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 RedHatProductSecurity/agentic-threat-modeling --skill threat-model-reportgit clone --depth 1 https://github.com/RedHatProductSecurity/agentic-threat-modelingWrote 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/redhatproductsecurity/agentic-threat-modeling/threat-model-report)<a href="https://agentmods.dev/skills/redhatproductsecurity/agentic-threat-modeling/threat-model-report"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/agentic-threat-modeling/threat-model-report/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/redhatproductsecurity/agentic-threat-modeling/threat-model-report"><img src="https://agentmods.dev/badge/skills/redhatproductsecurity/agentic-threat-modeling/threat-model-report.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 | $0.00024 | $0.02037 |
| Opus 5 | $0.00012 | $0.01019 |
| Sonnet 5 | $0.00005 | $0.00407 |
| Haiku 4.5 | $0.00002 | $0.00204 |
Grade A, and why
threat-model-report 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 — 156 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Threat Model Report Generator
You are producing the final threat model deliverable. You receive structured findings from the analysis phase and transform them into a report that tells the story of this system's threat landscape.
Core Principle: Tell the Story
This is not a data dump. Every section should be readable by someone encountering this system for the first time. The report should answer these questions in order:
- What is this system and why does it matter?
- Who wants to attack it and why?
- What are the most dangerous things that could happen?
- What should we do about it, and in what order?
Report Generation
Load the Template
Read ./templates/threat-model-template.md for the full section structure and writing guidelines. Follow it closely — it defines the output contract.
Write Each Section
Executive Summary: Write this LAST (after all other sections are complete) but place it FIRST in the document. Distill the entire analysis into 2-3 paragraphs. Name the top 3 threats in plain language. State the single most important action. Give an honest overall risk assessment.
Do not use security jargon in the executive summary. "An attacker could steal customer payment details through a flaw in the search feature" — not "STRIDE-identified information disclosure vulnerability via SQL injection in the user search endpoint."
Architecture Overview: Describe the system so someone could sketch it on a whiteboard. Use clear component names and describe how data actually flows — "When a user logs in, the React frontend sends credentials to the API gateway, which forwards to the auth service, which checks against the PostgreSQL user database and returns a JWT."
Threat Actor Landscape: For each relevant actor, write a paragraph explaining why they'd target THIS system specifically. Connect it to what the system stores, processes, or exposes. "This system processes payment card data for 50,000 transactions per month, making it a high-value target for organized crime groups who monetize stolen card data through dark web marketplaces."
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.
- 9d ago First seen · 156 lines · 24 tokens per session scan A 4ccc2dc95be9
threat-model-report is a skill published in the GitHub repository RedHatProductSecurity/agentic-threat-modeling (4 stars, last pushed 29d ago), licensed Apache-2.0. It adds 24 tokens to every session and 2,037 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 2026-08-31.
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