Borrowing it
Nothing to install: this file belongs to d-o-hub/github-template-ai-agents. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/d-o-hub/github-template-ai-agents/main/.gemini/commands/review.tomlgit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote 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/d-o-hub/github-template-ai-agents/review)<a href="https://agentmods.dev/commands/d-o-hub/github-template-ai-agents/review"><img src="https://agentmods.dev/badge/commands/d-o-hub/github-template-ai-agents/review/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/d-o-hub/github-template-ai-agents/review"><img src="https://agentmods.dev/badge/commands/d-o-hub/github-template-ai-agents/review.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 | $0.00000 | $0.00171 |
| Opus 5.5 | $0.00000 | $0.00068 |
| Sonnet 5.5 | $0.00000 | $0.00034 |
| Haiku 4.5 | $0.00000 | $0.00017 |
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 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
description = "Conduct a five-axis code review — correctness, readability, architecture, security, performance" prompt = """ Invoke the code-review-assistant skill. Review the current changes (staged or recent commits) across all five axes:
- Correctness — Does it match the spec? Edge cases handled? Tests adequate?
- Readability — Clear names? Straightforward logic? Well-organized?
- Architecture — Follows existing patterns? Clean boundaries? Right abstraction level?
- Security — Input validated? Secrets safe? Auth checked? (Use security-code-auditor skill)
- Performance — No N+1 queries? No unbounded ops? Categorize findings as Critical, Important, or Suggestion. Output a structured review with specific file:line references and fix recommendations. """
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 · 13 lines · 0 tokens per session scan A 1525a9ab3579
review is a command published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 171 tokens. 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 2026-09-03.
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check-architecture
Angular Architecture Review.
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ia-verify
Run pre-PR verification chain: build, types, lint, tests, security scan, diff review.
announce
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ia-agent-native-audit
Score each of the 5 agent-native principles (parity, granularity, composability, emergent capability, improvement-over-time) against a codebase and report gaps.