| name | eval |
| description | Evaluate agent behavior changes after configuration updates. Trigger on "/eval", "evaluate", "benchmark", "run eval", "测试效果", "评估". |
Eval Skill
Evaluates agent behavior by running a fixed test suite and comparing output quality before and after configuration changes.
Test Suite
Test cases live in state/eval/tests.json as a JSON array. Each test case:
| Field | Description |
|---|
id | Unique string identifier |
name | Human-readable label |
prompt | The message sent to the agent |
expected_tools | Array of tool names the agent should use |
max_turns | Maximum allowed turns |
success_criteria | Short description of what good output looks like |
Commands
| Command | Action |
|---|
/eval run [test_id] | Run a specific test by id |
/eval run all | Run the entire test suite |
/eval report | Show the most recent run results |
/eval compare | Compare last two runs (before/after config change) |
Workflow
1. Load Tests
Read state/eval/tests.json. If missing or empty, report an error.
2. Run Each Test
For each test case:
- Send the
prompt to the agent via pi.sendUserMessage
- Observe every tool call the agent makes
- Track the total turn count
- Capture the final response text
3. Score Each Test
Compute three sub-scores and combine them:
Tool accuracy (0-1): |expected_tools ∩ used_tools| / |expected_tools|. Full credit when every expected tool was called at least once. Extra tools not in expected_tools do not penalise this score.
Turn efficiency (0-1): 1.0 if turns <= max_turns, else max_turns / turns. Rewards completing the task within the turn budget.
Output quality (0-0.5, manual): Heuristic or manual assessment against success_criteria. Check that the response addresses the prompt fully and the tools were used for their intended purpose. 0 = irrelevant/hallucinated, 0.25 = partially correct, 0.5 = fully correct.
Total: min(tool_accuracy + turn_efficiency + output_quality, 1.0). Clamped to 1.0.
A test passes when its total score is >= 0.7.
4. Write Results
Save results to state/eval/results_{timestamp}.json with the format below. Also write a symlink or pointer at state/eval/latest.json so /eval report finds the most recent run quickly.
5. Compare Runs
/eval compare loads the two most recent result files and computes a delta:
read-file: 0.95 -> 0.88 ▼ -0.07
search-json: 0.80 -> 1.00 ▲ +0.20
web-search: 0.75 -> 0.75 = unchanged
Also print an overall summary:
Avg score: 0.83 -> 0.88 ▲ +0.05
Pass rate: 3/5 -> 4/5 ▲ +1
Highlight regressions (score drop >= 0.10) with a warning.
Result Format
{
"run_id": "eval_20260731_140000",
"timestamp": "2026-07-31T14:00:00Z",
"config_snapshot": {
"model": "claude-sonnet-4-20250514",
"skills_loaded": ["eval"]
},
"tests": [
{
"id": "read-file",
"passed": true,
"tools_used": ["read"],
"turns": 2,
"score": 0.95,
"sub_scores": {
"tool_accuracy": 1.0,
"turn_efficiency"
