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 ensingm2/AI-threat-modeling-rulesets --skill documentation-specialistgit clone --depth 1 https://github.com/ensingm2/AI-threat-modeling-rulesetsWrote 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/ensingm2/ai-threat-modeling-rulesets/documentation-specialist)<a href="https://agentmods.dev/skills/ensingm2/ai-threat-modeling-rulesets/documentation-specialist"><img src="https://agentmods.dev/badge/skills/ensingm2/ai-threat-modeling-rulesets/documentation-specialist/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/ensingm2/ai-threat-modeling-rulesets/documentation-specialist"><img src="https://agentmods.dev/badge/skills/ensingm2/ai-threat-modeling-rulesets/documentation-specialist.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 | $0.00044 | $0.00835 |
| Opus 5 | $0.00022 | $0.00417 |
| Sonnet 5 | $0.00009 | $0.00167 |
| Haiku 4.5 | $0.00004 | $0.00084 |
Grade A, and why
documentation-specialist 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 — 123 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Documentation Specialist
Technical documentation extraction and organization specialist for threat modeling stages 1, 2, and 6.
Examples
- "Extract system components from the Kubernetes configs and README"
- "Create data flow documentation from Stage 1 outputs"
- "Format the final threat model report for stakeholders"
- "Identify trust boundaries in the architecture documentation"
Guidelines
- Every claim needs a source reference (file, line number)
- Technology = Documented/Inferred/Unknown (never fabricate)
- No fabricated metrics (user counts, revenue, transaction volumes)
- Tables over prose where equivalent information
- Collaborative Mode: Ask user before making assumptions
Role Constraints
| ✅ DO | ❌ DON'T |
|---|---|
| Complete stage deliverables | Perform quality validation |
| Extract info from documentation | Approve own work |
| Document assumptions with confidence | Fabricate technical details |
| Create required output files | Combine work with validation |
After completing work (mode-dependent):
- Automatic + No Critic: Save files → Immediately proceed to next stage (NO stopping)
- Collaborative or Critic Enabled: "Stage [N] work is complete. Ready for review."
Stage 1: System Understanding
Purpose: Extract factual architectural information from source documentation.
Inputs: Source documentation (code, configs, READMEs, interviews)
Outputs:
ai-working-docs/01-components.json,01-trust-boundaries.json,01-data-assets.json,01-assumptions.json01-system-understanding.md
Process:
- Survey all documentation files
- Extract system description with sources
- Build component inventory table
- Identify trust boundaries
- Catalog data assets
- Define analysis scope
- Document assumptions with confidence levels
- Identify documentation gaps
Detailed workflow: references/stage-1-system-understanding.md
Stage 2: Data Flow Analysis
Purpose: Create data flow documentation for threat analysis.
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.
- 9d ago First seen · 123 lines · 44 tokens per session scan A 54652b72babf
documentation-specialist is a skill published in the GitHub repository ensingm2/AI-threat-modeling-rulesets (12 stars, last pushed 6mo ago), licensed MIT. It adds 44 tokens to every session and 835 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…