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 h4vzz/awesome-ai-agent-skills --skill threat-modelinggit clone --depth 1 https://github.com/h4vzz/awesome-ai-agent-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/h4vzz/awesome-ai-agent-skills/threat-modeling)<a href="https://agentmods.dev/skills/h4vzz/awesome-ai-agent-skills/threat-modeling"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-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/h4vzz/awesome-ai-agent-skills/threat-modeling"><img src="https://agentmods.dev/badge/skills/h4vzz/awesome-ai-agent-skills/threat-modeling.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 | $0.00029 | $0.02509 |
| Opus 5 | $0.00015 | $0.01255 |
| Sonnet 5 | $0.00006 | $0.00502 |
| Haiku 4.5 | $0.00003 | $0.00251 |
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 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.
This is a copy
92% identical to threat-modeling — 9 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 120 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Modeling
This skill enables the agent to perform structured threat modeling for software applications, APIs, and infrastructure. The agent analyzes system architecture, data flows, and trust boundaries to systematically identify potential security threats using established methodologies such as STRIDE, DREAD, PASTA, and attack trees. The output is a prioritized threat register with specific, actionable mitigation strategies that development teams can integrate into their backlog.
Workflow
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Decompose the System Architecture — Analyze architecture diagrams, code repositories, infrastructure-as-code files, and deployment configurations to identify all components, data stores, external services, and communication channels. Map trust boundaries between networks, services, and user privilege levels. Produce a data flow diagram (DFD) showing how data moves through the system.
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Select a Threat Modeling Methodology — Choose the appropriate methodology based on the project's needs. Use STRIDE for systematic enumeration of threat categories per component. Use DREAD for scoring and prioritizing known threats. Use PASTA (Process for Attack Simulation and Threat Analysis) for risk-centric analysis aligned with business objectives. Use attack trees for deep analysis of specific high-value targets like authentication or payment systems.
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Enumerate Threats — Apply the selected methodology to each component and data flow in the DFD. For STRIDE, evaluate each element against all six threat categories: Spoofing, Tampering, Repudiation, Information Disclosure, Denial of Service, and Elevation of Privilege. Document each threat with a unique identifier, description, affected component, and the trust boundary it crosses.
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Assess Risk and Prioritize — Score each threat using DREAD (Damage, Reproducibility, Exploitability, Affected Users, Discoverability) or a similar quantitative framework. Combine the score with business context — a threat to the payment service is higher priority than the same threat to an internal admin dashboard. Produce a ranked threat register.
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 · 120 lines · 29 tokens per session scan A a952b210be9d
threat-modeling is a skill published in the GitHub repository h4vzz/awesome-ai-agent-skills (34 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 2,509 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 92% identical to threat-modeling, differing in 9 lines, and is treated as a copy.
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