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 skills add Jignesh-Ponamwar/skills-mcp --skill code-reviewgit clone --depth 1 https://github.com/Jignesh-Ponamwar/skills-mcpWrote 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/skills/jignesh-ponamwar/skills-mcp/code-review)<a href="https://agentmods.dev/skills/jignesh-ponamwar/skills-mcp/code-review"><img src="https://agentmods.dev/badge/skills/jignesh-ponamwar/skills-mcp/code-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/skills/jignesh-ponamwar/skills-mcp/code-review"><img src="https://agentmods.dev/badge/skills/jignesh-ponamwar/skills-mcp/code-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 | $0.00061 | $0.00849 |
| Opus 5.5 | $0.00024 | $0.00340 |
| Sonnet 5.5 | $0.00012 | $0.00170 |
| Haiku 4.5 | $0.00006 | $0.00085 |
Grade A, and why
code-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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review Skill
Overview
A structured, language-agnostic code review process that identifies bugs, security issues, performance problems, and style violations. Always produces actionable output with severity ratings.
Review Process
Step 1: Read Before Commenting
Read the entire code block before writing any feedback. Understanding the full context prevents false positives and surface-level comments.
Step 2: Identify the Language and Context
Note the language, framework, and apparent purpose. Calibrate expectations accordingly (e.g., prototype vs. production, CLI script vs. library).
Step 3: Check for Correctness
- Logic errors and off-by-one bugs
- Unhandled edge cases (null/None, empty collections, negative numbers)
- Incorrect operator precedence or type coercion
- Missing error handling at I/O boundaries
- Race conditions in concurrent code
Step 4: Check for Security Issues
Scan specifically for:
- Injection: SQL injection, shell injection, SSTI, XSS
- Hardcoded secrets: API keys, passwords, tokens in source
- Insecure deserialization:
pickle.loads,eval,execon user input - Path traversal: user-controlled file paths without sanitization
- Weak cryptography: MD5/SHA1 for passwords, ECB mode, short keys
- IDOR: direct object references without authorization checks
- SSRF: user-controlled URLs in outbound HTTP requests
Step 5: Check for Performance Issues
- N+1 query patterns (database calls inside loops)
- Unnecessary copying of large data structures
- Missing indexes on frequently-queried fields
- Blocking I/O on async event loops
- Redundant computations that could be cached
Step 6: Check for Maintainability
- Unclear variable or function names
- Functions longer than ~50 lines (flag, don't mandate splitting)
- Missing or incorrect type annotations (for typed languages)
- Magic numbers without named constants
- Dead code or unused imports
Step 7: Write Structured Findings
Use this format for each finding:
[SEVERITY] Category: Title
File: path/to/file.py, Line: N
Issue: One sentence describing the problem.
Impact: What could go wrong.
Fix:
<corrected code snippet>
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 99 lines · 61 tokens per session scan A bd07b5819721
code-review is a skill published in the GitHub repository Jignesh-Ponamwar/skills-mcp (10 stars, last pushed 5mo ago), licensed Apache-2.0. It adds 61 tokens to every session and 849 once invoked, about $0.0002 per session on Opus 5.5. 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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code-review-ai-ai-review
Combine static analysis with model-assisted code review to assess security, performance, correctness, and architecture. Use when designing review automation or reviewing complex changes.