Enables AI agents to investigate and repair Python/pytest repositories in isolated Git worktrees with audit trails, without modifying the original repository.
Provides structured, sandboxed test and lint feedback for coding agents, returning compact typed verdicts with failure fingerprints instead of raw runner output. It enables impact-selected test execution in isolated containers, distinguishing pre-existing failures from regressions.
Enables AI assistants to run and analyze pytest tests for desktop applications through interactive commands. Supports test execution, filtering, result analysis, and debugging for comprehensive test automation workflows.
Enables deterministic evaluation of coding agents by exposing controlled repository tools and returning structured verification reports with pattern checks and repeat-run comparisons.
Local-first MCP and coding-agent reliability harness that captures bounded, sanitized failure evidence and generates deterministic executable regression tests. Capture is opt-in; no API key or hosted service is required.
Enables AI coding agents to debug Python projects by running pytest, extracting failure locations, displaying code context around failures, and optionally requesting fix suggestions from Gemini.