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+---
+name: agentic-eval
+description: |
+  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)
+  - Measuring and improving agent response quality
+---
+
+# 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 output
+        
+        # Refine based on critique
+        failed = {k: v["feedback"] for k, v in critique_data.items() if v["status"] == "FAIL"}
+        output = llm(f"Improve to address: {failed}\nOriginal: {output}")
+    
+    return output
+```
+
+**Key insight**: Use structured JSON output for reliable parsing of critique results.
+
+---
+
+## Pattern 2: Evaluator-Optimizer
+
+Separate generation and evaluation into distinct components for clearer responsibilities.
+
+```python
+class EvaluatorOptimizer:
+    def __init__(self, score_threshold: float = 0.8):
+        self.score_threshold = score_threshold
+    
+    def generate(self, task: str) -> str:
+        return llm(f"Complete: {task}")
+    
+    def evaluate(self, output: str, task: str) -> dict:
+        return json.loads(llm(f"""
+        Evaluate output for task: {task}
+        Output: {output}
+        Return JSON: {{"overall_score": 0-1, "dimensions": {{"accuracy": ..., "clarity": ...}}}}
+        """))
+    
+    def optimize(self, output: str, feedback: dict) -> str:
+        return llm(f"Improve based on feedback: {feedback}\nOutput: {output}")
+    
+    def run(self, task: str, max_iterations: int = 3) -> str:
+        output = self.generate(task)
+        for _ in range(max_iterations):
+            evaluation = self.evaluate(output, task)
+            if evaluation["overall_score"] >= self.score_threshold:
+                break
+            output = self.optimize(output, evaluation)
+        return output
+```
+
+---
+
+## Pattern 3: Code-Specific Reflection
+
+Test-driven refinement loop for code generation.
+
+```python
+class CodeReflector:
+    def reflect_and_fix(self, spec: str, max_iterations: int = 3) -> str:
+        code = llm(f"Write Python code for: {spec}")
+        tests = llm(f"Generate pytest tests for: {spec}\nCode: {code}")
+        
+        for _ in range(max_iterations):
+            result = run_tests(code, tests)
+            if result["success"]:
+                return code
+            code = llm(f"Fix error: {result['error']}\nCode: {code}")
+        return code
+```
+
+---
+
+## Evaluation Strategies
+
+### Outcome-Based
+Evaluate whether output achieves the expected result.
+
+```python
+def evaluate_outcome(task: str, output: str, expected: str) -> str:
+    return llm(f"Does output achieve expected outcome? Task: {task}, Expected: {expected}, Output: {output}")
+```
+
+### LLM-as-Judge
+Use LLM to compare and rank outputs.
+
+```python
+def llm_judge(output_a: str, output_b: str, criteria: str) -> str:
+    return llm(f"Compare outputs A and B for {criteria}. Which is better and why?")
+```
+
+### Rubric-Based
+Score outputs against weighted dimensions.
+
+```python
+RUBRIC = {
+    "accuracy": {"weight": 0.4},
+    "clarity": {"weight": 0.3},
+    "completeness": {"weight": 0.3}
+}
+
+def evaluate_with_rubric(output: str, rubric: dict) -> float:
+    scores = json.loads(llm(f"Rate 1-5 for each dimension: {list(rubric.keys())}\nOutput: {output}"))
+    return sum(scores[d] * rubric[d]["weight"] for d in rubric) / 5
+```
+
+---
+
+## Best Practices
+
+| Practice | Rationale |
+|----------|-----------|
+| **Clear criteria** | Define specific, measurable evaluation criteria upfront |
+| **Iteration limits** | Set max iterations (3-5) to prevent infinite loops |
+| **Convergence check** | Stop if output score isn't improving between iterations |
+| **Log history** | Keep full trajectory for debugging and analysis |
+| **Structured output** | Use JSON for reliable parsing of evaluation results |
+
+---
+
+## Quick Start Checklist
+
+```markdown
+## Evaluation Implementation Checklist
+
+### Setup
+- [ ] Define evaluation criteria/rubric
+- [ ] Set score threshold for "good enough"
+- [ ] Configure max iterations (default: 3)
+
+### Implementation
+- [ ] Implement generate() function
+- [ ] Implement evaluate() function with structured output
+- [ ] Implement optimize() function
+- [ ] Wire up the refinement loop
+
+### Safety
+- [ ] Add convergence detection
+- [ ] Log all iterations for debugging
+- [ ] Handle evaluation parse failures gracefully
+```