Enables users to make AI coding agents prove their work with passing tests before finishing, snapshot every edit so any step can be undone, and trim noisy tool output to save tokens. Runs locally with no telemetry and works with Claude Code, Codex and OpenCode.
Enables AI-powered code review and improvement, including analysis, refactoring suggestions, and automatic test generation, with an optional agentic loop for iterative refinement.
Enables diagnosis of recurring coding-agent loop failures from clean, local, zero-telemetry traces and proposes small, reversible fixes to verifiers, retry policies, stop conditions, or skills that are applied only after explicit approval.
Enables AI agents to run a project's pytest, unittest, jest, or vitest suites and get back compact structured results with trimmed tracebacks, collection errors kept apart from real failures, and the slowest tests. Agents can then re-run just the failures, expand one failure with local variables, repeatedly execute a single test to judge flakiness, and inspect Python coverage gaps, all under a hard timeout that kills the whole process tree.
Enables LLM editors and agents to run structured dialectical reasoning — thesis, antithesis, and synthesis — plus a player-coach autocoding loop where an independent verifier checks code against requirements. Exposes tools for single-shot or multi-phase workflows, session persistence, and optional coach backends grounded in web search.