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/fatihkan/badiWrote 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/fatihkan/badi/ai-review)<a href="https://agentmods.dev/commands/fatihkan/badi/ai-review"><img src="https://agentmods.dev/badge/commands/fatihkan/badi/ai-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/fatihkan/badi/ai-review"><img src="https://agentmods.dev/badge/commands/fatihkan/badi/ai-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 | $0.00370 |
| Opus 5 | $0.00000 | $0.00185 |
| Sonnet 5 | $0.00000 | $0.00074 |
| Haiku 4.5 | $0.00000 | $0.00037 |
Grade A, and why
ai-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 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
AI code review of the staged git diff via the Claude API.
Required Tools
- Bash (badi ai review)
Prerequisite
The ANTHROPIC_API_KEY environment variable must be set:
export ANTHROPIC_API_KEY=sk-ant-...
Sign-up: https://console.anthropic.com/settings/keys
Procedure
Step 1: Stage the Changes
git add [files]
Step 2: AI Review
badi ai review
The staged diff is reviewed with the Claude Haiku 4.5 model. A fast pass taking ~1-3 seconds.
Step 3: Interpret
Findings in 5 categories:
- CRITICAL security — fix immediately
- Bug potential — check test coverage
- Performance — hot-path changes
- Code quality — DRY, naming, complexity
- Positive observations — things done well
Step 4: Action
- Critical finding: do not commit; fix first
- High: add a review note, separate commit
- Medium/Low: create a TODO/issue
Step 5: Follow-up
A git hook to use before every commit:
# .git/hooks/pre-commit
badi ai review || exit 1
Cost
- Haiku 4.5: ~$0.25 / 1M input, ~$1.25 / 1M output
- Average review: 2-3K input, 500-1000 output tokens
- Approximate cost: $0.001 per review
Example
git add src/auth.js
/ai-review
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 · 63 lines · 0 tokens per session scan A 8a51c7140935
ai-review is a command published in the GitHub repository fatihkan/badi (7 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 370 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 2026-08-31.
Other commands, from other repositories
sonarqube
Analyze SonarCloud quality issues for a specific PR.
review-pr
Perform a comprehensive code review of a pull request.
pr
Analyze changes, detect scope issues, and create a well-structured PR.
refactor
Analyze code for SOLID violations and suggest targeted improvements.
audit-codebase
Codebase health audit scoring 7 categories with progression plan.
check-cache-bugs
Audit Claude Code setup for cache bugs (CC#40524) — sentinel, --resume/--continue, attribution header + ArkNill B3/B4/B5.