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 martinffx/atelier --skill atelier-orchestratorgit clone --depth 1 https://github.com/martinffx/atelierWrote 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/martinffx/atelier/atelier-orchestrator)<a href="https://agentmods.dev/skills/martinffx/atelier/atelier-orchestrator"><img src="https://agentmods.dev/badge/skills/martinffx/atelier/atelier-orchestrator/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/martinffx/atelier/atelier-orchestrator"><img src="https://agentmods.dev/badge/skills/martinffx/atelier/atelier-orchestrator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00052 | $0.01837 |
| Opus 5 | $0.00026 | $0.00919 |
| Sonnet 5 | $0.00010 | $0.00367 |
| Haiku 4.5 | $0.00005 | $0.00184 |
Grade A, and why
atelier-orchestrator 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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 204 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IF A SKILL APPLIES TO YOUR TASK, YOU DO NOT HAVE A CHOICE. YOU MUST USE IT.
This is not negotiable. This is not optional. You cannot rationalize your way out of this.
Planning Workflow
You have skills. You MUST use them. Not "should." Not "when convenient." MUST.
Invoke relevant skills BEFORE any response or action. Even a 1% chance a skill might apply means you invoke it. If an invoked skill turns out to be wrong for the situation, you don't need to follow it. But you must check.
The One Rule
When an implementation choice exists, never write code until the human has reviewed and approved a written plan. Exact mechanical changes may use the exception below.
The plan may be inline in the conversation or backed by persisted spec artifacts. Select the mode before invoking an artifact-producing skill, state the choice with one brief reason, and let the human override it.
Governing Principles
Invoking a skill selects relevant guidance. It does not make every pattern in every loaded skill part of the solution. Existing source, explicit requirements, and the smallest correct change determine which patterns apply.
Begin with the current design and the minimum behavior-preserving change. Product, infrastructure, and architectural expansion require explicit scope. Keep ponytail active throughout development when it is available or required by the repository. Keep writing-clearly-and-concisely active whenever writing or editing prose for humans. Before returning or persisting that prose, apply humanizer in embedded mode.
Planning Modes
Inline Plan (default)
Use for bounded, well-understood work, including ordinary features, bug fixes, refactors,
configuration changes, and multi-file changes. Route to spec-plan with Inline mode
explicitly selected. It presents a concise plan in conversation, creates no planning artifacts
or tracker entries, and stops for approval. Approval ends the spec-plan invocation. A later,
explicit implementation request may implement the plan directly; do not invoke
spec-implement, spec-finish, or code-subagents for Inline work.
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.
- 10d ago First seen · 204 lines · 52 tokens per session scan A 517fe0b0d183
atelier-orchestrator is a skill published in the GitHub repository martinffx/atelier (46 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 1,837 once invoked, about $0.0003 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 skills, from other repositories
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review-work
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tech-debt-audit
Thorough, file-cited technical debt audit across 9 dimensions using AST-grep (tree-sitter), grep, LSP, and language-native tooling. Produces TECHDEBTAUDIT.md with severity, effort estimates, and prioritized fixes. Use when asked for codebase health check, tech debt audit, architecture review, code quality assessment…
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link-check
Verify @file references in AIWG skills and agents against the linking contract — per-file or corpus-wide, with optional auto-fix.
checkpoint
Create, list, or recover mid-workflow checkpoints so interrupted work resumes from a known-good position.