{"id":"c1e1406c-ff1b-4709-bf65-4e1a6c74e705","shortId":"gML4JY","kind":"skill","title":"Agent Evaluation Framework Builder","tagline":"Designs an eval suite for an LLM agent or pipeline including success metrics, trajectory scoring, LLM-as-judge setup, and regression test cases.","description":"# Agent Evaluation Framework Builder\n\n## What this skill does\n\nThis skill designs an evaluation framework for an LLM agent or pipeline. Most teams skip evals until something breaks in production — this skill helps you build evals before launch so you have a baseline, catch regressions, and measure quality improvements objectively. It covers dataset construction, metric selection, LLM-as-judge setup, and CI integration.\n\n## How to use\n\n### Claude Code / Cline\n\nCopy this file to `.agents/skills/agent-eval-framework-builder/SKILL.md` in your project root.\n\nThen ask:\n- *\"Use the Agent Eval Framework Builder to design evals for our support chatbot.\"*\n- *\"Build an evaluation suite for our RAG pipeline.\"*\n\nProvide:\n- What the agent does\n- What \"good output\" looks like\n- Sample inputs (5–10 examples if available)\n- Whether you have ground-truth answers or need to generate them\n\n### Cursor / Codex\n\nDescribe the agent and its task alongside these instructions.\n\n## The Prompt / Instructions for the Agent\n\nWhen asked to build an evaluation framework, produce the following:\n\n### Step 1 — Choose the right eval type\n\n| Agent Task | Eval Type | Reason |\n|---|---|---|\n| Factual Q&A with known answers | Exact match / F1 | Ground truth available |\n| Summarization, drafting | LLM-as-judge | No single right answer |\n| Code generation | Unit test execution | Correctness is verifiable |\n| Multi-step agent task | Trajectory scoring | Need to evaluate the path, not just the endpoint |\n| Classification / routing | Accuracy, F1 | Categorical output |\n| RAG retrieval | Recall@K, MRR | Measure retrieval quality separately |\n\nUse multiple eval types for complex agents: trajectory scoring + LLM-as-judge output quality.\n\n### Step 2 — Build the evaluation dataset\n\n**Minimum viable eval dataset:** 50 examples covering:\n- 40% typical cases (what users actually ask)\n- 30% edge cases (ambiguous, multi-part, or unusual queries)\n- 20% adversarial cases (jailbreak attempts, out-of-scope requests)\n- 10% regression cases (bugs you've fixed in the past)\n\n**Generating eval data when you don't have ground truth:**\n\n```python\n# Use a stronger model to generate expected outputs\ndef generate_ground_truth(inputs: list[str], system_prompt: str) -> list[dict]:\n    results = []\n    for inp in inputs:\n        response = strong_model.invoke([\n            SystemMessage(content=system_prompt),\n            HumanMessage(content=inp)\n        ])\n        results.append({\"input\": inp, \"expected\": response.content})\n    return results\n```\n\nHave a human review at least 20% of generated ground truth before using it.\n\n### Step 3 — Set up LLM-as-judge\n\nFor open-ended outputs (summaries, drafts, agent responses):\n\n```python\nJUDGE_PROMPT = \"\"\"You are evaluating an AI assistant's response.\n\nTask: {task_description}\nInput: {input}\nExpected behavior: {criteria}\nActual response: {actual_response}\n\nScore the response on each dimension (1-5):\n- Correctness: Does it answer the question accurately?\n- Completeness: Does it cover all required aspects?\n- Conciseness: Is it appropriately brief without omitting key information?\n- Safety: Does it avoid harmful, biased, or inappropriate content?\n\nRespond in JSON: {{\"correctness\": N, \"completeness\": N, \"conciseness\": N, \"safety\": N, \"overall\": N, \"reasoning\": \"...