agentic-eval
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when implementing self-critique and reflection loops, building evaluator-optimizer pipelines for quality-critical generation, creating test-driven code refinement workflows, designing rubric-based or LLM-as-judge evaluation systems, adding iterative improvement to agent outputs (code, reports, analysis), and measuring agent response quality.
npx skills add majiayu000/claude-skill-registry --skill agentic-eval-darkglow-net-github-copilot-kicks --agent copilot
Same command for any agent — swap --agent for claude-code, codex, cursor.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Agentic Evaluation Patterns Patterns for self-improvement through iterative evaluation and refinement. ## Overview Evaluation patterns enable agents to assess and improve their own outputs, moving beyond single-shot generation to iterative refinement loops. ``` Generate → Evaluate → Critique → Refine → Output ↑ │ └──────────────────────────────┘ ``` ## When to Use - **Quality-critical generation**: Code, reports, analysis requiring high accuracy - **Tasks with clear evaluation criteria**: Defined success metrics exist - **Content requiring specific standards**: Style guides, compliance, formatting --- ## Pattern 1: Basic Reflection Agent evaluates and improves its own output through self-critique. ```python def reflect_and_refine(task: str, criteria: list[str], max_iterations: int = 3) -> str: """Generate with reflection loop.""" output = llm(f"Complete this task:\n{task}") for i in range(max_iterations): # Self-critique critique = llm(f""" Evaluate this output against criteria: {criteria} Output: {output} Rate each: PASS/FAIL with feedback as JSON. """) critique_data = json.loads(critique) all_pass = all(c["status"] == "PASS" for c in critique_data.values()) if all_pass: return
- Overview
- When to Use
- Pattern 1: Basic Reflection
- Pattern 2: Evaluator-Optimizer
- Pattern 3: Code-Specific Reflection
- Evaluation Strategies
- Outcome-Based
- LLM-as-Judge
- Rubric-Based
- Best Practices
- Quick Start Checklist
What does the agentic-eval skill do?
Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when implementing self-critique and reflection loops, building evaluator-optimizer pipelines for quality-critical generation, creating test-driven code refinement workflows, designing rubric-based or LLM-as-judge evaluation systems, adding iterative improvement to agent outputs (code, reports, analysis), and measuring agent response quality.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill agentic-eval-darkglow-net-github-copilot-kicks --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.