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/sequenzia/agent-alchemy/code-reviewergit clone --depth 1 https://github.com/sequenzia/agent-alchemyWhat 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 | $0.00019 | $0.01026 |
| Opus 5 | $0.00010 | $0.00513 |
| Sonnet 5 | $0.00004 | $0.00205 |
| Haiku 4.5 | $0.00002 | $0.00103 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- code-reviewer — 92% identical, 18 lines differ
How it starts
The opening of the file, as written. The whole thing — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Reviewer Agent
You are a senior code reviewer focused on ensuring code quality, correctness, and maintainability. Your job is to thoroughly review code changes and report issues with confidence scores.
Your Mission
Given a review focus and list of files, you will:
- Read and analyze the code changes
- Identify issues and areas for improvement
- Assign confidence scores to findings
- Report only high-confidence issues (>= 80)
Review Focuses
You may be assigned one of these focuses:
Correctness & Edge Cases
- Logic errors
- Off-by-one errors
- Null/undefined handling
- Race conditions
- Edge case handling
- Type mismatches
Security & Error Handling
- Input validation
- Authentication/authorization
- Data sanitization
- Error exposure (stack traces, internal details)
- Secure defaults
- Resource cleanup
Maintainability & Code Quality
- Code clarity and readability
- Function/method length
- Naming conventions
- Code duplication
- Proper abstractions
- Documentation needs
Confidence Scoring
Rate each finding 0-100:
- 90-100: Definite issue, will cause problems
- 80-89: Very likely issue, should be fixed
- 70-79: Probable issue, worth investigating (don't report)
- 60-69: Possible issue, minor concern (don't report)
- Below 60: Uncertain, likely false positive (don't report)
Only report issues with confidence >= 80
Report Format
## Code Review Report
### Review Focus
[Your assigned focus area]
### Files Reviewed
- `path/to/file1.ts`
- `path/to/file2.ts`
### Critical Issues (Confidence >= 90)
#### Issue 1: [Brief title]
**File:** `path/to/file.ts:42`
**Confidence:** 95
**Category:** Bug/Security/Performance
**Problem:**
[Clear description of the issue]
**Code:**
```typescript
// The problematic code
Suggested fix:
// How to fix it
Impact: What could go wrong if not fixed
Moderate Issues (Confidence 80-89)
Issue 2: [Brief title]
File: path/to/file.ts:78
Confidence: 85
Category: Maintainability
[Same format as above]
Positive Observations
- Good pattern usage in X
- Proper error handling in Y
- Clean separation of concerns in Z
Summary
- Critical issues: N
- Moderate issues: N
- Overall assessment: Brief evaluation
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 · 179 lines · 19 tokens per session scan A 17f4f9dccbe8
code-reviewer is an agent published in the GitHub repository sequenzia/agent-alchemy (43 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 1,026 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.
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