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erichowens
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skill-logger

Log and score skill usage quality with a defined invocation data schema.

181|30|Updated Nov 16, 2025
Search Tags:#analytics#logging#metrics#quality#improvement
One-click install
npx skills add https://github.com/erichowens/some_claude_skills --skill skill-logger
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: skill-logger
Source: https://github.com/erichowens/some_claude_skills/tree/main/.claude/skills/skill-logger
Command: npx skills add https://github.com/erichowens/some_claude_skills --skill skill-logger

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Logs and scores skill usage quality, enabling data-driven improvement of outputs, capturing user satisfaction signals, and surfacing opportunities for enhancement.

Core Features & Use Cases

  • Quality Metrics Logging: Capture invocation details, timing, and outcomes for each skill use.
  • Output Scoring: Compute multi-dimensional quality scores to guide iteration.
  • Feedback Loops: Build data-driven loops for continuous skill refinement.
  • Experiment Tracking: Compare variations (A/B tests) to optimize results.

Quick Start

Run this skill to start logging a sample invocation and review the resulting quality score.

Frequently Asked Questions about skill-logger

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track skill quality and measure invocation performance?▼

Skill quality tracking captures invocation details, timing, and outcomes for each execution. This Skill logs these metrics and computes multi-dimensional quality scores, enabling you to measure performance across analytics, QA, and continuous improvement workflows without manual instrumentation.

What data schema and scoring framework does this logging pipeline use?▼

The Skill delivers a defined invocation data schema and multi-dimensional scoring rubric with implementation-ready scoring functions. This technical contract standardizes how quality signals and user feedback are captured and aggregated across skill executions.

Can I use this for A/B testing and experiment tracking?▼

Yes. The Skill supports experiment tracking by logging and scoring skill variations side-by-side. You can compare outputs and user satisfaction signals across test conditions to identify which variations optimize results.

How do I build feedback loops to improve skill outputs over time?▼

Log invocations with quality signals and user feedback, then use the scoring functions to compute improvement opportunities. This data-driven feedback loop surfaces areas for enhancement and guides iterative refinement of skill performance.

What makes this better than manually tracking metrics across skill executions?▼

Manual tracking is fragmented and incomplete. This Skill automates capture, analysis, scoring, and aggregation in a single pipeline, delivering consistent quality metrics, alert generation, and actionable insights without custom instrumentation per skill.

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