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/Lab94/frenchie-skillWrote 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/lab94/frenchie-skill/extract)<a href="https://agentmods.dev/commands/lab94/frenchie-skill/extract"><img src="https://agentmods.dev/badge/commands/lab94/frenchie-skill/extract/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/commands/lab94/frenchie-skill/extract"><img src="https://agentmods.dev/badge/commands/lab94/frenchie-skill/extract.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 | $0.00021 | $0.00080 |
| Opus 5.5 | $0.00008 | $0.00032 |
| Sonnet 5.5 | $0.00004 | $0.00016 |
| Haiku 4.5 | $0.00002 | $0.00008 |
Grade A, and why
extract 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 1mo 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
Use the extract_to_markdown MCP tool for $ARGUMENTS.
If the MCP server is HTTP, upload first with upload_file and pass uploaded_file_reference.
If the MCP server is stdio, pass the absolute local path as file_path.
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.
- 1mo ago First seen · 9 lines · 21 tokens per session scan A ecac4867df3b
extract is a command published in the GitHub repository Lab94/frenchie-skill (0 stars, last pushed 5mo ago), licensed MIT. It adds 21 tokens to every session and 80 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 2026-08-31.
Other commands, from other repositories
brand-generate
Generate an on-brand document from a saved Brand Profile.
cti-report
Render case deliverables — relationship graph (PNG/SVG/Mermaid) and a polished PDF/DOCX assessment. Usage: /cti-report [--graph|--pdf].
render-figures
Compile all .tex and .typ figure files in a directory.
ppt-image2-editable-rebuild
Rebuild image2 or imagegen reference slides as editable PowerPoint decks.
publish-api-docs
Generate interactive API documentation from blueprint specs (Swagger UI, Redoc, AsyncAPI).
write-patent
An automated workflow for creating a complete Chinese patent application from a technical disclosure document, including written sections, patent drawings, and a Word file.