Use when you have a written implementation plan to execute in a separate session with review checkpoints
npx skills add phrazzld/claude-config --skill "skill-builder"
Install specific skill from multi-skill repository
# Description
|
# SKILL.md
name: skill-builder
description: |
Create new Claude Code skills from procedural knowledge. Invoke PROACTIVELY when:
- Discovering a reusable workflow pattern worth preserving
- User asks to "capture this as a skill" or "make this reusable"
- Recognizing institutional knowledge that should persist
- After solving a problem in a novel way worth repeating
- Noticing repeated patterns across sessions
AUTONOMOUS: Create skills proactively, then inform user what was created.
Skill Builder
Build new Claude Code skills that capture procedural knowledge.
Autonomy Model
Create then inform: When recognizing skill-worthy knowledge, create the skill proactively, then inform the user what was created and why. Don't ask permission first.
When to Invoke (Proactively)
- Successful novel solution - Just solved something in a way worth repeating
- Repeated pattern - Noticed doing the same thing multiple times
- Institutional knowledge - Learning domain-specific rules that should persist
- User workflow - User demonstrates a process they want automated
- Research findings - Discovered best practices worth preserving
Quality Gates (Pre-Extraction)
Before creating a skill, verify ALL gates pass:
| Gate | Question | Fail Criteria |
|---|---|---|
| REUSABLE | Applies beyond this instance? | One-off solution |
| NON-TRIVIAL | Required discovery, not docs lookup? | Just followed documentation |
| SPECIFIC | Clear trigger conditions defined? | Vague "sometimes useful" |
| VERIFIED | Solution confirmed working? | Theoretical, untested |
If ANY gate fails β Stop. Not skill-worthy.
Skill Creation Workflow
0. Research Best Practices
Before extracting, search for current patterns:
# Use Gemini CLI for web-grounded research
gemini "[technology] [feature] best practices 2026"
gemini "[technology] [problem type] official recommendations"
Why: Don't just codify what you did. Incorporate current best practices.
Skip if: Pattern is project-specific internal convention.
1. Identify the Knowledge
- What problem does this solve?
- What trigger terms would activate it?
- Is it cross-project or project-specific?
2. Draft Structure
Reference references/structure-guide.md for ideal anatomy.
3. Write Description
Reference references/description-patterns.md for trigger-rich descriptions (~100 words, explicit trigger terms).
4. Validate
Run scripts/validate_skill.py <skill-path> to check structure and frontmatter.
5. Inform User
After creating, tell user:
- What skill was created and why
- What triggers will activate it
- How to test it works
Progressive Disclosure
Keep SKILL.md lean (<100 lines). Put detailed specs in references/:
- Detailed examples β references/examples.md
- Edge cases β references/edge-cases.md
- Anti-patterns β references/anti-patterns.md
Code Opportunities
If skill involves deterministic operations (validation, parsing, extraction), create scripts/ with executable code rather than prose instructions. Scripts:
- Run without loading into context
- Must be executable (chmod +x)
- Should handle errors gracefully
Skill Locations
- Personal:
~/.claude/skills/- Available across all projects - Project:
.claude/skills/- Shared with team via git
Template
Use templates/SKILL-TEMPLATE.md as starting point for new skills.
# Supported AI Coding Agents
This skill is compatible with the SKILL.md standard and works with all major AI coding agents:
Learn more about the SKILL.md standard and how to use these skills with your preferred AI coding agent.