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Engagement memory

Skill hypnguyen1209/offensive-claude/skills/engagement-memory

Use when recalling prior techniques at recon/weaponize, or recording a confirmed finding at report — cross-engagement pattern memory ranked by impactFrom its SKILL.md

Install
npx -y skills add hypnguyen1209/offensive-claude --skill engagement-memory

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

  • runs commandsInstructs the agent to run 8 commands, including `python skills/engagement-memory/scripts/pattern_db.py match --vuln-class ssrf --query "imds metadata" --tech-stack aws` and 7 more.

SKILL.md

5.3 KB, ~1.2k tokens by cl100k_base, as published. Nobody here has run it

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

ConcernNote
Secrets at restStores 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 bleedPer-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.
TrustNew auto-captures can be proposed; only confirmed/reviewed findings are active. A key collision is review-gated (--resolve), not silently merged.
AuditabilityEvery 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.
IntegrityRecords 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.pycompact/maybe_gc (lossless dedup-merge, auto-triggered) vs rotate_audit (discard the disposable log + write a retention-gap marker).

What ships with it: 3 files

42.3 KB alongside SKILL.md, 3 of them executable

scripts/

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