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 woohyun212/security-skill --skill threat-modelgit clone --depth 1 https://github.com/woohyun212/security-skillWrote 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/woohyun212/security-skill/threat-model)<a href="https://agentmods.dev/skills/woohyun212/security-skill/threat-model"><img src="https://agentmods.dev/badge/skills/woohyun212/security-skill/threat-model.svg" alt="Measured on agentmods" 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 | $0.00025 | $0.01322 |
| Opus 5 | $0.00013 | $0.00661 |
| Sonnet 5 | $0.00005 | $0.00264 |
| Haiku 4.5 | $0.00003 | $0.00132 |
Grade A, and why
threat-model 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 8d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What this skill does
Guides a structured threat modeling exercise for a target system using industry-standard frameworks: STRIDE (threat categorization), DREAD (risk scoring), PASTA (process-driven methodology), and Attack Trees (attack decomposition). Produces a threat catalog with MITRE ATT&CK mappings, a risk matrix, and a prioritized mitigation roadmap.
When to use
- During system design or architecture review to identify security risks before implementation
- When preparing for a security audit or compliance assessment that requires documented threat analysis
- When onboarding a new system into a security program and baseline threat coverage is needed
- When a significant feature or infrastructure change warrants re-evaluation of the attack surface
Prerequisites
- No external tools required (methodology/checklist-based)
- Access to system architecture diagrams, data flow diagrams (DFDs), or equivalent documentation
- Knowledge of the system's technology stack, trust boundaries, and data sensitivity levels
- (Optional) MITRE ATT&CK reference: https://attack.mitre.org
Inputs
| Item | Description | Example |
|---|---|---|
SYSTEM_NAME |
Name of the system under review | Payment API v3 |
SYSTEM_TYPE |
Type of system | web-app / api / microservice / infrastructure |
TECH_STACK |
Technology stack | Node.js, PostgreSQL, Redis, AWS ECS |
DATA_SENSITIVITY |
Highest data classification in scope | PII / PCI / internal / public |
REVIEW_SCOPE |
Boundaries of the review | All external-facing endpoints and auth flows |
Workflow
Step 1: System Decomposition
Identify what needs protecting before identifying threats. Collect the following information about the target system.
Reference: See REFERENCE.md for asset inventory, trust boundaries, and entry points markdown templates.
Data Flow Diagram (DFD) Summary
Describe the primary data flows as a numbered list if a visual DFD is unavailable:
What ships with it
1 file 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.
- 8d ago First seen · 110 lines · 25 tokens per session scan A f8c8ae64cb89
threat-model is a skill published in the GitHub repository woohyun212/security-skill (21 stars, last pushed 4mo ago), licensed MIT. It adds 25 tokens to every session and 1,322 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 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…
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…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…