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
  }
}

Quality Signals

QUALITY_SIGNALS = {
    # Implicit signals (automated)
    'completion': 'Did the skill complete without errors?',
    'token_efficiency': 'Output quality per token used',
    'tool_success_rate': 'Tool calls that succeeded',
    'retry_count': 'How many retries needed?',

    # Explicit signals (user feedback)
    'user_edit_ratio': 'How much did user modify output?',
    'user_accepted': 'Did user accept/use the output?',
    'follow_up_needed': 'Did user need to ask for fixes?',
    'explicit_rating': 'Thumbs up/down if available',

    # Outcome signals (delayed)
    'code_ran_successfully': 'Did generated code work?',
    'tests_passed': 'Did it pass tests?',
    'reverted': 'Was the output later reverted?',
}

Scoring Framework

Multi-Dimensional Quality Score

def calculate_skill_score(invocation_log):
    """Score a skill invocation 0-100."""

    scores = {
        # Completion (25%)
        'completion': (
            25 if invocation_log['errors'] == [] else
            15 if invocation_log['recovered'] else
            0
        ),

        # Efficiency (20%)
        'efficiency': min(20, 20 * (
            BASELINE_TOKENS / invocation_log['tokens_used']
        )),

        # Output Quality (30%)
        'quality': (
            30 if invocation_log['user_accepted'] else
            20 if invocation_log['user_edit_ratio'] < 0.2 else
            10 if invocation_log['user_edit_ratio'] < 0.5 else
            0
        ),

        # User Satisfaction (25%)
        'satisfaction': (
            25 if invocation_log['explicit_rating'] == 'positive' else
            15 if invocation_log['no_follow_up'] else
            5 if invocation_log['follow_up_resolved'] else
            0
        ),
    }

    return sum(scores.values())

Score Interpretation

Score Range	Quality Level	Action
90-100	Excellent	Document as exemplar
75-89	Good	Monitor for consistency
50-74	Acceptable	Review for improvements
25-49	Poor	Prioritize fixes
0-24	Failing	Immediate intervention

Log Storage Schema

SQLite Schema (Local)

CREATE TABLE skill_invocations (
    id TEXT PRIMARY KEY,
    skill_name TEXT NOT NULL,
    skill_version TEXT,
    timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,

    -- Input
    user_query TEXT,
    context_tokens INTEGER,

    -- Execution
    duration_ms INTEGER,
    tokens_input INTEGER,
    tokens_output INTEGER,
    tool_calls_json TEXT,
    errors_json TEXT,

    -- Output
    output_type TEXT,
    artifacts_json TEXT,
    response_length INTEGER,

    -- Quality signals
    user_accepted BOOLEAN,
    user_edit_ratio REAL,
    follow_up_needed BOOLEAN,
    explicit_rating TEXT,

    -- Computed
    quality_score REAL,

    INDEX idx_skill_name (skill_name),
    INDEX idx_timestamp (timestamp),
    INDEX idx_quality (quality_score)
);

CREATE TABLE skill_aggregates (
    skill_name TEXT,
    period TEXT,  -- 'daily', 'weekly', 'monthly'
    period_start DATE,

    invocation_count INTEGER,
    avg_quality_score REAL,
    error_rate REAL,
    avg_tokens_used INTEGER,
    avg_duration_ms INTEGER,

    PRIMARY KEY (skill_name, period, period_start)
);

JSON Log Format (Portable)

{
  "logs_version": "1.0",
  "skill_name": "wedding-immortalist",
  "entries": [
    {
      "id": "uuid",
      "timestamp": "2025-01-15T14:30:00Z",
      "input": {...},
      "execution": {...},
      "output": {...},
      "quality": {
        "signals": {...},
        "score": 85,
        "computed_at": "2025-01-15T14:35:00Z"
      }
    }
  ]
}

Analytics Queries

Skill Performance Dashboard

-- Overall skill rankings
SELECT
    skill_name,
    COUNT(*) as uses,
    AVG(quality_score) as avg_quality,
    AVG(tokens_output) as avg_tokens,
    SUM(CASE WHEN errors_json != '[]' THEN 1 ELSE 0 END) * 100.0 / COUNT(*) as error_rate
FROM skill_invocations
WHERE timestamp > datetime('now', '-30 days')
GROUP BY skill_name
ORDER BY avg_quality DESC;

-- Quality trend (weekly)
SELECT
    skill_name,
    strftime('%Y-%W', timestamp) as week,
    AVG(quality_score) as avg_quality,
    COUNT(*) as uses
FROM skill_invocations
GROUP BY skill_name, week
ORDER BY skill_name, week;

-- Problem detection
SELECT skill_name, COUNT(*) as failures
FROM skill_invocations
WHERE quality_score < 50
  AND timestamp > datetime('now', '-7 days')
GROUP BY skill_name
HAVING failures >= 3
ORDER BY failures DESC;

Improvement Opportunities

def identify_improvement_opportunities(skill_name, logs):
    """Analyze logs to suggest skill improvements."""

    opportunities = []

    # Pattern 1: Common follow-up questions
    follow_ups = extract_follow_up_patterns(logs)
    if follow_ups:
        opportunities.append({
            'type': 'missing_capability',
            'description': f'Users frequently ask: {follow_ups[0]}',
            'suggestion': 'Add guidance for this common need'
        })

