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SKILL unreviewed MIT Self-run

Ai Code Review

skill-ssrjkk-claude-skills-ai-code-review · by ssrjkk

Automated code review with AI agents

No reviews yet
0 installs
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Install

$ agentstack add skill-ssrjkk-claude-skills-ai-code-review

Open-source listing, not yet scanned by AgentStack. Follow the source repository for install instructions.

Security review

⚠ Flagged

1 finding(s); flagged for manual review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures
  • high Dangerous shell/eval execution.

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution Used
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

Reliability & compatibility

Not yet reviewed
0 installs to date
no reviews yet
1mo ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.

How agent discovery & health will work →
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About

AI Code Review

> Automate code reviews with AI agents that catch bugs, security issues, and style violations.

Quick Start

# ai_review.py — AI-powered code review script
import subprocess
import json
from anthropic import Anthropic

client = Anthropic()

def get_pr_diff():
    result = subprocess.run(
        ["git", "diff", "main...HEAD"],
        capture_output=True, text=True
    )
    return result.stdout

def review_code(diff: str) -> dict:
    response = client.messages.create(
        model="claude-sonnet-4-20250514",
        system="You are an expert code reviewer. Analyze diffs for:\n"
               "1. Security vulnerabilities (XSS, SQLI, CSRF, injection)\n"
               "2. Performance issues (N+1 queries, memory leaks)\n"
               "3. Logic bugs (off-by-one, race conditions)\n"
               "4. Type safety (missing null checks, any usage)\n"
               "5. Code quality (dead code, complexity, duplication)\n"
               "Rate each category: CRITICAL, WARNING, INFO",
        messages=[{
            "role": "user",
            "content": f"Review this diff:\n```diff\n{diff}\n```"
        }],
        max_tokens=2000
    )
    return response.content[0].text

if __name__ == "__main__":
    diff = get_pr_diff()
    if diff:
        print(review_code(diff))

Key Concepts

AI code review complements (not replaces) human review. Best for catching common issues, suggesting improvements, and enforcing conventions. Integrate via CI pipeline or pre-commit hooks.

When to Use

  • CI pipeline gate before human review
  • Pre-commit hooks for immediate feedback
  • Large PRs where manual review is slow
  • Security audits on every commit

Validation

  1. Review correctly identifies known issues in test diffs
  2. False positive rate is acceptable (< 20%)
  3. Review is actionable — each finding has a suggested fix

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

Reviews

No reviews yet, be the first.

Versions

  • v0.1.0 Imported from the upstream source.