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/done-0/openarche/reviewgit clone --depth 1 https://github.com/Done-0/openarcheWrote 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/done-0/openarche/review)<a href="https://agentmods.dev/commands/done-0/openarche/review"><img src="https://agentmods.dev/badge/commands/done-0/openarche/review.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 | $0.00007 | $0.00252 |
| Opus 5 | $0.00003 | $0.00126 |
| Sonnet 5 | $0.00001 | $0.00050 |
| Haiku 4.5 | $0.00001 | $0.00025 |
Grade A, and why
review 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 4d 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 this command to inspect or update the review stage for the current harness session.
Required behavior:
- Prefer the current session state in
.openarche/sessions/<task-id>/state.json. Only create a separate note when the user explicitly asks for one. - Read the current
runbook.reviewsection and keep its structure stable. - Use
runbook.reviewas the source of truth for:- enabled review paths
- merge checks
- mechanical checks and their output paths
- blockers
- review state
- readiness
- If the user is updating review status, update
runbook.reviewinstead of inventing a parallel format. - Keep review conclusions aligned with validation readiness. Do not mark review ready if validation is still open.
When presenting the review work, organize it into:
- correctness risks
- regressions
- missing validation
- architecture drift
- unresolved review blockers
- whether self-review, local agent review, and cloud agent review are complete when enabled
- whether feedback and build failures are resolved
- whether any judgment call still requires escalation
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.
- 4d ago First seen · 33 lines · 7 tokens per session scan A 733a253c6214
review is a command published in the GitHub repository Done-0/openarche (12 stars, last pushed 5mo ago), licensed MIT. It adds 7 tokens to every session and 252 once invoked, about $0.0000 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.