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/JSK9999/ai-nexusWrote 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/jsk9999/ai-nexus/review)<a href="https://agentmods.dev/commands/jsk9999/ai-nexus/review"><img src="https://agentmods.dev/badge/commands/jsk9999/ai-nexus/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/jsk9999/ai-nexus/review"><img src="https://agentmods.dev/badge/commands/jsk9999/ai-nexus/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 | $0.00015 | $0.00146 |
| Opus 5 | $0.00008 | $0.00073 |
| Sonnet 5 | $0.00003 | $0.00029 |
| Haiku 4.5 | $0.00002 | $0.00015 |
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 8d 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
Code Review Command
Review the code changes following these guidelines:
- Read the diff carefully
- Check against review checklist
- Provide specific, actionable feedback
Focus Areas
- Security vulnerabilities
- Performance issues
- Code style consistency
- Test coverage
- Documentation
Output Format
## Summary
Brief overview of the changes
## Issues Found
- [ ] Issue 1: Description + suggestion
- [ ] Issue 2: Description + suggestion
## Good Patterns
- Pattern 1: Why it's good
## Suggestions
- Optional improvements
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.
- 8d ago First seen · 37 lines · 15 tokens per session scan A 6ab2348459ef
review is a command published in the GitHub repository JSK9999/ai-nexus (19 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 15 tokens to every session and 146 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 2026-08-30.
Other commands, from other repositories
review-prs
Parallel PR triage: decide merge-worthiness, prepare rebased worktrees, fix blockers, return them for human merge.
maintainer-review-synthesize
Synthesize findings from all review aspects into a single maintainer-ready review report (Pi-tuned).
maintainer-review-code-review
Review the PR for code quality, CLAUDE.md compliance, project conventions, and bugs (Pi-tuned).
maintainer-review-comment-quality
Review the PR's added/modified comments and docstrings for accuracy, value, and long-term maintainability (Pi-tuned).
maintainer-review-docs-impact
Review whether the PR's user-facing changes (APIs, CLI flags, env vars, behavior) are reflected in documentation (Pi-tuned).
maintainer-review-error-handling
Review the PR for error-handling correctness — surfaced errors, no silent swallows, consistent error patterns (Pi-tuned).