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 briiirussell/cybersecurity-skills --skill threat-modelinggit clone --depth 1 https://github.com/briiirussell/cybersecurity-skillsWrote 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/briiirussell/cybersecurity-skills/threat-modeling)<a href="https://agentmods.dev/skills/briiirussell/cybersecurity-skills/threat-modeling"><img src="https://agentmods.dev/badge/skills/briiirussell/cybersecurity-skills/threat-modeling/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/briiirussell/cybersecurity-skills/threat-modeling"><img src="https://agentmods.dev/badge/skills/briiirussell/cybersecurity-skills/threat-modeling.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 84 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00111 | $0.02571 |
| Opus 5 | $0.00056 | $0.01286 |
| Sonnet 5 | $0.00022 | $0.00514 |
| Haiku 4.5 | $0.00011 | $0.00257 |
Grade A, and why
threat-modeling 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 12d 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 — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling — Pre-Implementation Security Design
Run a structured threat-modeling session against a proposed feature, system, or architecture. This is the design-time security skill — different from audit (which inspects code that exists). Use this when there's a design doc, a feature spec, an architecture diagram — but not yet code.
When to use:
- New feature touching auth, payments, multi-tenant data, or sensitive PII
- New external integration (third-party API, OAuth provider, webhook receiver)
- New service / microservice being added to the architecture
- Significant refactor of a security-sensitive component
- Before committing to a major architecture decision (event-driven vs request/response, monolith split, AI-feature introduction)
Cross-references: owasp-audit (code-level checklist that lines up with the threats this surfaces), api-audit (API-specific category mapping), iam-audit (identity decisions touch every threat model).
The four questions
Adam Shostack's framing — every threat model answers these four:
- What are we working on? (Scope and model)
- What can go wrong? (Threats)
- What are we going to do about it? (Mitigations)
- Did we do a good job? (Validation)
The rest of this skill walks through each in order.
Step 1 — What are we working on?
Produce a Data Flow Diagram (DFD) at one of three levels:
- Level 0 — Context diagram. One bubble for the system, lines to every external entity (users, third-party APIs, internal admin tools). Use this when the question is "what does this system even touch?"
- Level 1 — Major processes. Auth service, API gateway, primary data store, payment integration, etc. Use this for most feature-level threat models.
- Level 2 — Detailed component model. Specific endpoints, specific tables, specific queues. Use this for the trickiest parts only.
A useful DFD has:
- External entities (rectangles) — users, third-party services, admins
- Processes (circles) — your services, functions, handlers
- Data stores (parallel lines / cylinders) — databases, caches, blob storage, queues
- Data flows (arrows, labeled with what crosses) — request bodies, tokens, files, events
- Trust boundaries (dashed lines) — every crossing is a place data is validated, authenticated, or filtered
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.
- 12d ago First seen · 211 lines · 111 tokens per session scan A b0d403f72fc6
threat-modeling is a skill published in the GitHub repository briiirussell/cybersecurity-skills (391 stars, last pushed 3mo ago), licensed MIT. It adds 111 tokens to every session and 2,571 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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