Build your first Claude Code app

free beta with Pawgrammer

Skill Performance Profiler

Optimize skill usage by tracking token consumption, measuring invocation frequency, and identifying consolidation opportunities across conversations.

aidevelopment

Skill Performance Profiler

A comprehensive analysis tool for optimizing skill usage by tracking token consumption, measuring invocation frequency, and identifying consolidation opportunities across conversations.

This skill is ideal for users who want to understand and optimize their skill usage patterns, reduce token waste, and improve overall efficiency.

Core Purpose

The Skill Performance Profiler helps you:

  • Track token consumption across all skills
  • Measure invocation frequency to find heavily-used patterns
  • Identify consolidation opportunities for related skills
  • Optimize resource usage based on actual data
  • Make informed decisions about skill management

Three Key Capabilities

1. Data Analysis & Metrics

Gathers conversation history and processes it to extract:

  • Token consumption per skill
  • Invocation counts and frequency
  • Usage patterns over time
  • Co-occurrence data for skill pairs

Skills are categorized into four performance tiers:

  • Lightweight - Under 500 tokens average
  • Medium - 500-2,000 tokens
  • Heavy - 2,000-5,000 tokens
  • Very Heavy - Over 5,000 tokens

2. Usage Pattern Detection

Identifies optimization opportunities:

  • Co-occurrence patterns - Skills used together
  • Consolidation candidates - Skills used together ≥50% of the time
  • Redundant invocations - Overlapping functionality
  • Workflow sequences - Common skill chains

3. Flexible Reporting

Multiple output formats for different needs:

  • Inline summaries - Quick answers to specific questions
  • Markdown reports - Detailed analysis documents
  • CSV exports - Spreadsheet-compatible data
  • Interactive visualizations - Trend analysis and charts

Five-Step Analysis Workflow

Sequential Process:

  1. Collect - Gather conversation data via recent_chats tool
  2. Prepare - Format data as JSON for analysis
  3. Execute - Run analysis script on conversation history
  4. Generate - Create formatted reports
  5. Present - Deliver results in appropriate format

Performance Tier Categories

Skills are automatically classified:

Lightweight Skills (< 500 tokens)

Efficient, low-overhead capabilities:

  • Quick reference lookups
  • Simple transformations
  • Basic queries
  • Optimization: Use freely

Medium Skills (500-2,000 tokens)

Standard complexity operations:

  • Moderate analysis tasks
  • Standard workflows
  • Common transformations
  • Optimization: Monitor for consolidation

Heavy Skills (2,000-5,000 tokens)

Complex, resource-intensive tasks:

  • Deep analysis
  • Multi-step workflows
  • Comprehensive processing
  • Optimization: Use strategically

Very Heavy Skills (> 5,000 tokens)

Extremely resource-intensive operations:

  • Massive data processing
  • Complex multi-stage workflows
  • Extensive analysis
  • Optimization: Critical to optimize

Consolidation Detection

Consolidation Candidates:

Skills used together ≥50% of the time may benefit from merging:

  • Reduces total token consumption
  • Simplifies workflow management
  • Improves consistency
  • Streamlines invocation

The profiler identifies these patterns automatically.

Report Formats

Inline Summaries

Quick answers to questions like:

  • "Which skill uses the most tokens?"
  • "How often do I use skill X?"
  • "What skills are typically used together?"

Markdown Reports

Detailed analysis documents including:

  • Executive summary
  • Performance breakdown by skill
  • Usage frequency charts
  • Consolidation recommendations
  • Trend analysis over time

CSV Exports

Spreadsheet-compatible data for:

  • Custom analysis
  • Long-term tracking
  • Integration with other tools
  • Historical comparisons

Interactive Visualizations

Dynamic charts showing:

  • Token consumption trends
  • Invocation frequency patterns
  • Co-occurrence networks
  • Performance distribution

Important Limitation

Token Estimation:

Token counts use a 4:1 character-to-token ratio estimation.

This provides useful relative comparisons but doesn't reflect actual API tokenization precisely. Use for comparative analysis, not absolute measurements.

Practical Applications

Usage Optimization

  • Identify token-heavy skills to optimize
  • Find underutilized skills to remove
  • Detect frequently-paired skills to consolidate

Workflow Analysis

  • Understand common skill sequences
  • Optimize multi-skill workflows
  • Identify bottlenecks

Resource Planning

  • Predict token consumption patterns
  • Plan skill deployment strategies
  • Budget context window usage

Performance Tuning

  • Track improvements over time
  • Measure optimization impact
  • Validate consolidation benefits

Repository Resources

The repository includes analysis scripts, data collection tools, reporting templates, visualization generators, and consolidation detection algorithms.

Visit the Skill Performance Profiler repository for complete profiling tools.

About This Skill

This skill was created by Nate Jones as part of his comprehensive Nate's Substack Skills collection. Learn more about Nate's work at Nate's Newsletter.

Explore the full collection to discover all 10+ skills designed to enhance your Claude workflows!

Optimize skill usage by tracking token consumption, measuring invocation frequency, and identifying consolidation opportunities across conversations.