yeaight7

Run an anti-slop cleanup workflow on AI-generated code — regression-tests-first, smell-by-smell, behavior preserved. Supports explicit review-only mode.

Use when designing or using MCP-backed structured code search with search, AST query, symbol inventory, and bounded extraction workflows.

Use when designing, running, debugging, or hardening deterministic eval suites for agent skills, prompts, tool workflows, or MCP-backed cases.

Maintain short, focused Markdown files per subsystem to provide agents with isolated context.

Full autonomous execution from idea to working code

Use when the user provides a URL to a standard web page and clean Markdown extraction with Defuddle would reduce clutter and token cost.

Use when a Codex or generic local agent needs a focused second opinion from Gemini CLI without MCP routing.

Diagnose NaN losses, out-of-memory errors, and shape mismatches in deep learning or ML pipelines.

Run independent work in parallel with explicit wave boundaries, lightweight coordination, and bounded verification. Use when latency matters and the work does not need a persistent completion loop.

Use when a user needs to build or refresh persistent graph memory from a mixed corpus and the right path may include graphify, incremental update, or helper conversion before ingestion.

Run an anti-slop cleanup workflow on AI-generated code — regression-tests-first, smell-by-smell, behavior preserved.

Use when creating new skills, editing existing skills, or verifying skills work before deployment