool-result records masquerading as user messages, lossy cwd encodings).

Memory stores. Discover capabilities from the tool surface exactly as the gardener's Phase 0 does — memory search/mutation, knowledge graph, diary, stats. Don't assume tool names.

The cursor. Find the previous collection state: a memory item or artifact tagged collector-cursor holding, per source: a highWater timestamp (pass it as --since when listing) plus maps of processed and skipped-trivial session ids → timestamps (the maps are the sole source of truth — EI's processed_sessions pattern; highWater is the cheap pre-filter that keeps a noisy source from re-listing hundreds of already-judged sessions every run). No cursor → first run: start with the most recent few sessions, not all of history; backfill over subsequent runs.

Phase 1 — select

From each available source, list sessions not in the cursor, oldest first, and take sessions up to the budget. Apply the live-session guard (rule 4). The readers supply title (cwd-derived) and messageCount per session.

Prefer real conversations. Agent automation produces sessions too — a Pi store can hold a thousand mechanical runner-job sessions for every human one. Skip sessions that are tiny (fewer than ~4 messages) or whose opening message is plainly a machine-generated job prompt, and record them in the cursor as skipped-trivial so they are never re-listed. Spending the budget on noise is how a collector starves.

Phase 2 — convert

node readers/<tool>.mjs --session <id> returns the session already reduced to a clean conversation — human text and assistant text only; thinking blocks, tool calls/results, system noise, and sub-agent chatter are stripped by the reader. From that output:

• Build fully qualified message ids: <tool>:<machine>:<session>:<reader msg id>

(e.g., claudecode:mbp:0a1f…:42). Quotes and provenance point at these.

• Process the session in windows (~20–40 messages). For each window, the

window itself is the "Most Recent Messages" and a compact tail of what came before is the "Earlier Conversation" — the shipped prompts are built around exactly this split and only ever analyze the recent window.

Phase 3 — extract

Run the shipped pipelines over each window, with technical_context: true for coding-tool sessions (it makes Technical a priority category):

1. Topics — [prompts/topics.md](prompts/topics.md): scan flags candidate

topics → match checks each against existing memory (conservative: unsure ⇒ "new") → update writes the record under the right discipline (Event narratives; Technical accumulate, don't synthesize; everything else synthesize, don't accumulate). Quotes ride along.

1. People — [prompts/people.md](prompts/people.md): scan flags people

(confidence 1–5, identifier capture, self/hypothetical guards) → match by identifiers first, then name → update under the person disciplines. For coding sessions most windows yield nobody; that's correct.

1. Events — [prompts/events.md](prompts/events.md): once per session, the

campaign-recap test ("The Night We Debugged the CPU"). Empty is the norm.

1. Facts — [prompts/facts.md](prompts/facts.md): only if you maintain a

missing-facts list (kept beside the cursor). No list, no run.

Phase 4 — store

Write extractions into the discovered stores, mapping fields onto the store's schema (confidence/exposure-impact → importance-like fields; categories → tags/containers; drop fields the store can't hold rather than inventing homes). Tag everything source:<tool>:<machine>:<session> plus collected:<ISO date>.

Where the store distinguishes recent/unreviewed items, leave new items visibly new — the gardener's validate gate ([its Phase 1](../memory-gardener/SKILL.md)) is the door these newcomers are supposed to walk through. If both a fast store and a structured knowledge store exist, put summaries where retrieval happens and structure (entities, links) where the graph lives.

Phase 5 — advance the cursor & report

Update the cursor only for sessions fully processed — a budget-truncated session stays uncursored and resumes next run. Also advance each source's highWater to the newest lastMessageAt among sessions you resolved (processed or skipped-trivial), but never at-or-past a skipped-live session's timestamp — live sessions must re-list once they settle. Then report:

# Collection report — <ISO timestamp>
Sources: <tool: sessions found / processed / skipped-live>
Windows analyzed: N · budget used: <sessions>/<max>
Planted: topics N (new X, updated Y) · people N · events N · facts N · quotes N
Dropped: secrets-shaped values N · low-confidence extractions N
Cursor: advanced to <session id / timestamp> per source
Handoff: <n> new items awaiting the gardener's validate gate

Substrate semantic completion

When this skill runs as a Substrate runner or workflow job, keep the collection report as the human-readable output, then make the final non-empty assistant line exactly one SUBSTRATEOUTCOMEV1= declaration. Lifecycle completion is not the success signal.

Declare outcome: "succeeded" only when every counted session was fully processed or honestly skipped, the cursor advanced only across resolved sessions, planted items retain provenance, and the report and cursor were persisted and read back through the available store. A partial uncursored session is allowed only when it is reported as the resume point; lost provenance, cursor advancement past unresolved work, secret persistence, or failure to persist the required report is outcome: "failed". Memory-store writes are not Git changes, so use changes.status: "notApplicable".

Example success line (replace placeholders with real evidence):

SUBSTRATE_OUTCOME_V1={"version":1,"outcome":"succeeded","summary":"Collected <n> sessions and persisted report <artifact-id>","evidence":{"changes":{"status":"notApplicable","reason":"Memory-store mutations are not Git changes"},"verification":{"status":"passed","commands":["read back collector cursor","read back report artifact <artifact-id>"]}}}

Emit no second declaration and nothing after it. Outside a Substrate job, do not add this platform-specific line.

Running periodically

Same hosting story as the gardener: any scheduler that can invoke an agent with this skill. A good rhythm — collector daily, gardener nightly after it — so each harvest is tended within a day. Both are budget-capped; worst case is a report that says "nothing new."

Provenance & credit

The extraction pipeline (scan → match → update), its conservative defaults, the three description disciplines, the quote bar-test, and the prompts in [prompts/](prompts/README.md) come from Flare576/ei by Jeremy Scherer (MIT, © 2026 Jeremy Scherer) — EI runs this pipeline against five coding tools as its importer layer. This skill generalizes the storage side and pairs it with [memory-gardener](../memory-gardener/SKILL.md).