Skill Harvester

Transform your past work into reusable Claude Code skills automatically.

Overview

The Skill Harvester is a meta-skill that helps you systematically extract reusable patterns, workflows, and expertise from your Claude Code sessions and convert them into well-structured skills that can be shared and reused.

When to Use

Use this skill when:

• Completing a significant project or work session
• Identifying repetitive patterns across multiple sessions
• Building organizational knowledge repositories
• Creating team-wide skill libraries
• Documenting complex workflows for future reuse
• Converting infrastructure/tooling expertise into skills
• After solving complex problems that could help future work

Skill Harvesting Process

1. Reflection & Analysis

Examine recent work:

# Review recent git commits
git log --oneline -20

# Analyze file changes
git diff HEAD~10..HEAD --stat

# Check which files were most modified
git log --pretty=format: --name-only | sort | uniq -c | sort -rg | head -20

Identify domains:

• What technologies were used? (frameworks, languages, tools)
• What problems were solved? (deployment, testing, optimization)
• What patterns emerged? (error handling, API integration, workflow)
• What expertise was developed? (domain knowledge, best practices)

2. Skill Identification

Questions to ask:

1. Reusability: Could this help in future projects?
2. Generalizability: Does it apply beyond this specific context?
3. Complexity: Is it non-trivial enough to warrant a skill?
4. Value: Would others benefit from this pattern?
5. Completeness: Can it be documented as a standalone skill?

Skill categories to consider:

• Infrastructure: Docker, Kubernetes, cloud platforms, CI/CD
• Backend: API design, database optimization, authentication
• Frontend: Component patterns, state management, build optimization
• DevOps: Deployment strategies, monitoring, automation
• Data Engineering: ETL pipelines, data validation, transformation
• Security: Auth patterns, encryption, vulnerability scanning
• Testing: Test strategies, mocking, coverage analysis
• Documentation: API docs, architecture diagrams, runbooks

3. Skill Template Creation

Essential components of a good skill:

---
name: skill-name (kebab-case)
description: Clear, concise 1-2 sentence description of what the skill does
license: MIT
tags: [relevant, searchable, tags]
---

# Skill Title

Brief overview paragraph explaining the skill's purpose and value.

## When to Use

- Specific scenario 1
- Specific scenario 2
- Specific scenario 3

## Core Concepts

Explain the fundamental ideas and principles.

## Workflow

Step-by-step process for using the skill.

### Step 1: [Action]
Detailed explanation with code examples.

### Step 2: [Action]
More details and examples.

## Common Patterns

### Pattern 1: [Name]
Description and example.

### Pattern 2: [Name]
Description and example.

## Best Practices

### ✅ DO
- Recommended approach 1
- Recommended approach 2

### ❌ DON'T
- Anti-pattern 1
- Anti-pattern 2

## Examples

### Example 1: [Scenario]
Full working example with explanation.

### Example 2: [Scenario]
Another complete example.

## Troubleshooting

### Issue 1: [Problem]
**Symptoms**: What you see
**Cause**: Why it happens
**Solution**: How to fix

## Reference

Quick reference table or cheat sheet.

## Additional Resources

- Links to relevant documentation
- Related skills
- External references

4. Content Extraction

Extract from various sources:

# From code files
# Look for:
# - Complex functions that solve specific problems
# - Utility scripts with general applicability
# - Configuration patterns that work well
# - Error handling strategies
# - Integration patterns

# From documentation
# Harvest:
# - README instructions
# - Setup guides
# - Troubleshooting notes
# - Architecture decisions
# - Lessons learned

# From git commits
git log --all --grep="fix\|feat\|refactor" --pretty=format:"%h %s" -20

# From issue trackers
# Extract:
# - Common problems and solutions
# - Debugging strategies
# - Workarounds and fixes

5. Skill Organization

Directory structure:

harvestable_skills/
├── automation/
│   ├── skill-name/
│   │   └── skill.md
├── backend/
├── devops/
├── frontend/
├── infrastructure/
├── testing/
└── documentation/

Categorization guidelines:

• automation: Workflow automation, scripting, batch operations
• backend: Server-side development, APIs, databases
• cloud: Cloud platforms, serverless, infrastructure
• data-engineering: Data processing, ETL, analytics
• devops: CI/CD, deployment, monitoring
• documentation: Documentation generation, diagrams
• frontend: UI development, client-side frameworks
• infrastructure: Container orchestration, VMs, networking
• security: Authentication, authorization, encryption
• testing: T

...