Orchestrates AI agents to efficiently fix static analysis issues from SARIF reports.
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Sheriff acts as a Model Context Protocol (MCP) server, designed to streamline the process of AI agents fixing static analysis findings. It addresses common AI challenges like context overload and lost progress by intelligently batching issues, persisting progress across sessions, and allowing agents to focus on specific rules or file patterns. By providing compact responses and a structured workflow, Sheriff enables AI agents to efficiently navigate, fix, and manage code quality issues identified by various static analysis tools.
주요 기능
01Scope filtering by rules, severities, or file patterns
020 GitHub stars
03Intelligent issue batching by file for efficient fixing
04Persistent progress tracking across agent sessions
05Compact responses to minimize AI context window usage
06Supports all SARIF-producing static analysis tools
사용 사례
01Integrating AI-powered code remediation into existing CI/CD pipelines that generate SARIF reports
02Guiding AI agents to fix all issues of a specific type (e.g., 'ConstantValue' issues) in a codebase
03Managing a queue of static analysis findings for AI agents to resolve incrementally
