skills/ad-campaign-optimization/_scores.json

{
  "version": "1.0.0",
  "skillHash": "sha256:3fe2cfbff0c6089ce6fdd3912f826fc0f173746162030713a12c336e6d6bad73",
  "scoredAt": "2026-05-13T12:15:47.503Z",
  "backend": "ollama",
  "model": "gpt-oss:20b",
  "quality": {
    "score": 100,
    "dimensions": {
      "clarity": "PASS",
      "completeness": "PASS",
      "conciseness": "PASS",
      "actionability": "PASS",
      "crossPlatform": "PASS",
      "examples": "PASS"
    },
    "issues": []
  },
  "security": {
    "verdict": "SAFE",
    "issues": []
  },
  "impact": {
    "multiplier": 1.28,
    "baselineAvg": 57,
    "treatmentAvg": 73,
    "scenarios": [
      {
        "name": "multi-platform-campaign-structure",
        "baseline": 20,
        "treatment": 60,
        "rationale": "Response A covers the required campaign structure, CBO, exclusions, UTM links, and a creative brief, but fails to provide at least three ads per ad set and leaves retention ad sets incomplete; Response B lacks ad‑set detail, CBO, exclusions, UTM links, and a full creative brief."
      },
      {
        "name": "declining-roas-bid-optimization",
        "baseline": 75,
        "treatment": 95,
        "rationale": "Response A covers all rubric items with detailed actions, while Response B lacks explicit weekly targets and frequency control."
      },
      {
        "name": "cross-platform-attribution-framework",
        "baseline": 75,
        "treatment": 65,
        "rationale": "Response A provides a more comprehensive UTM scheme, holdout plan, and weekly template, but both lack an attribution model and explicit double‑counting adjustment; A covers more rubric items overall."
      }
    ]
  }
}