Evaluate Claude Code commands, skills, and agents with structured rubrics.
npx skills add https://github.com/Zpankz/mcp-skillset --skill agent-evaluation-zpankz
Please help me install this Agent Skill. Skill: agent-evaluation Source: https://github.com/Zpankz/mcp-skillset/tree/main/agent-evaluation Command: npx skills add https://github.com/Zpankz/mcp-skillset --skill agent-evaluation-zpankz
Evaluate and improve Claude Code commands, skills, and agents. It helps teams test prompt effectiveness, validate context engineering choices, and measure improvement quality.
Run an evaluation pass on your latest Claude Code prompt to identify gaps, surface actionable improvements, and document the results.
High-intent search queries and answers about installing and using this skill.
To evaluate Claude Code agents, define evaluation rubrics and apply LLM-as-Judge patterns with bias mitigation techniques. This delivers structured outputs and provenance for iterative prompt refinement and performance measurement.
An evaluation rubric for prompt engineering defines specific criteria to assess agent performance and context validation. It works by applying these metrics across single or multi-agent setups to measure improvement quality and surface actionable gaps.
Yes, you can validate context engineering choices for multi-agent setups by applying hierarchical evaluation workflows. It measures improvement quality across multiple agents and delivers structured reporting for prompt refinement.
The best way to mitigate bias in LLM-as-Judge evaluations is to apply structured bias mitigation techniques alongside defined rubrics. This ensures robust assessments and delivers provenance for iterative prompt improvement.
Document evaluation results and track improvements by generating structured reporting and trend analysis. These outputs provide provenance and actionable guidance for iterative prompt refinement across test sets.
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