engagement-memory

# Engagement Memory (cross-engagement learning) ## When to Activate - At **recon/weaponize**: recall what already worked against this target class / tech stack. - At **report**: persist each `[CONFIRMED]` finding as a reusable pattern (ranked by impact). - Periodic housekeeping: compact the pattern DB / rotate the audit log. ## Model Append-only JSONL store (`~/.claude/engagement-memory/patterns.jsonl`, override `$ENGAGEMENT_DB`). Three record types in their own files so they never mix: **patterns** (`patterns.jsonl`), **target profiles** (`profiles.jsonl`), **audit log** (`audit.jsonl`, disposable). A pattern is keyed by `(target, vuln_class, technique)`, ranked by **severity / CVSS / confidence** (real impact, never payout), and carries a **lifecycle status** (`proposed/active/stale/deprecated/...`). Recall is an **explicit top-N query** (anti-context-bloat). Duplicates **merge** (count bumped, most-recent status wins), never blind-discarded; `compact` runs automatically over a size threshold and stays lossless. TTL `stale` patterns and `deprecated/rejected` ones drop out of default recall but are kept. ## Commands ```bash # RECALL — relevance-ranked (stdlib BM25 + aliases), active-only by default python skills/engagement-memory/scripts/pattern_db.py match --vuln-class ssrf --query "imds metadata" --tech-stack aws # INJECT — budgeted prior-intel card for a phase (top-N, byte-capped; $ENGAGEMENT_MEMORY_MODE=auto|debug|off) python skills/engagement-memory/scripts/patter...