evaluate-rag
Evaluate a RAG (retrieval-augmented generation) pipeline's retrieval quality and generation quality separately. Use when the pipeline retrieves context from a knowledge base before generating answers.
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error-analysis
Systematically identify and categorize failure modes in an LLM pipeline by reading traces. Use when starting a new eval project, after significant pipeline changes (new features, model switches, prompt rewrites), when production metrics drop, or after incidents.
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Install skill "error-analysis" with this command: npx skills add majidraza1228/skillsmp-majidraza1228-majidraza1228-error-analysis
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build-review-interface
Build a custom browser-based annotation interface for reviewing LLM traces and collecting human labels. Use when reviewers are working with raw JSON files, when you need to collect Pass/Fail labels at scale, or when trace data needs domain-specific formatting to be readable.
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eval-audit
Audit an LLM eval pipeline and surface problems: missing error analysis, unvalidated judges, vanity metrics, etc. Use when inheriting an eval system, when unsure whether evals are trustworthy, or as a starting point when no eval infrastructure exists.
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eval-coding-agent
Evaluate a coding agent's output quality across the failure modes specific to code generation and editing: correctness, scope discipline, instruction following, safety, and diff quality. Use when building or improving a Claude-powered coding assistant, code review agent, or code generation pipeline.
Repository SourceNeeds Review