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CodeSherlock — AI Code Review for the Agentic Engineering Era

CodeSherlock is an AI-powered code analysis and review platform that helps teams identify security, quality, and production-readiness issues before code is merged.

Built for both human-written and AI-generated code, CodeSherlock integrates across GitHub Pull Requests, VS Code, Claude Code, Cursor, Kiro, either as MCP, and or CLI — bringing automated review from code creation through pre-merge.

Context-Aware PR Reviews

CodeSherlock analyzes Pull Request changes with relevant repository context to surface issues that matter, not just generic observations.

Reviews include:

• PR summaries and insights • Prioritized findings by severity • Inline code findings • Recommended fixes • Security and quality analysis

Power Analysis

Run focused analysis across four core dimensions:

Security | Maintainability | Performance | Reliability and Observability

Power Analysis prioritizes critical and high-impact issues, helping developers focus their attention where it matters most.

Security Analysis

Choose the security lens you need for the same code or PR:

• Power Analysis • OWASP Top 10 • CWE-MITRE • CWE-KEV

Security findings can include the affected code, severity, explanation, remediation guidance, and relevant OWASP/CWE mapping along with detailed prompts for instant fixes.

Built for AI Development

CodeSherlock can be used directly by developers or integrated into AI coding workflows through MCP and CLI.

This enables an independent validation loop:

Generate → Analyze → Fix → Validate

Teams can also define custom rules to incorporate internal security, architecture, and engineering standards into their reviews.

Review Earlier. Fix Earlier.

Use CodeSherlock in the IDE while writing code, through MCP with coding agents, from the CLI, and on GitHub Pull Requests before merge.

Context over clutter. Precision over volume.

Visit CodeSherlock.ai to get started.