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/fletchgqc/agentbox/reviewgit clone --depth 1 https://github.com/fletchgqc/agentboxWrote 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/fletchgqc/agentbox/review)<a href="https://agentmods.dev/commands/fletchgqc/agentbox/review"><img src="https://agentmods.dev/badge/commands/fletchgqc/agentbox/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 | $0.00004 | $0.00168 |
| Opus 5 | $0.00002 | $0.00084 |
| Sonnet 5 | $0.00001 | $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 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
Conduct a code review of $1.
Your aim is to convince a skeptical human that you have found all relevant issues and no false positive problems. To this end, create a report including snippets or line references to the problematic code. Justify why you think this is, or could be, a problem, quoting from authoratitive sources including books and online references known to you.
When reviewing, point out:
- possible maintenance problems
- unnecessarily complex solutions
- where more code has been added than strictly necessary
- hard to understand for humans
- unnecessary comments
- architechtural bad practices
- security issues or potential problems
- anything which deviates from existing project conventions.
Suggest solutions to any issues you find. Where multiple good solutions or options exist, explain all options.
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 · 19 lines · 4 tokens per session scan A 635d7c90e18b
review is a command published in the GitHub repository fletchgqc/agentbox (113 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 4 tokens to every session and 168 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.