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/tunahanaliozturk/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/commands/tunahanaliozturk/atelier/crew)<a href="https://agentmods.dev/commands/tunahanaliozturk/atelier/crew"><img src="https://agentmods.dev/badge/commands/tunahanaliozturk/atelier/crew/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/tunahanaliozturk/atelier/crew"><img src="https://agentmods.dev/badge/commands/tunahanaliozturk/atelier/crew.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 | $0.00020 | $0.00184 |
| Opus 5 | $0.00010 | $0.00092 |
| Sonnet 5 | $0.00004 | $0.00037 |
| Haiku 4.5 | $0.00002 | $0.00018 |
Grade A, and why
crew 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 9d 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
Read the agent registry from agents/ with readRegistry (lib/registry.mjs), or use the
registry the SessionStart hook put in context. Build a coverage report with coverageReport
(lib/coverage.mjs) and render:
- Each populated layer with its agents, their capabilities, and the union of task kinds.
- A "Coverage" line: which stack layers are populated.
- A "Gaps" line: stack layers (
backend, frontend, infra, data, docs, qa, mobile, ml) with no agent, or "no gaps". When there is a gap, note that the lead scaffolds an agent with the writing-agents skill.
formatRegistry gives a quick flat view if you want one.
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.
- 9d ago First seen · 16 lines · 20 tokens per session scan A c49d976736e4
crew is a command published in the GitHub repository tunahanaliozturk/atelier (1 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 184 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
bug
Reproduce then TDD-fix a ready-for-agent bug ticket in this checkout. Web bugs get a browser repro first.
bob-work
Drain the board as a worker — claim pending tasks Claude can do, execute them, submit results.
decree
Issue a decree. Seats the organization on first use, then dispatches the teams.
bob-new
Provision a well-formed task onto the Bob board from a rough description.
report
The full account of the current decree — every figure verified before stated.
assess
Run an AI literacy assessment — scan the repo for evidence, ask clarifying questions, produce a timestamped assessment document, apply immediate habitat fixes, recommend workflow changes, capture a reflection, and add a literacy level badge to the README.