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skill-create: drop the "Example Output" section (53 lines) — it re-rendered the same skeleton already defined by the Step 3 output template, just with filled-in `my-app` values. learn-eval: drop the "Next Action" column from the 5b verdict table — it duplicated Step 6's "Verdict-specific confirmation flow". The table now carries Verdict + Meaning, and a pointer to Step 6 as the single source for each verdict's action. No behavior, frontmatter, or design-rationale changes.
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name, description, allowed_tools
| name | description | allowed_tools | |||||
|---|---|---|---|---|---|---|---|
| skill-create | Analyze local git history to extract coding patterns and generate SKILL.md files. Local version of the Skill Creator GitHub App. |
|
/skill-create - Local Skill Generation
Analyze your repository's git history to extract coding patterns and generate SKILL.md files that teach Claude your team's practices.
Usage
/skill-create # Analyze current repo
/skill-create --commits 100 # Analyze last 100 commits
/skill-create --output ./skills # Custom output directory
/skill-create --instincts # Also generate instincts for continuous-learning-v2
What It Does
- Parses Git History - Analyzes commits, file changes, and patterns
- Detects Patterns - Identifies recurring workflows and conventions
- Generates SKILL.md - Creates valid Claude Code skill files
- Optionally Creates Instincts - For the continuous-learning-v2 system
Analysis Steps
Step 1: Gather Git Data
# Get recent commits with file changes
git log --oneline -n ${COMMITS:-200} --name-only --pretty=format:"%H|%s|%ad" --date=short
# Get commit frequency by file
git log --oneline -n 200 --name-only | grep -v "^$" | grep -v "^[a-f0-9]" | sort | uniq -c | sort -rn | head -20
# Get commit message patterns
git log --oneline -n 200 | cut -d' ' -f2- | head -50
Step 2: Detect Patterns
Look for these pattern types:
| Pattern | Detection Method |
|---|---|
| Commit conventions | Regex on commit messages (feat:, fix:, chore:) |
| File co-changes | Files that always change together |
| Workflow sequences | Repeated file change patterns |
| Architecture | Folder structure and naming conventions |
| Testing patterns | Test file locations, naming, coverage |
Step 3: Generate SKILL.md
Output format:
---
name: {repo-name}-patterns
description: Coding patterns extracted from {repo-name}
version: 1.0.0
source: local-git-analysis
analyzed_commits: {count}
---
# {Repo Name} Patterns
## Commit Conventions
{detected commit message patterns}
## Code Architecture
{detected folder structure and organization}
## Workflows
{detected repeating file change patterns}
## Testing Patterns
{detected test conventions}
Step 4: Generate Instincts (if --instincts)
For continuous-learning-v2 integration:
---
id: {repo}-commit-convention
trigger: "when writing a commit message"
confidence: 0.8
domain: git
source: local-repo-analysis
---
# Use Conventional Commits
## Action
Prefix commits with: feat:, fix:, chore:, docs:, test:, refactor:
## Evidence
- Analyzed {n} commits
- {percentage}% follow conventional commit format
GitHub App Integration
For advanced features (10k+ commits, team sharing, auto-PRs), use the Skill Creator GitHub App:
- Install: github.com/apps/skill-creator
- Comment
/skill-creator analyzeon any issue - Receives PR with generated skills
Related Commands
/instinct-import- Import generated instincts/instinct-status- View learned instincts/evolve- Cluster instincts into skills/agents
Part of Everything Claude Code