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Found 3 Skills
Use this skill when the user wants to debug, diagnose, or systematically iterate on an experiment that already exists, or when they need a structured experiment log for tracking runs, hypotheses, failures, results, and next steps during active research. Apply it to underperforming methods, training that will not converge, regressions after a change, inconsistent results across datasets, aimless experimentation without progress, and questions like 'why doesn't this work?', 'no progress after many attempts', or 'how should I investigate this failure?'. Also use it for setting up practical experiment logging/record-keeping that supports debugging and iteration. Do not use it for designing a brand-new experiment pipeline or full experiment program (use experiment-pipeline), generating research ideas, fixing isolated coding/syntax errors, or writing retrospective summaries into research memory/notes/knowledge bases.
Fetch CI build results and diagnose failures. Auto-detects provider from project files or URLs. Supports GitHub Actions, Buildkite, and CircleCI.
Evaluates RAG (Retrieval-Augmented Generation) pipeline quality across retrieval and generation stages. Measures precision, recall, MRR for retrieval; groundedness, completeness, and hallucination rate for generation. Diagnoses failure root causes and recommends chunk, retrieval, and prompt improvements. Triggers on: "audit RAG", "RAG quality", "evaluate retrieval", "hallucination detection", "retrieval precision", "why is RAG failing", "RAG diagnosis", "retrieval quality", "RAG evaluation", "chunk quality", "RAG pipeline review", "grounding check". Use this skill when diagnosing or evaluating a RAG pipeline's quality.