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.
git 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-feedback)<a href="https://agentmods.dev/commands/redhatproductsecurity/agentic-threat-modeling/threat-model-feedback"><img src="https://agentmods.dev/badge/commands/redhatproductsecurity/agentic-threat-modeling/threat-model-feedback.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 | $0.00014 | $0.00715 |
| Opus 5 | $0.00007 | $0.00358 |
| Sonnet 5 | $0.00003 | $0.00143 |
| Haiku 4.5 | $0.00001 | $0.00072 |
Grade A, and why
threat-model-feedback 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 7d 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
Collect feedback about the most recent threat model and file it as a GitHub issue using the Feedback issue template.
Ask the user these questions one at a time:
-
How useful was this threat model? Options: Very useful / Somewhat useful / Not useful
-
Which section was most valuable? Options: Executive summary / Architecture overview / Threats / Attack scenarios / Mitigations / Data classification / Other
-
Was anything missing or wrong? (free text, or "nothing")
-
Were there any false positives — threats that don't actually apply? (free text, or "none")
-
On a scale of 1-5, how would you rate the overall quality? (1 = poor, 5 = excellent)
-
Would you use this tool again? Options: Yes / Maybe / No
-
Any additional comments? (free text, or skip)
Filing the Issue
After collecting answers, try these in order — a filed issue is more useful than a link the user might not click, so only fall back to a link if neither tool is available.
1. GitHub CLI (gh), if installed and authenticated:
Check with gh auth status. If authenticated, create the issue directly:
gh issue create \
--repo RedHatProductSecurity/agentic-threat-modeling \
--title "[Feedback] <one-line summary from the usefulness + rating answers>" \
--label feedback \
--body "$(cat <<'EOF'
**How useful was this threat model?** {usefulness}
**Which section was most valuable?** {most_valuable}
**Was anything missing or wrong?** {missing}
**Were there any false positives?** {false_positives}
**Overall quality rating (1-5):** {rating}
**Would you use this tool again?** {use_again}
**Additional comments:** {comments}
EOF
)"
Share the issue URL that gh issue create returns.
2. A connected GitHub MCP server, if gh isn't installed or isn't authenticated:
If an MCP tool for creating GitHub issues is available in this session, use it against the same repository (RedHatProductSecurity/agentic-threat-modeling) with the same title, labels, and body as above.
3. Fallback: a pre-filled link, if neither of the above is available: Construct a pre-filled "new issue" URL against the Feedback issue form, URL-encoding each answer into its field:
https://github.com/RedHatProductSecurity/agentic-threat-modeling/issues/new?template=feedback.yml&title=%5BFeedback%5D&usefulness={usefulness}&most-valuable={most_valuable}&missing={missing}&false-positives={false_positives}&rating={rating}&use-again={use_again}&comments={comments}
Present it as:
Thanks for the feedback! Click here to file it: Submit Feedback
The issue will open pre-filled with your answers on GitHub. Just review and click "Submit new issue."
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.
- 7d ago First seen · 71 lines · 14 tokens per session scan A cd8b47a72b07
threat-model-feedback is a command published in the GitHub repository RedHatProductSecurity/agentic-threat-modeling (4 stars, last pushed 27d ago), licensed Apache-2.0. It adds 14 tokens to every session and 715 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.