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Agent Evaluation

Skill Verified Active

Use when testing skills, commands, or agents for quality. Use after creating new skills, before deploying agents, or when debugging inconsistent agent behavior. Triggers on "evaluate", "test quality", "is this skill working", or QA of AI workflows.

Purpose

Systematically assess and improve the quality and reliability of AI agents and skills through a structured evaluation process.

Features

  • Structured 5-dimension evaluation rubric
  • Methods for direct scoring and LLM-as-judge
  • Bias detection and mitigation strategies
  • Workflow examples for new skills and agent QA
  • Test case design principles including edge cases

Use Cases

  • Evaluating new skills before deployment
  • Debugging inconsistent agent behavior
  • Comparing different agent approaches or models
  • Performing systematic QA reviews of AI workflows

Non-Goals

  • Performing one-off manual reviews of simple outputs
  • Evaluating purely creative or subjective tasks
  • Adding significant latency to real-time evaluations

Workflow

  1. Define criteria and thresholds
  2. Create test cases (easy, medium, hard, adversarial)
  3. Run direct scoring or LLM-as-judge evaluation
  4. Compare outputs and validate against ground truth
  5. Monitor agreement and iterate on prompts/skills

Practices

  • Evaluation methodology
  • Quality assurance
  • Agent testing
  • Rubric design

Installation

npx skills add guia-matthieu/clawfu-skills

Runs the Vercel skills CLI (skills.sh) via npx — needs Node.js locally and at least one installed skills-compatible agent (Claude Code, Cursor, Codex, …). Assumes the repo follows the agentskills.io format.

Quality Score

Verified
99 /100
Analyzed 1 day ago

Trust Signals

Last commitabout 1 month ago
Stars104
LicenseMIT
Status
View Source

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