    # Pattern 2: High edit ratio in specific output types
    edit_patterns = analyze_edit_patterns(logs)
    if edit_patterns['code'] > 0.4:
        opportunities.append({
            'type': 'code_quality',
            'description': 'Users frequently edit generated code',
            'suggestion': 'Review code examples and templates'
        })

    # Pattern 3: Repeated errors
    error_patterns = cluster_errors(logs)
    for error_type, count in error_patterns:
        if count >= 3:
            opportunities.append({
                'type': 'recurring_error',
                'description': f'{error_type} occurred {count} times',
                'suggestion': 'Add error handling or documentation'
            })

    return opportunities

Implementation Guide

Basic Logger Hook

# hooks/skill_logger.py
import json
import sqlite3
from datetime import datetime
from pathlib import Path

LOG_DB = Path.home() / '.claude' / 'skill_logs.db'

def log_skill_invocation(
    skill_name: str,
    user_query: str,
    output: str,
    tool_calls: list,
    duration_ms: int,
    tokens: dict,
    errors: list = None
):
    """Log a skill invocation to the database."""

    conn = sqlite3.connect(LOG_DB)
    cursor = conn.cursor()

    cursor.execute('''
        INSERT INTO skill_invocations
        (id, skill_name, timestamp, user_query, duration_ms,
         tokens_input, tokens_output, tool_calls_json, errors_json,
         response_length)
        VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?)
    ''', (
        str(uuid.uuid4()),
        skill_name,
        datetime.utcnow().isoformat(),
        user_query,
        duration_ms,
        tokens.get('input', 0),
        tokens.get('output', 0),
        json.dumps(tool_calls),
        json.dumps(errors or []),
        len(output)
    ))

    conn.commit()
    conn.close()

Quality Signal Collection

def collect_quality_signals(invocation_id: str, signals: dict):
    """Update an invocation with quality signals."""

    conn = sqlite3.connect(LOG_DB)
    cursor = conn.cursor()

    # Update with user feedback
    cursor.execute('''
        UPDATE skill_invocations
        SET user_accepted = ?,
            user_edit_ratio = ?,
            follow_up_needed = ?,
            explicit_rating = ?,
            quality_score = ?
        WHERE id = ?
    ''', (
        signals.get('accepted'),
        signals.get('edit_ratio'),
        signals.get('follow_up'),
        signals.get('rating'),
        calculate_score(signals),
        invocation_id
    ))

    conn.commit()
    conn.close()

Alerting & Notifications

Alert Conditions

ALERT_CONDITIONS = {
    'quality_drop': {
        'condition': 'avg_quality_7d < avg_quality_30d * 0.8',
        'message': 'Skill {skill} quality dropped 20%+ in past week',
        'severity': 'warning'
    },
    'error_spike': {
        'condition': 'error_rate_24h > error_rate_7d * 2',
        'message': 'Skill {skill} error rate doubled in past 24h',
        'severity': 'critical'
    },
    'underused': {
        'condition': 'uses_7d < uses_30d_avg * 0.5',
        'message': 'Skill {skill} usage down 50%+ this week',
        'severity': 'info'
    },
    'high_performer': {
        'condition': 'avg_quality_7d > 90 AND uses_7d > 10',
        'message': 'Skill {skill} performing excellently',
        'severity': 'positive'
    }
}

Anti-Patterns

"Log Everything"

Wrong: Logging complete input/output for every invocation. Why: Privacy concerns, storage explosion, noise. Right: Log metadata, summaries, and opt-in detailed logging.

"Score Once, Forget"

Wrong: Calculating quality score immediately after completion. Why: Misses delayed signals (did code work? was it reverted?). Right: Collect signals over time, recalculate periodically.

"Averages Only"

Wrong: Only tracking average quality scores. Why: Hides distribution, misses failure modes. Right: Track percentiles, failure rates, and patterns.

"No Baseline"

Wrong: Measuring quality without establishing baselines. Why: Can't detect improvement or regression. Right: Establish baselines per skill, compare trends.

Output Reports

Weekly Skill Health Report

# Skill Health Report - Week of 2025-01-13

## Overview
- Total invocations: 247
- Average quality: 78.3 (up 2.1 from last week)
- Error rate: 4.2% (down 1.8%)

## Top Performers
1. **wedding-immortalist** - 92.1 avg quality, 18 uses
2. **skill-coach** - 89.4 avg quality, 34 uses
3. **api-architect** - 87.2 avg quality, 22 uses

## Needs Attention
1. **legacy-code-converter** - 52.3 avg quality (down 15%)
   - Common issue: Missing dependency detection
   - Suggested fix: Add dependency scanning step

## Improvement Opportunities
- `partner-text-coach`: Users frequently ask for tone adjustment
- `yard-landscaper`: High edit ratio on plant recommendations

Integration Points

• skill-coach: Feed quality data for skill improvements
• agent-creator: Use metrics when designing new skills
• automatic-stateful-prompt-improver: Quality signals for prompt optimization

---

Core Philosophy: What gets measured gets improved. Skill logging transforms intuition about skill quality into actionable data, enabling continuous improvement of the entire skill ecosystem.