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/orlando-japan/claude-code-setting/reviewgit clone --depth 1 https://github.com/orlando-japan/claude-code-settingWrote 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/orlando-japan/claude-code-setting/review)<a href="https://agentmods.dev/commands/orlando-japan/claude-code-setting/review"><img src="https://agentmods.dev/badge/commands/orlando-japan/claude-code-setting/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 5.1 | $0.00014 | $0.00385 |
| Opus 5 | $0.00007 | $0.00192 |
| Sonnet 5 | $0.00003 | $0.00077 |
| Haiku 4.5 | $0.00001 | $0.00038 |
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 5d 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
Review the target: $ARGUMENTS
- If the argument is a file path, review that file.
- If the argument is
stagedor empty, reviewgit diff --staged. - If the argument looks like a PR number, use
gh pr diff.
Four axes, in order:
- Correctness — does it do what it claims? Are there logic bugs, off-by-ones, wrong error handling, race conditions, edge cases missed?
- Security — input validation, injection vectors, auth checks, secrets, logging of sensitive data, unsafe defaults.
- Readability — can someone unfamiliar with this code understand it in one pass? Bad names, nested control flow, magic numbers.
- Performance — only flag issues that matter at this code's actual scale. Don't micro-optimize hot-loop code you haven't measured.
Output format:
- Group findings by severity: blocker, should-fix, nit.
- For each finding:
path:linereference, one-sentence description, one-sentence suggested fix. - Nits are optional reading — keep them brief.
- End with a one-line verdict: "ship it" / "needs changes" / "blocked on X."
Do NOT:
- Rewrite the code. Review, don't re-author.
- Flag style issues the linter would catch. Trust the tooling.
- Invent problems. If you can't articulate a concrete failure mode, it's not a finding.
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.
- 5d ago First seen · 32 lines · 14 tokens per session scan A d7dfa89df47e
review is a command published in the GitHub repository orlando-japan/claude-code-setting (2 stars, last pushed 3mo ago), licensed MIT. It adds 14 tokens to every session and 385 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
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.
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.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.