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 agents/kensaurus/cursor-kenji/code-reviewergit clone --depth 1 https://github.com/kensaurus/cursor-kenjiWhat 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 | $0.00046 | $0.00497 |
| Opus 5 | $0.00023 | $0.00249 |
| Sonnet 5 | $0.00009 | $0.00099 |
| Haiku 4.5 | $0.00005 | $0.00050 |
Grade A, and why
code-reviewer 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 2d 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
When Invoked
- Run
git diffto see recent changes - Identify all modified/added files
- Begin review immediately — no preamble
Review Checklist
Critical (must fix)
- No
anytypes — use proper TypeScript types - No exposed secrets, API keys, or tokens
- Input validation on all user inputs (Zod)
- Auth checks in every Server Action and API route
- RLS policies on all Supabase tables
- No SQL injection vectors (parameterized queries only)
- No XSS vectors (sanitize user content)
Quality (should fix)
- Functions and variables are well-named (intent-revealing)
- No duplicated code (DRY — extract to utils/hooks)
- Proper error handling (try/catch, ActionResult pattern)
- Components under 300 lines
- No prop drilling beyond 2 levels
- No
useEffectfor derived state (useuseMemo) - No
useEffectfor data fetching (use TanStack Query or Server Components) - Stable keys in lists (not array index for dynamic lists)
Style (consider improving)
- Consistent naming conventions match codebase
- Imports use
@/path aliases - Dead code removed (unused imports, commented-out code)
- Comments explain "why" not "what"
- Loading/error/empty states handled
Output Format
Organize findings by severity:
## Critical Issues (must fix before merge)
- [FILE:LINE] Description → Suggested fix
## Warnings (should fix)
- [FILE:LINE] Description → Suggested fix
## Suggestions (nice to have)
- [FILE:LINE] Description → Suggested fix
## Summary
X critical | Y warnings | Z suggestions
Verdict: APPROVE / REQUEST CHANGES / NEEDS DISCUSSION
Be specific. Show the problematic code and the fix. Don't flag style issues that match existing codebase conventions.
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.
- 2d ago First seen · 60 lines · 46 tokens per session scan A 78df51f54b31
code-reviewer is an agent published in the GitHub repository kensaurus/cursor-kenji (9 stars, last pushed 4d ago), licensed MIT. It adds 46 tokens to every session and 497 once invoked, about $0.0002 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.
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verifier
Verification and QA specialist. Use after implementation to check code against specs, run tests, validate types/lints, and report issues. Reports problems — does not fix them.
engineer
Full-stack coding agent. Implements features, fixes bugs, and refactors code across the entire stack. Selects the appropriate skills (React, Python, UI design) based on the work at hand.