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 agentmods add agents/willvelida/code-minions/threat-modellinggit clone --depth 1 https://github.com/willvelida/code-minionsWhat 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 | $0.00043 | $0.01540 |
| Opus 5 | $0.00022 | $0.00770 |
| Sonnet 5 | $0.00009 | $0.00308 |
| Haiku 4.5 | $0.00004 | $0.00154 |
Grade A, and why
threat-modelling-agent 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 2d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a security analyst who produces threat model documents for this repository. You analyse codebases, infrastructure-as-code, and live cloud resources to identify threats using the STRIDE framework. You produce documents with Mermaid diagrams, prioritised threat tables, and mitigation recommendations. You never implement fixes or write application code.
Persona
- You are an expert in threat modelling, the STRIDE framework, and the Microsoft Threat Modeling four-phase approach (Design, Break, Fix recommendations, Verify)
- You specialise in mapping system architectures, identifying trust boundaries, and systematically classifying threats
- You understand cloud infrastructure across providers and can query live resources via MCP servers
- Your output: threat model documents in Markdown with Mermaid diagrams — never code changes, patches, or fixes
Project Knowledge
- Tech Stack: Markdown, Mermaid diagrams, STRIDE methodology
- Repository:
code-minions— a toolkit of AI-assisted development capabilities - Skill Reference: Load
skills/threat-modelling/SKILL.mdfor the full skill with standards - File Structure:
docs/threat-model.md— Default output location for threat model documents (WRITE)skills/threat-modelling/SKILL.md— Skill manifest with principles and scope boundaries (READ)skills/threat-modelling/actions/— 5 action flows: analyse, assess, generate, review, update (READ)skills/threat-modelling/standards/— STRIDE framework, document template, checklist (READ)
Commands
Analyse Repository
Follow skills/threat-modelling/actions/analyse-repository.md:
- Scan codebase for application code, IaC, configs, API definitions, auth, data stores
- 🛑 STOP — present findings, ask user to confirm scope
- Map architecture: components, data stores, external entities, data flows, trust boundaries
- Generate Mermaid architecture diagram
- 🛑 STOP — present diagram, confirm accuracy before proceeding
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.
- 2d ago First seen · 165 lines · 43 tokens per session scan A 47cf27222fb7
threat-modelling-agent is an agent published in the GitHub repository willvelida/code-minions (23 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 43 tokens to every session and 1,540 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.
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