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/starslingdev/skills/registry-securitygit clone --depth 1 https://github.com/starslingdev/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/commands/starslingdev/skills/registry-security)<a href="https://agentmods.dev/commands/starslingdev/skills/registry-security"><img src="https://agentmods.dev/badge/commands/starslingdev/skills/registry-security.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 | $0.00023 | $0.00364 |
| Opus 5 | $0.00012 | $0.00182 |
| Sonnet 5 | $0.00005 | $0.00073 |
| Haiku 4.5 | $0.00002 | $0.00036 |
Grade A, and why
registry-security 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 3d 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
Run the maintainer-only skills-registry-security skill against $ARGUMENTS
(default starslingdev/skills/ci-secure if no target is given).
Read maintainers/skills-registry-security/SKILL.md and follow it. In short:
-
Run the audit and read the
NEXT ACTIONverdict:python3 maintainers/skills-registry-security/scripts/registry_audit.py <owner/repo/skill> --repo-root .Add
--no-installfor the ~3s check instead of ~40s. -
Act on the verdict:
RESOLVED— the badge is clean. Report and stop.ACTION_REQUIRED— a cited literal is still in HEAD. It is a real finding; surface it with the code and stop. Do not fix silently.MONITOR— the finding is stale, there is nothing to patch, and the registry's own ~daily re-audit is what clears it. Arm the unattended watch per SKILL.md's "Drive it to green unattended" section and report only on a decision change.DISAGREEMENT/UNVERIFIED— surfaces disagree or could not be classified. Re-check before trusting; never report a badge as clean.
Rails from SKILL.md that always apply: never rename or re-slug a published skill, never auto-file an issue on the registry's repo, and keep any research subagents read-only.
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.
- 3d ago First seen · 33 lines · 23 tokens per session scan A 8041d7222e73
registry-security is a command published in the GitHub repository starslingdev/skills (19 stars, last pushed 7d ago), licensed MIT. It adds 23 tokens to every session and 364 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 commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
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.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
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.