Agent Skills: Skill Evolution Tracker

Track skill changes, improvements, and evolution over time. Task-based operations for version tracking, change documentation, impact measurement, and evolution analysis. Use when tracking skill versions, documenting changes over time, measuring improvement impact, or analyzing how skills evolved.

UncategorizedID: adaptationio/skrillz/skill-evolution-tracker

Install this agent skill to your local

pnpm dlx add-skill https://github.com/adaptationio/Skrillz/tree/HEAD/.claude/skills/skill-evolution-tracker

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.claude/skills/skill-evolution-tracker/SKILL.md

Skill Metadata

Name
skill-evolution-tracker
Description
Track skill changes, improvements, and evolution over time. Task-based operations for version tracking, change documentation, impact measurement, and evolution analysis. Use when tracking skill versions, documenting changes over time, measuring improvement impact, or analyzing how skills evolved.

Skill Evolution Tracker

Overview

skill-evolution-tracker documents and analyzes how skills change and improve over time, providing visibility into evolution patterns and improvement effectiveness.

Purpose: Track and understand skill evolution for continuous improvement insights

The 4 Tracking Operations:

  1. Version Tracking - Document skill versions and changes
  2. Change Documentation - Record what changed and why
  3. Impact Measurement - Quantify improvement effects
  4. Evolution Analysis - Identify patterns in how skills evolve

When to Use

  • After skill updates (document changes)
  • Version releases (track versions)
  • Analyzing improvement effectiveness
  • Understanding skill maturity
  • Identifying evolution patterns

Operations

Operation 1: Version Tracking

Purpose: Maintain version history for skills

Process:

  1. Assign version numbers (semver: v1.0, v1.1, v2.0)
  2. Document version in YAML frontmatter
  3. Track major changes per version
  4. Maintain changelog

Format:

## Version History

### v1.1 (2025-11-07)
- Added Quick Reference section
- Enhanced examples in Operations
- Fixed validation criteria

### v1.0 (2025-10-15)
- Initial release

Operation 2: Change Documentation

Purpose: Record what changed, why, and impact

Process:

  1. List all changes made
  2. Explain rationale for each
  3. Note expected impact
  4. Document validation results

Format:

### Change: Added Quick Reference

**Rationale**: User experience improvement, easier memory refresh
**Changes**: Added Quick Reference section (96 lines)
**Impact**: Improved usability, 100% Quick Ref coverage
**Validation**: Passed structure validation, Quick Ref detected

Operation 3: Impact Measurement

Purpose: Quantify effectiveness of improvements

Metrics:

  • Quality score changes (before/after review-multi scores)
  • Usage frequency changes
  • User satisfaction changes
  • Efficiency improvements

Example:

Impact Measurement: Quick Reference Addition

Before:
- Quick Reference: 4 of 7 skills (57%)
- User feedback: "Hard to refresh memory"

After:
- Quick Reference: 7 of 7 skills (100%)
- Validation: All pass Quick Ref check
- Impact: Improved UX, consistency

Quantified:
- Coverage: 57% → 100% (+43%)
- Time to refresh: ~30min → ~5min (-83%)

Operation 4: Evolution Analysis

Purpose: Identify patterns in skill evolution

Process:

  1. Review version histories
  2. Identify common evolution patterns
  3. Understand what drives changes
  4. Extract meta-learnings

Example Patterns:

  • Skills often add Quick Reference in v1.1
  • Examples get refined/made concrete
  • Validation criteria become more specific over time

Quick Reference

| Operation | Focus | Output | |-----------|-------|--------| | Version Tracking | Version numbers, changelog | Version history | | Change Documentation | What/why/impact of changes | Change log | | Impact Measurement | Quantify improvements | Metrics, evidence | | Evolution Analysis | Patterns across skills | Meta-learnings |

Integration: best-practices-learner (uses evolution insights for learnings)


skill-evolution-tracker provides visibility into how skills improve over time, enabling data-driven understanding of what makes skills better.