Agricidaniel Skill Forge Skill Forge

Skill Benchmarking & Performance Tracking

Measure and compare skill performance across iterations with statistical rigor using multiple trials, variance analysis, and trend tracking.

Process

Step 1: Define Benchmark Configuration

Accept configuration as:

• Existing eval set: Path to evals/evals.json (from /skill-forge eval)
• Benchmark config: Custom config with trial count and thresholds

Benchmark config schema:

{
  "skill_name": "my-skill",
  "skill_path": "./my-skill",
  "eval_set_path": "./evals/evals.json",
  "trials_per_eval": 3,
  "baseline_type": "no_skill",
  "previous_benchmark": null,
  "thresholds": {
    "min_pass_rate": 0.8,
    "max_avg_tokens": 100000,
    "max_avg_duration_seconds": 120,
    "min_improvement_ratio": 1.0
  }
}

Step 2: Execute Benchmark Runs

For each eval, run trials_per_eval times (default: 3) to get reliable metrics:

1. Execute with-skill runs (3x per eval)
2. Execute baseline runs (3x per eval)
3. Capture per-run: pass/fail, token count, duration
4. Save each run's timing.json and grading.json

Use agents/skill-forge-executor.md for parallel execution where possible.

Step 3: Aggregate Results

Run python scripts/aggregate_benchmark.py <workspace>/iteration-<N> --skill-name <name>:

Output benchmark.json schema:

{
  "skill_name": "my-skill",
  "iteration": 1,
  "timestamp": "2026-03-06T12:00:00Z",
  "summary": {
    "total_evals": 10,
    "with_skill": {
      "pass_rate": 0.87,
      "pass_rate_std": 0.05,
      "avg_tokens": 45000,
      "avg_duration_seconds": 34.2
    },
    "baseline": {
      "pass_rate": 0.60,
      "pass_rate_std": 0.08,
      "avg_tokens": 62000,
      "avg_duration_seconds": 52.1
    },
    "improvement_ratio": 1.45,
    "token_savings_ratio": 0.73,
    "time_savings_ratio": 0.66
  },
  "per_eval": [
    {
      "eval_id": 0,
      "eval_name": "basic-trigger",
      "with_skill": {"pass_rate": 1.0, "avg_tokens": 30000, "avg_duration_seconds": 20.1},
      "baseline": {"pass_rate": 0.67, "avg_tokens": 50000, "avg_duration_seconds": 45.0},
      "trials": 3
    }
  ],
  "thresholds_met": {
    "min_pass_rate": true,
    "max_avg_tokens": true,
    "max_avg_duration_seconds": true,
    "min_improvement_ratio": true
  }
}

Step 4: Compare with Previous Iterations

If previous_benchmark is provided or prior iteration-<N-1> exists:

1. Load previous benchmark.json
2. Calculate delta per metric:
   • Pass rate change
   • Token usage change
   • Duration change
   • New regressions (evals that passed before but fail now)
   • New improvements (evals that failed before but pass now)

Step 5: Generate Benchmark Report

# Benchmark Report: [skill-name]

## Iteration [N] vs [N-1]

### Summary
| Metric | Current | Previous | Delta | Threshold | Status |
|--------|---------|----------|-------|-----------|--------|
| Pass Rate | 87% | 78% | +9% | >= 80% | PASS |
| Avg Tokens | 45K | 52K | -13% | <= 100K | PASS |
| Avg Time | 34s | 41s | -17% | <= 120s | PASS |
| Improvement | 1.45x | 1.30x | +0.15x | >= 1.0x | PASS |

### Regressions (Action Required)
| Eval | Previous | Current | Notes |
|------|----------|---------|-------|
| eval-5 | PASS | FAIL | Output missing required section |

### Improvements
| Eval | Previous | Current | Notes |
|------|----------|---------|-------|
| eval-3 | FAIL | PASS | Error handling now works |

### Per-Eval Detail
[Full breakdown table]

### Variance Analysis
| Eval | Pass Rate | Std Dev | Trials | Reliability |
|------|-----------|---------|--------|-------------|
| eval-0 | 100% | 0.00 | 3 | High |
| eval-1 | 67% | 0.47 | 3 | Low (investigate) |

### Recommendations
[Based on regressions, low-reliability evals, and threshold failures]

Step 6: Threshold Gating

If any threshold fails:

1. Flag as FAIL with specific threshold details
2. List which evals caused the failure
3. Recommend running /skill-forge evolve to address issues
4. Do NOT approve for publish until thresholds pass

Error Handling

• Flaky trials: If a trial times out or crashes, exclude it from variance calculation and note "trials_completed" vs "trials_requested" in per-eval results
• Insufficient trials: If fewer than 2 trials complete for an eval, flag variance as "unreliable" in the report
• Missing baseline: If baseline runs fail entirely, report with-skill results only and skip improvement_ratio
• Threshold edge cases: If pass_rate equals the threshold exactly, treat as PASS

Integration with Other Sub-Skills

• skill-forge-eval: Provides the eval set and grading infrastructure
• skill-forge-evolve: Receives benchmark failures as improvement targets
• skill-forge-publish: Requires benchmark pass (score >= thresholds) before publish
• skill-forge-review: Can include benchmark summary in review report

Source: AgriciDaniel/skill-forge — distributed by TomeVault.