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.
git 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/commands/hypnguyen1209/offensive-claude/engage.threatmodel)<a href="https://agentmods.dev/commands/hypnguyen1209/offensive-claude/engage.threatmodel"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.threatmodel/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/commands/hypnguyen1209/offensive-claude/engage.threatmodel"><img src="https://agentmods.dev/badge/commands/hypnguyen1209/offensive-claude/engage.threatmodel.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00012 | $0.00394 |
| Opus 5 | $0.00006 | $0.00197 |
| Sonnet 5 | $0.00002 | $0.00079 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
engage.threatmodel 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 10d 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.threatmodel
Builds the threat model from recon-osint output, lints it for completeness, and diffs a re-run
against the reviewed baseline to catch new unreviewed attack surface (drift). Backed by
skills/threat-model-discipline/ + scripts/threatmodel_lint.py; the drift check hooks into
/engage.gate.
Usage
/engage.threatmodel {materialize|lint|drift} ...
Process
- Materialize — from recon-osint output, fill
templates/threat-model/threat-model.mdand its machine-readablethreat-model.json(assets, entry_points, trust_boundaries, attck, mitigations). - Lint —
threatmodel_lint.py lint .engage/recon/threat-model.json: every required field present, noTBD/[fill in]placeholders, valid ATT&CK ids. Exit 1 if incomplete. - Baseline — after review, save
threat-model.baseline.json. - Drift — re-run recon → regenerate
threat-model.json→threatmodel_lint.py drift baseline.json threat-model.json. A NEW entry point / asset / trust boundary is unreviewed surface and blocks the gate (exit 1) until re-reviewed or explicitly acknowledged. Removed surface is recorded but does not block.
Notes
attckchanges are not surface drift (they're coverage, not new surface) — only entry_points/assets/trust_boundaries additions block.- Pairs with
scope-discipline(what's in bounds) andfinding-discipline(what counts as proven).
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.
- 10d ago First seen · 32 lines · 12 tokens per session scan A 8a47413a78db
engage.threatmodel is a command published in the GitHub repository hypnguyen1209/offensive-claude (357 stars, last pushed 23d ago), licensed MIT. It adds 12 tokens to every session and 394 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
discover
Run a full product discovery cycle — from outcome definition through opportunity mapping, prioritisation, and experiment design. Use when the team isn't sure what to build next, or before writing a PRD for a complex feature space.
voice-compliance
Voice/telephony compliance check — invokes voice-ai-reviewer to produce TM-voice-{slug}.md with TCPA, STIR/SHAKEN, state recording-consent, EU AI Act Art. 50, and synth-voice deepfake-law gaps.
claude-tracker
List recent Claude Code sessions with live status.
qa
Smoke or browser-walk a running app. Report only. Do not implement. Do not merge.
esp-harden
Harden and inspect ESP32 firmware for field failures, crashes, memory, and security.
replication-package
Scaffold or audit a social-science replication package at a target directory, and audit the manuscript and its archived research objects against FAIR principles.