| name | researcher-evaluation |
| description | Technical playbook for GenAI agent evaluation using agents/benchmark.py with G-Eval methodology |
| license | MIT |
| compatibility | opencode |
| metadata | {"audience":"developers","workflow":"evaluation"} |
Researcher Evaluation Skill
Technical playbook for evaluating GenAI agents. OpenCode uses agents/benchmark.py directly.
Required Output Format
Evaluation Results
| Framework | Input | Output | Score | Feedback | Recommendations |
|---|
| AutoGen | {task} | {truncated}... | 4/5 | {feedback} | {improvements} |
| CrewAI | {task} | {truncated}... | 3/5 | {feedback} | {improvements} |
| OpenHands | {task} | {truncated}... | 5/5 | {feedback} | {improvements} |
Summary
| Metric | Value |
|---|
| Winner | {framework} |
| Reasoning | {why} |
| Judge Model | {model} |
| Eval Time | {ms} |
Scoring Scale
| Score | Meaning |
|---|
| 0 | Failed/error |
| 1 | Mostly wrong |
| 2 | Partial |
| 3 | Acceptable |
| 4 | Good |
| 5 | Excellent |
Evaluation Dimensions
| Dimension | Description |
|---|
| Accuracy | Facts correct, no hallucinations |
| Completeness | All sub-tasks addressed |
| Actionability | Concrete next steps |
| Clarity | Well-structured |
| Relevance | On topic |
| Efficiency | Concise |
Methodology (G-Eval)
Chain-of-Thought evaluation steps:
- Check factual accuracy
- Verify task completion
- Assess actionability
- Check for hallucinations
- Evaluate conciseness
→ Score 0-5
CLI
python -m agents.benchmark --tasks github-issue-triage --frameworks autogen crewai openhands
Key Files
| File | Line | Class/Function |
|---|
agents/benchmark.py | 286 | QualityEvaluator |
agents/benchmark.py | 459 | ComparativeEvaluator |
agents/benchmark.py | 694 | Benchmark |
