skill-logger

Skill Logger

Track, measure, and improve skill quality through systematic logging and scoring.

When to Use This Skill

Use for:

• Setting up skill usage logging
• Defining quality metrics for skill outputs
• Analyzing skill performance over time
• Identifying skills that need improvement
• Building feedback loops for skill enhancement
• A/B testing skill variations

NOT for:

• Creating new skills → use agent-creator
• Skill documentation → use skill-coach
• Runtime debugging → use appropriate debugger skills
• General logging/monitoring → use devops-automator

Core Logging Architecture

┌────────────────────────────────────────────────────────────────┐
│                    SKILL LOGGING PIPELINE                       │
├────────────────────────────────────────────────────────────────┤
│                                                                 │
│  1. CAPTURE          2. ANALYZE           3. SCORE              │
│  ├─ Invocation       ├─ Output parse      ├─ Quality metrics    │
│  ├─ Input context    ├─ Token usage       ├─ User satisfaction  │
│  ├─ Output           ├─ Tool calls        ├─ Goal completion    │
│  └─ Timing           └─ Error patterns    └─ Efficiency         │
│                                                                 │
│  4. AGGREGATE        5. ALERT             6. IMPROVE            │
│  ├─ Per-skill stats  ├─ Quality drops     ├─ Identify patterns  │
│  ├─ Trend analysis   ├─ Error spikes      ├─ Suggest changes    │
│  └─ Comparisons      └─ Underuse          └─ Track experiments  │
│                                                                 │
└────────────────────────────────────────────────────────────────┘

What to Log

Invocation Data

{
  "invocation_id": "uuid",
  "timestamp": "ISO8601",
  "skill_name": "wedding-immortalist",
  "skill_version": "1.2.0",

  "input": {
    "user_query": "Create a 3D model from my wedding photos",
    "context_tokens": 1500,
    "files_referenced": ["photos/", "config.json"]
  },

  "execution": {
    "duration_ms": 45000,
    "tool_calls": [
      {"tool": "Bash", "count": 5},
      {"tool": "Write", "count": 3}
    ],
    "tokens_used": {
      "input": 8500,
      "output": 3200
    },
    "errors": []
  },

  "output": {
    "type": "code_generation",
    "artifacts_created": ["pipeline.py", "config.yaml"],
    "response_length": 3200
  }
}