Document Review

The document-review skill enables AI agents to systematically analyze text files, codebases, and documentation to identify errors, inconsistencies, and areas for improvement. It acts as an automated quality assurance layer, providing detailed feedback on syntax, logic, and adherence to best practices without requiring manual line-by-line inspection.

When to use it

• Pre-merge validation: Run reviews before committing code changes to catch bugs early in the development cycle.
• Onboarding assistance: Analyze new documentation or legacy codebases to help team members understand existing standards and patterns.
• Compliance checks: Verify that generated reports or legal documents adhere to specific formatting guidelines or regulatory requirements.
• Refactoring support: Identify outdated libraries, redundant functions, or inefficient algorithms within a large project structure.

Key capabilities

• Parses multiple file formats including Markdown, JSON, YAML, and various programming languages.
• Detects syntax errors, logical flaws, and security vulnerabilities in code snippets.
• Highlights inconsistencies in tone, style, or terminology across documents.
• Generates actionable suggestions for fixing identified issues directly within the context of the source material.

Example prompts

• "Review this Python script for potential security vulnerabilities and suggest fixes."
• "Analyze the attached project documentation to ensure it aligns with our company's style guide."
• "Scan this JSON configuration file for missing required fields or invalid data types."

Tips & gotchas

Ensure the document-review skill has access to the full context of the files being analyzed, as fragmented inputs may lead to incomplete assessments. While effective at identifying obvious errors, complex architectural decisions may still require human judgment beyond automated detection.