name: engagement-memory description: Use when recalling prior techniques at recon/weaponize, or recording a confirmed finding at report — cross-engagement pattern memory ranked by impact metadata: type: support phase: all tools: pattern_db.py, schemas.py, rotation.py kill_chain: phase: [recon, weaponize, report] step: [1, 2, 8] attck_tactics: [] attck_techniques: [] depends_on: [vulnerability-analysis] feeds_into: [recon-osint, exploit-development, web-pentest] inputs: [confirmed_findings, target, tech_stack] outputs: [prior_intel, ranked_patterns] references: [] scripts:

• scripts/pattern_db.py
• scripts/schemas.py
• scripts/rotation.py

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

# 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/pattern_db.py inject --vuln-class ssrf --query imds --max-bytes 1500

# RECORD a confirmed finding (flags or finding JSON). A key collision needs --resolve update|merge|reject|force.
python skills/engagement-memory/scripts/pattern_db.py record --target acme.com --vuln-class ssrf \
    --cwe CWE-918 --attack-id T1190 --severity high --cvss 9.1 --tech-stack nginx,aws --technique "metadata theft"
python skills/engagement-memory/scripts/pattern_db.py record --json '<finding json from validate_findings>'

# LIFECYCLE + cross-client
python skills/engagement-memory/scripts/pattern_db.py promote   --target acme.com --vuln-class ssrf --technique "metadata theft" [--global]
python skills/engagement-memory/scripts/pattern_db.py deprecate --target acme.com --vuln-class ssrf --technique "metadata theft"
python skills/engagement-memory/scripts/pattern_db.py match --vuln-class ssrf --include-global   # add sanitized cross-client TTPs

# PROFILES + housekeeping + observability
python skills/engagement-memory/scripts/pattern_db.py profile --target acme.com --tech-stack nginx,aws --endpoints /api,/admin
python skills/engagement-memory/scripts/pattern_db.py recall-profile --target acme.com
python skills/engagement-memory/scripts/pattern_db.py compact         # manual lossless dedup-merge
python skills/engagement-memory/scripts/pattern_db.py stats           # patterns by class + profile count
python skills/engagement-memory/scripts/pattern_db.py audit-stats     # action log: by tool/action/outcome

Or use the /engage.memory command (recall | inject | record | promote | deprecate | gc | stats).

OPSEC & Detection

| Concern | Note | |---------|------| | Secrets at rest | Stores technique + CWE/CVSS + an evidence reference, never loot. A secret-input guard rejects evidence_ref/source that look like inline secrets (private keys, password=, AKIA, JWTs, tokens) — store a path; rotate the exposed credential, don't just delete. | | Cross-client bleed | Per-client isolation is the default ($ENGAGEMENT_DB). The shared global store is opt-in (promote --global / record --global) and sanitized (target + evidence blanked); recall it only with --include-global. | | Trust | New auto-captures can be proposed; only confirmed/reviewed findings are active. A key collision is review-gated (--resolve), not silently merged. | | Auditability | Every record/match/compact/promote — and every refused line (denial) — is written to audit.jsonl (rotated by discard, with a retention-gap marker). The append-only patterns journal + audit log ARE the history. | | Integrity | Records carry schema_version; malformed/type-poisoned/foreign lines are skipped on read, never trusted. |

Deep Dives

• scripts/schemas.py — record types (pattern/audit/target_profile/retention_gap), validation + secret guard, pattern_key/pattern_id, impact+confidence rank_score, recency-resolving merge.
• scripts/pattern_db.py — typed routing, merge-on-read with TTL staleness, BM25 relevance recall, inject, lifecycle verbs, global scope, CLI.
• scripts/rotation.py — compact/maybe_gc (lossless dedup-merge, auto-triggered) vs rotate_audit (discard the disposable log + write a retention-gap marker).