Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/hypnguyen1209/offensive-claudenpx agentmods add commands/hypnguyen1209/offensive-claude/engage.memoryWrote 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/commands/hypnguyen1209/offensive-claude/engage.memory)<a href="https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.memory"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.memory.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00015 | $0.00379 |
| Opus 5 | $0.00008 | $0.00189 |
| Sonnet 5 | $0.00003 | $0.00076 |
| Haiku 4.5 | $0.00002 | $0.00038 |
Grade A, and why
engage.memory scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
/engage.memory
Manage the cross-engagement learning store (skills/engagement-memory). Patterns are ranked by
impact (CVSS/severity) and recalled as an explicit top-N query.
Usage
/engage.memory <recall|record|gc|stats> [options]
Subcommands
recall
Pull the top prior patterns for the current target's class / tech stack and write them to
.engage/recon/prior-intel.md so weaponization starts from what already worked.
python skills/engagement-memory/scripts/pattern_db.py match \
--vuln-class <class> --tech-stack <a,b> --target <host> --top 10 --json
record
Persist a [CONFIRMED] finding (run after /engage.report / validate_findings.py). Only confirmed
findings are recorded; [POSSIBLE]/[REJECTED] are not learned.
python skills/engagement-memory/scripts/pattern_db.py record --json '<finding json>'
gc
Compact the pattern DB (dedup-merge, knowledge preserved) and rotate the disposable audit log.
python skills/engagement-memory/scripts/pattern_db.py compact
stats
Show pattern counts by vulnerability class.
Notes
- Storage:
~/.claude/engagement-memory/patterns.jsonl(override$ENGAGEMENT_DB; use a per-client DB if ROE requires client isolation). - Recall is generic-by-class/stack; review before reusing across clients.
- Records hold technique + CWE/CVSS + an evidence reference, never raw loot.
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.
- 8d ago First seen · 49 lines · 15 tokens per session scan A b4b1394475be
engage.memory is a command published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 21d ago), licensed MIT. It adds 15 tokens to every session and 379 once invoked, about $0.0001 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.
Other commands, from other repositories
learn
Manually run the continuous-learner. Extract patterns from this session and write to .greatcto/lessons.md. Use when SessionEnd hook missed something or you want to capture a lesson mid-session.
ccr
CCR (Compressed Context with Retrieval) — recall the full original of context that greatcto compressed/filtered out, by its short id. The retrieval half of the compression layer.
ensoul
Activate Claudicle soul identity in this session. Creates a per-session marker so soul.md, soul state, and session awareness persist through compaction and resume.
tag
Attach manual keyword metatags to the current Claude Code session. These tags supplement the auto-generated Qwen tags in /.claude/session-registry.json and are searchable via /claude-tracker-search --name (and, after the Phase 2 default-mode pre-pass, via the bare /claude-tracker-search ).
quote-search
Search notable phrases captured via /quote or auto-extracted from sessions. Uses FTS5 full-text search or tag-based filtering.
rust-teach
One-time setup that scans your Rust project, understands its patterns and conventions, and writes a Rust-specific context section to your CLAUDE.md. Run once per project to establish persistent Rust guidelines.