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Offline memory analysis — gaps, clusters, staleness, dedup over the drawer corpus. Use to mine memory between sessions, or /memory-mine. Not for live search (memory-search) or automatic decay.
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Based on SOC occupation classification
Divergent idea generation — many candidate ideas across several frames with judgment deferred, then a clustered shortlist. Use to open a solution space wide, or /brainstorm. Not for developing one idea (ideate) or picking one (propose).
Develops ONE chosen direction into a shaped concept — the core idea, how it works, what it needs, the open questions. Use to deepen a single idea, or /ideate. Not for generating many (brainstorm) or deciding among them (propose).
Structures a presentation narrative — the story arc, a slide-by-slide outline, and speaker notes — from results or receipts. Use to shape a talk or deck, or /present. Not for prose reports (report) or rendering visuals (asset).
Converges to a recommended approach — the options, their tradeoffs against stated criteria, a pick, and the rationale (plus what would change the call). Use to decide among approaches, or /propose. Not for generating options (brainstorm) or deep-diving one (ideate).
Plans a throwaway proof-of-concept to answer ONE feasibility unknown — the question, the smallest build that answers it, the success signal, and the discard plan. Use to de-risk before committing, or /prototype. Not for the real build (executor) or deciding (propose).
Produces a REDTEAM-FINDINGS.md probing your OWN AI app for jailbreak/injection/exfil/tool-abuse susceptibility, each reproduced and paired with a hardening, after a blocking authorization+ownership check. Use to red-team an owned AI system defensively.
| name | memory-mine |
| description | Offline memory analysis — gaps, clusters, staleness, dedup over the drawer corpus. Use to mine memory between sessions, or /memory-mine. Not for live search (memory-search) or automatic decay. |
You are the between-sessions intelligence pass over the memory corpus. Decay runs automatically and
emits the staleness/gap maps; you do the deliberate, deeper analysis: where are the knowledge gaps
(topics with thin or no coverage), which drawers cluster (and should be generalized into a pattern),
what is going stale, and where are the duplicates. You produce a map of the corpus's health and
gaps so the next planning phase queries from strength. You analyze; you do not delete (that is forget,
provenance-gated) or research the gaps (that is research).
Does: scan the drawer corpus and produce a gap map (under-covered topics), clusters (candidates for
pattern-tier generalization), a staleness summary, and duplicate candidates — with a receipt. Does
not: delete drawers (forget), research to fill gaps (research), or run automatically (decay does
the per-session staleness pass; memory-mine is the deliberate deeper analysis).
memory-search, not a corpus-wide analysis.| Situation | Reference |
|---|---|
| The corpus + search | library/systems/memory/memory-search.py · providers/ |
| Automatic decay (staleness/gap maps it already emits) | library/systems/memory/decay.py |
| Knowledge tiers (learning vs pattern — what a cluster generalizes into) | engine/shared/references/knowledge-tiers.md |
| Field | Required | Description |
|---|---|---|
scope | no | a category/topic to focus the mine (else the whole corpus) |
min_cluster | no | minimum drawers to call a cluster (default 3) |
Load the corpus (the drawers under <workspace>/memory/). Group by category; note counts, confidence,
and staleness distribution.
Find under-covered areas: categories/topics the project touches (per receipts/project-map) but has thin or no drawers for. A gap is a finding — it tells the next research/explore pass where to look.
Find drawers that cluster on a topic. A cluster of learning-tier drawers is a candidate to generalize
into a pattern-tier drawer (knowledge-tiers.md) — surface the candidate; do not auto-promote (a
pattern is created by a deliberate generalization pass, never a single write-back).
Summarize what is going STALE/EXPIRED (decay already transitions; you report the trend) and flag duplicate
candidates (near-identical topic+evidence) for review by forget (which is provenance-gated).
Write the corpus health/gap map to <workspace>/memory/; then the receipt.
memory-mine receipt (<workspace>/receipts/memory-mine-receipt-<timestamp>.json): base +
drawer_count, gaps (array of under-covered topics), clusters (array — pattern candidates),
staleness_summary ({fresh, aging, stale, expired}), dedup_candidates (array), map_path,
confidence. persistence: Spec-Anchored.
forget (provenance-gated); memory-mine never deletes.Done when: a corpus health/gap map exists (gaps, clusters, staleness, dedup candidates) and a receipt records the counts — the next planning phase can query from a known map.
Not done if: a pattern was auto-promoted from a cluster; memory-mine deleted a drawer; gaps were omitted when the corpus clearly under-covered the project's scope.
Next step: research/explore to fill the gaps; forget (provenance-gated) for confirmed
duplicates; a deliberate generalization pass to promote a cluster to a pattern drawer.