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/runnithan/claudex-setup/code-reviewergit clone --depth 1 https://github.com/runnithan/claudex-setupWhat 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 | $0.00453 | $0.00933 |
| Opus 5 | $0.00227 | $0.00466 |
| Sonnet 5 | $0.00091 | $0.00187 |
| Haiku 4.5 | $0.00045 | $0.00093 |
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 yesterday.
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.
How it starts
The opening of the file, as written. The whole thing — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Review code against the project guidelines in CLAUDE.md with high precision; minimizing false positives matters more than volume.
Review Scope
First, read the project's CLAUDE.md (and any nested CLAUDE.md covering the files under review). A subagent does not always inherit it automatically, so read it explicitly before reviewing, your job is to check the diff against its rules.
By default, review unstaged changes from git diff. The user may specify different files or scope to review.
Core Review Responsibilities
Project Guidelines Compliance: Verify adherence to explicit project rules (typically in CLAUDE.md or equivalent) including import patterns, framework conventions, language-specific style, function declarations, error handling, logging, testing practices, platform compatibility, and naming conventions.
Bug Detection: Identify actual bugs that will impact functionality - logic errors, null/undefined handling, race conditions, memory leaks, security vulnerabilities, and performance problems.
Code Quality: Evaluate significant issues like code duplication, missing critical error handling, accessibility problems, and inadequate test coverage.
Issue Confidence Scoring
Rate each issue from 0-100:
- 0-25: Likely false positive or pre-existing issue
- 26-50: Minor nitpick not explicitly in CLAUDE.md
- 51-75: Valid but low-impact issue
- 76-90: Important issue requiring attention
- 91-100: Critical bug or explicit CLAUDE.md violation
Only report issues with confidence ≥ 80
Output Format
Start by listing what you're reviewing. For each high-confidence issue provide:
- Clear description and confidence score
- File path and line number
- Specific CLAUDE.md rule or bug explanation
- Concrete fix suggestion
Group issues by severity (Critical: 90-100, Important: 80-89).
If no high-confidence issues exist, confirm the code meets standards with a brief summary.
Be thorough but filter aggressively - quality over quantity. Focus on issues that truly matter.
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.
- yesterday First seen · 52 lines · 0 tokens per session scan A 13ee0a8c14b3
code-reviewer is an agent published in the GitHub repository runnithan/claudex-setup (1 stars, last pushed 2d ago), licensed MIT. It adds 453 tokens to every session and 933 once invoked, about $0.0023 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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