Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add hypnguyen1209/offensive-claude --skill engagement-memorygit clone --depth 1 https://github.com/hypnguyen1209/offensive-claudeWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/hypnguyen1209/offensive-claude/engagement-memory)<a href="https://agentmods.dev/skills/hypnguyen1209/offensive-claude/engagement-memory"><img src="https://agentmods.dev/badge/skills/hypnguyen1209/offensive-claude/engagement-memory/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/hypnguyen1209/offensive-claude/engagement-memory"><img src="https://agentmods.dev/badge/skills/hypnguyen1209/offensive-claude/engagement-memory.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00031 | $0.01359 |
| Opus 5 | $0.00015 | $0.00679 |
| Sonnet 5 | $0.00006 | $0.00272 |
| Haiku 4.5 | $0.00003 | $0.00136 |
Grade A, and why
engagement-memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 11d ago First seen · 86 lines · 31 tokens per session scan A 2dee495a4497
engagement-memory is a skill published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 24d ago), licensed MIT. It adds 31 tokens to every session and 1,359 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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