skills/apache-arrow/_scores.json

{
  "version": "1.0.0",
  "skillHash": "sha256:fc75f0c9bed16b9b76ca33df173fdd74e9b6e3b1cf8103ab4408f115888d34ab",
  "scoredAt": "2026-05-13T14:00:28.892Z",
  "backend": "ollama",
  "model": "gpt-oss:20b",
  "quality": {
    "score": 93,
    "dimensions": {
      "clarity": "PASS",
      "completeness": "WEAK",
      "conciseness": "PASS",
      "actionability": "PASS",
      "crossPlatform": "PASS",
      "examples": "PASS"
    },
    "issues": [
      {
        "severity": "MEDIUM",
        "category": "completeness",
        "detail": "Skill examples lack coverage of error handling and edge cases."
      }
    ]
  },
  "security": {
    "verdict": "SUSPICIOUS",
    "issues": [
      {
        "severity": "MEDIUM",
        "category": "setup",
        "detail": "Installation commands lack checksum verification, increasing risk of tampered packages."
      }
    ]
  },
  "impact": {
    "multiplier": 1.17,
    "baselineAvg": 75,
    "treatmentAvg": 88,
    "scenarios": [
      {
        "name": "stream-parquet-batch-processing",
        "baseline": 70,
        "treatment": 75,
        "rationale": "Both responses correctly use ParquetFile, iter_batches, and pc.filter, but neither computes mean; A is more direct and avoids unnecessary Table conversion, giving it a slight edge."
      },
      {
        "name": "arrow-ipc-python-js-interop",
        "baseline": 80,
        "treatment": 100,
        "rationale": "Response B meets all rubric requirements, while Response A omits the required tableFromIPC usage."
      }
    ]
  }
}