Agent skill · data analytics · erichowens
skill-logger
Logs and scores skill usage quality, tracking output effectiveness, user satisfaction signals, and improvement opportunities. Expert in skill analytics, quality metrics, feedback loops, and
Why this skill is useful
Provides specific metrics and logging procedures for skill performance analysis that the AI wouldn't reliably generate on its own.
What it needs
About 6k tokens when loaded. Last updated 2026-07-14. 180 stars on the source repository.
What this skill does
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 What to Log Invocation Data Quality Signals Scoring Framework Multi-Dimensional Quality Score 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) JSON Log Format (Portable) Analytics Queries Skill Performance Dashboard Improvement Opportunities Implementation Guide Basic Logger Hook Quality Signal Collection Alerting & Notifications Alert Conditions 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. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills erichowens/skill-logger