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 commands/redhatproductsecurity/agentic-threat-modeling/threat-model-quickgit 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/commands/redhatproductsecurity/agentic-threat-modeling/threat-model-quick)<a href="https://agentmods.dev/commands/redhatproductsecurity/agentic-threat-modeling/threat-model-quick"><img src="https://agentmods.dev/badge/commands/redhatproductsecurity/agentic-threat-modeling/threat-model-quick.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 | $0.00420 |
| Opus 5 | $0.00013 | $0.00210 |
| Sonnet 5 | $0.00005 | $0.00084 |
| Haiku 4.5 | $0.00003 | $0.00042 |
Grade A, and why
threat-model-quick 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 5d 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.
What it actually says
Invoke the threat-model skill in quick mode.
Run a full, non-interactive threat assessment — auto-picks all options so the user doesn't have to answer questions:
- If
context:arguments are provided, ingest them first — these are docs the code scan won't find (architecture write-ups, compliance assessments, prior threat models, SBOMs). Merge with code analysis: prefer external docs for architectural intent, prefer code for implementation reality, flag discrepancies as findings. - Auto-discover system context from code (no interview questions)
- Build a data classification table mapping each data type to sensitivity, storage, flows, and regulatory scope
- Apply STRIDE framework
- Ground every threat in code with
file:linecitations. For each threat, locate the specific file and line that makes it exploitable. For absence-of-control threats, note where the control should exist. - Auto-detect 2-3 most relevant threat actor personas
- Find as many threats as the codebase warrants — do not cap or limit the count
- Produce a full THREAT_MODEL.md following the report template, including all sections: executive summary, architecture overview, actor profiles, assets, data classification, attack surface, threats with narratives and code citations, mitigation roadmap, open questions, and provenance
This is the "autopilot" mode — same depth as guided, without the interview. Useful when you want a complete threat model without back-and-forth.
Optional arguments:
- A file or directory path to focus on (default: entire repo)
actors:insider,aptto override auto-detected actor personascontext:file,dir/,urlfor docs the code scan won't find (comma-separated; paths relative to repo root)
Arguments from user: $ARGUMENTS
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.
- 5d ago First seen · 26 lines · 25 tokens per session scan A 8ef0df478e12
threat-model-quick is a command published in the GitHub repository RedHatProductSecurity/agentic-threat-modeling (4 stars, last pushed 25d ago), licensed Apache-2.0. It adds 25 tokens to every session and 420 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.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.