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/fitlab-ai/agent-infraWrote 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/fitlab-ai/agent-infra/review-analysis.en)<a href="https://agentmods.dev/commands/fitlab-ai/agent-infra/review-analysis.en"><img src="https://agentmods.dev/badge/commands/fitlab-ai/agent-infra/review-analysis.en/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/fitlab-ai/agent-infra/review-analysis.en"><img src="https://agentmods.dev/badge/commands/fitlab-ai/agent-infra/review-analysis.en.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 | $0.00005 | $0.00058 |
| Opus 5 | $0.00003 | $0.00029 |
| Sonnet 5 | $0.00001 | $0.00012 |
| Haiku 4.5 | $0.00001 | $0.00006 |
Grade A, and why
review-analysis.en 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 and execute the review-analysis skill from .agents/skills/review-analysis/SKILL.md.
Follow all steps defined in the skill exactly.
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 · 9 lines · 5 tokens per session scan A 6fd45a4b4baa
review-analysis.en is a command published in the GitHub repository fitlab-ai/agent-infra (84 stars, last pushed today), licensed MIT. It adds 5 tokens to every session and 58 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 2026-09-03.
Other commands, from other repositories
atomic-plan
Write a design doc (concepts, business rules, approaches) and a checkpoint-table spec (contract) for non-trivial work; inline spec only for trivial. Gauges triviality; loops spec authoring with subagents. Human-facing artifact, Mermaid diagrams allowed.
next
Suggest the most likely next workflow action based on current context.
afo
Open feature worktree in terminal and start agent (shortcut for feature-open).
advanced-code-review-context
Advanced Code Review Phase 2: Context Analysis - load previous reviews, PR history, declined items.
aigon-research-context
Record the original author's durable, transcript-free handoff.
pick-agent
A command that helps users choose and import only the agent role they need, such as a frontend developer, researcher, or editor.