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 dayfinggg/openai-codex-agent-skills --skill supply-chain-risk-auditorgit clone --depth 1 https://github.com/dayfinggg/openai-codex-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/dayfinggg/openai-codex-agent-skills/supply-chain-risk-auditor)<a href="https://agentmods.dev/skills/dayfinggg/openai-codex-agent-skills/supply-chain-risk-auditor"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/supply-chain-risk-auditor/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/dayfinggg/openai-codex-agent-skills/supply-chain-risk-auditor"><img src="https://agentmods.dev/badge/skills/dayfinggg/openai-codex-agent-skills/supply-chain-risk-auditor.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 | $0.00031 | $0.01546 |
| Opus 5.5 | $0.00012 | $0.00618 |
| Sonnet 5.5 | $0.00006 | $0.00309 |
| Haiku 4.5 | $0.00003 | $0.00155 |
Grade A, and why
supply-chain-risk-auditor 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 today.
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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Read local host integration before applying this imported workflow.
Supply Chain Risk Auditor
Generates a supply-chain risk report for a project's direct dependencies (npm, PyPI, Go), plus an advisory sweep of everything its lockfile resolves. Two deterministic scripts do the measuring; your job is the judgment they refuse to automate.
Why the scripts do the measuring, not you
Every figure in this report is a claim about somebody else's project, and hand-collected
figures were measured wrong before this skill was rebuilt around scripts: GitHub
contributor counts said five-plus people maintain lodash where npm's ACL says one, and
gh saw zero downloads for a package that moves 164 million a week. Do not estimate
maintainer counts, downloads, staleness, or CVE history from gh, web search, or
memory — run the collector, and quote what it measured.
The scripts enforce two rules worth knowing before you read their output:
- Unavailable data is never evidence of risk. Every criterion resolves to assessed-clean, assessed-flagged, or unassessable-with-a-reason.
- An absent measurement is never a clean verdict. A run that measured nothing exits non-zero instead of printing a report that finds nothing.
Workflow
- Confirm the target directory has manifests:
package.json,pyproject.toml,requirements*.txt, orgo.mod. If none exist, say so and stop — do not audit an ecosystem this collector does not parse by hand. Lockfiles read for exact versions and the transitive sweep:package-lock.json/npm-shrinkwrap.json,uv.lock, and a go 1.17+go.mod.yarn.lock,pnpm-lock.yaml, andpoetry.lockare not read — the report says so when they are present, and versions fall back to pins or the latest release. - Check
gh auth status. Unauthenticated GitHub allows 60 requests/hour against 5,000, and the collector makes several per dependency; expect repository criteria to come back unassessable without it. Say so rather than fixing it silently. - Collect, then render. Put outputs somewhere outside the audited repository unless asked otherwise:
What ships with it
11 files 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.
- assets/trail-of-bits-mark.svg 3.0 KB
- scripts/collect.py 64 KB runs code
- scripts/model.py 17 KB runs code
- scripts/pyproject.toml 683 B
- scripts/render.py 34 KB runs code
- scripts/sources.py 29 KB runs code
- scripts/test_collect.py 37 KB runs code
- scripts/test_model.py 7.7 KB runs code
- scripts/test_render.py 14 KB runs code
- scripts/test_sources.py 10 KB runs code
- scripts/uv.lock 337 B
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.
- today First seen · 124 lines · 31 tokens per session scan A 1486401c4931
supply-chain-risk-auditor is a skill published in the GitHub repository dayfinggg/openai-codex-agent-skills (8 stars, last pushed today), licensed MIT. It adds 31 tokens to every session and 1,546 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-30.
Other skills, from other repositories
merge-pr
Merge a pull request with status checks, squash merge, and branch cleanup. Handles worktree contexts.
codex-autoresearch
Run repeated, measured Git experiments toward a numeric target; keep improvements and revert failures. Use for autonomous optimization or managing an autoresearch run, not one-shot edits.
bootstrap-prd
Set up PRD-driven development infrastructure for a new project, including directory structure, templates, and roadmap.
vibe
Delegate a coding task to a cheap AI model (Mistral Vibe by default, but any provider Vibe knows about — DeepSeek, Gemini Flash, etc.) and supervise the result via git diff. Claude orchestrates, the cheap model codes. Claude consumes 500-1500 tokens per delegation regardless of how many file reads the delegate does…
code-that-fits-in-your-head
Software-engineering heuristics based on Mark Seemann's Code That Fits in Your Head (2021), updated for agent-driven development. Use when writing or reviewing code, refactoring accidental complexity or a Big Ball of Mud, controlling technical or architectural debt in generated code, designing APIs and invariants…
verify
Fact-check claims encountered during reading — dates, names, events, citations. Use when encountering historical facts or disputed claims.