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 alpha-omega-security/threat-model --skill threat-model-backtestgit clone --depth 1 https://github.com/alpha-omega-security/threat-modelWrote 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/alpha-omega-security/threat-model/threat-model-backtest)<a href="https://agentmods.dev/skills/alpha-omega-security/threat-model/threat-model-backtest"><img src="https://agentmods.dev/badge/skills/alpha-omega-security/threat-model/threat-model-backtest/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/alpha-omega-security/threat-model/threat-model-backtest"><img src="https://agentmods.dev/badge/skills/alpha-omega-security/threat-model/threat-model-backtest.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 | $0.00124 | $0.02316 |
| Opus 5 | $0.00062 | $0.01158 |
| Sonnet 5 | $0.00025 | $0.00463 |
| Haiku 4.5 | $0.00012 | $0.00232 |
Grade A, and why
threat-model-backtest 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 9d 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 — 154 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Model — Backtest (validate against history)
Phase 3.6. A draft that has never been backtested is untested software. The project's own history is a free test suite; use it before presenting the draft for sign-off. This is a producer-side quality gate — the corpus does not go into the published document (per the leave-out list: CVE history is not the threat model).
Procedure
- Assemble a stratified corpus — start with the last 10–30 inbound security
findings: published advisories, reports closed as "not a bug" / "by design",
issues labeled
security, and scanner/fuzzer/AI-analysis output. If a vendoredsecurity-context.mdis present in the working directory (a runner's pre-fetch of exactly this material), seed the corpus from it before searching elsewhere. Prefer contested items, but do not let recency or controversy leave component families or contract dimensions untested.- Cover every in-scope component family.
- Cover every applicable contract-dimension row: numeric limits, failure atomicity, topology, callbacks, serialization, lifecycle, concurrency, and resource complexity.
- For a large corpus, cluster by
(component, sink, attack class, required attacker capability)and route at least one representative from every cluster. Increase the corpus beyond 30 when necessary to avoid an untested cluster; report both item count and cluster count. - Record each item's actual historical outcome where one exists —
fixed,wontfix,by-design,out-of-scope, orunknown— with the advisory or issue URL it came from. That label is the ground truth step 3 scores against, and without it the backtest cannot fail. Set the outcome aside while routing (step 2 is blind); compare only afterwards. - When no historical record is reachable, say so rather than inventing
one. Synthesize cases to exercise the matrix, mark every one
synthesized, and write the §1.1 note verbatim: "no historical corpus was available; the backtest routed N synthesized cases only." A self-invented corpus reported as history is worse than no backtest, because it reads as evidence.
- Route each item blind — using only the draft (not hindsight knowledge of how it was actually resolved), apply the §1.1 triager quick-start and assign exactly one §1.17 disposition, citing the licensing section. Routing rules and the closed disposition set are in output-structure.md.
- Score the routing. The two directions of error are not symmetric. Wrongly closing a real vulnerability is far worse than wrongly escalating a non-finding: an over-escalating model wastes maintainer time, an over-closing one hands a reporter a "not a bug" on a live issue. Score accordingly — this asymmetry decides every fix below.
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.
- 9d ago First seen · 154 lines · 124 tokens per session scan A 5087e5bd4140
threat-model-backtest is a skill published in the GitHub repository alpha-omega-security/threat-model (54 stars, last pushed 21d ago), licensed MIT. It adds 124 tokens to every session and 2,316 once invoked, about $0.0006 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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