Skill: boleyn/agentic-eval (version 1.0.0)

--- 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 ```


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