القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.