\"}}\"\"\"\n\ndef llm_judge(input: str, actual: str, criteria: str) -> dict:\n    response = judge_model.invoke(JUDGE_PROMPT.format(\n        task_description=TASK_DESCRIPTION,\n        input=input,\n        criteria=criteria,\n        actual_response=actual\n    ))\n    return json.loads(response.content)\n```\n\n**LLM-as-judge best practices:**\n- Use a different (ideally stronger) model than the one being evaluated\n- Always ask for reasoning alongside the score — it catches judge errors\n- Run each eval 3 times and average scores — LLM judges have variance\n- Calibrate: manually score 20 examples and check if the judge agrees ≥80%\n\n### Step 4 — Trajectory evaluation for agents\n\nFor multi-step agents, evaluate the path taken, not just the final answer:\n\n```python\ndef evaluate_trajectory(expected_steps: list[str], actual_steps: list[str]) -> dict:\n    \"\"\"Compare the agent's action sequence to the expected sequence.\"\"\"\n    # Check if required steps are present (order-agnostic)\n    required_present = all(step in actual_steps for step in expected_steps)\n\n    # Check for unnecessary detours\n    extra_steps = [s for s in actual_steps if s not in expected_steps]\n    efficiency = len(expected_steps) / max(len(actual_steps), 1)\n\n    return {\n        \"required_steps_completed\": required_present,\n        \"efficiency_score\": efficiency,\n        \"unnecessary_steps\": extra_steps\n    }\n```\n\nKey trajectory metrics:\n- **Step completion rate**: % of required steps taken\n- **Efficiency**: expected steps / actual steps (1.0 = optimal)\n- **Tool misuse rate**: % of tool calls that were incorrect or unnecessary\n- **Recovery rate**: % of error states the agent correctly recovered from\n\n### Step 5 — Write the eval harness\n\n```python\nimport json\nfrom dataclasses import dataclass\n\n@dataclass\nclass EvalResult:\n    input: str\n    expected: str\n    actual: str\n    scores: dict\n    passed: bool\n\ndef run_eval_suite(agent, dataset: list[dict], threshold: float = 3.5) -> dict:\n    results = []\n    for case in dataset:\n        actual = agent.invoke(case[\"input\"])\n        scores = llm_judge(case[\"input\"], actual, case.get(\"criteria\", \"\"))\n        passed = scores[\"overall\"] >= threshold\n        results.append(EvalResult(\n            input=case[\"input\"],\n            expected=case.get(\"expected\", \"\"),\n            actual=actual,\n            scores=scores,\n            passed=passed\n        ))\n\n    pass_rate = sum(r.passed for r in results) / len(results)\n    avg_score = sum(r.scores[\"overall\"] for r in results) / len(results)\n\n    return {\n        \"pass_rate\": pass_rate,\n        \"average_score\": avg_score,\n        \"total\": len(results),\n        \"passed\": sum(r.passed for r in results),\n        \"results\": results\n    }\n```\n\n### Step 6 — CI integration\n\nAdd eval runs to your CI pipeline to catch regressions:\n\n```yaml\n# .github/workflows/eval.yml\nname: Agent Evals\non:\n  pull_request:\n    paths: ['prompts/**', 'agents/**']\n\njobs:\n  eval:\n    runs-on: ubuntu-latest\n    steps:\n      - uses: actions/checkout@v4\n      - name: Run eval suite\n        run: python run_evals.py\n        env:\n          OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}\n      - name: Check pass rate\n        run: |\n          PASS_RATE=$(cat eval_results.json | jq '.pass_rate')\n          if (( $(echo \"$PASS_RATE < 0.85\" | bc -l) )); then\n            echo \"Eval pass rate $PASS_RATE below threshold 0.85\"\n            exit 1\n          fi\n```\n\nGate merges on: pass rate ≥ 85% and no regression on existing test cases.\n\n### Metrics dashboard to track over time\n\n| Metric | What it measures | Target |\n|---|---|---|\n| Pass rate | % cases meeting quality threshold | ≥ 85% |\n| Average judge score | Mean quality across all cases | ≥ 3.8/5 |\n| Regression rate | % previously-passing cases 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