| name | skill-architect |
| description | Orchestrates complete skill lifecycle from creation to optimization. Use for comprehensive skill development, reviewing skills, or managing skill quality. |
| skills | skill-generator, skill-reviewer, skill-optimizer |
Skill Architect
Purpose
Meta-orchestrator for the complete Claude Code skill lifecycle. Manages skill creation, review, optimization, and quality assurance through coordinated subagent workflows.
When to Use
- User asks to "manage skills" or "oversee skill development"
- Complete skill lifecycle management
- Quality assurance for skill libraries
- Team skill development workflows
- Comprehensive skill audits
Available Subagents
This orchestrator coordinates three specialized skills:
- @skill-generator - Creates new skills from descriptions
- @skill-reviewer - Reviews skills for quality and best practices
- @skill-optimizer - Refactors skills using PDA
Workflows
Workflow 1: Create and Review
Use when: Creating a new skill with quality assurance
1. @skill-generator
- Generate SKILL.md from user description
- Create directory structure
- Apply best practices
2. @skill-reviewer
- Review generated skill
- Identify any issues
- Provide quality score
3. Report and Iterate (if needed)
- Present skill to user
- Address review feedback
- Finalize skill
Trigger phrases:
- "Create a skill for X and review it"
- "Generate and check a new skill"
- "Make a skill for X with quality check"
Workflow 2: Audit and Optimize
Use when: Improving existing skills
1. @skill-reviewer
- Review existing skill
- Identify improvement opportunities
- Calculate potential savings
2. @skill-optimizer
- Apply recommended improvements
- Implement PDA refactoring
- Optimize token usage
3. Validation
- Compare before/after
- Verify functionality preserved
- Report improvements
Trigger phrases:
- "Audit this skill and optimize it"
- "Review and improve this skill"
- "Check and fix this skill"
Workflow 3: Complete Lifecycle
Use when: End-to-end skill development
1. Requirements Gathering
- Understand skill purpose
- Identify use cases
- Determine scope
2. @skill-generator
- Create initial skill
- Structure content
- Add examples
3. @skill-reviewer
- Quality assessment
- Best practices check
- Improvement recommendations
4. @skill-optimizer (if needed)
- Apply optimizations
- Implement PDA
- Reduce token usage
5. Final Review
- Validate improvements
- Confirm quality standards
- Deliver production-ready skill
Trigger phrases:
- "Create a production-ready skill for X"
- "Develop a complete skill from scratch"
- "Build and validate a skill"
Workflow 4: Skill Library Audit
Use when: Reviewing multiple skills
1. Discover Skills
- Find all skills in .claude/skills/
- List each skill with metadata
2. @skill-reviewer (parallel)
- Review each skill
- Generate quality scores
- Identify common issues
3. Aggregate Report
- Overall quality assessment
- Prioritized improvements
- Token savings opportunities
4. Recommendations
- Which skills need optimization
- Which skills are excellent
- Best practices to share
Trigger phrases:
- "Audit all my skills"
- "Review my skill library"
- "Check all skills for quality"
Output Format
For Creation Workflows
# Skill Creation Complete
## Skill Overview
**Name:** [skill-name]
**Location:** .claude/skills/[skill-name]/
**Size:** [X] KB
## Quality Assessment
**Score:** [X/10]
**PDA Compliance:** Yes/No
**Status:** Ready for use / Needs improvements
## What Was Created
1. SKILL.md - Main orchestrator
2. reference/ - Detailed documentation
3. scripts/ - Utility scripts (if applicable)
## Usage
Invoke with: [example invocation]
## Next Steps
- [ ] Test the skill
- [ ] Adjust if needed
- [ ] Add to version control
For Optimization Workflows
# Skill Optimization Complete
## Summary
**Skill:** [skill-name]
**Original Size:** [X] KB
**Optimized Size:** [Y] KB
**Token Savings:** [Z]%
## Improvements Made
### Structure
- [Changes]
### Content
- [Changes]
### Token Efficiency
- [Changes]
## Quality Improvements
**Before:** Score [X]/10
**After:** Score [Y]/10
## Cost Impact
At 100 requests/day:
- Before: $[cost]/year
- After: $[new cost]/year
- Saved: $[savings]/year
## Validation
✅ Functionality preserved
✅ All links work
✅ Performance improved
For Audit Workflows
# Skill Library Audit Report
## Overview
**Total Skills:** [N]
**Average Score:** [X]/10
**Skills Needing Work:** [N]
## Skills by Quality
### Excellent (9-10)
- [skill-1]
- [skill-2]
### Good (7-8)
- [skill-3]
- [skill-4]
### Needs Improvement (5-6)
- [skill-5]
- [skill-6]
### Poor (1-4)
- [skill-7]
- [skill-8]
## Common Issues Found
1. [Issue] - [Count] skills affected
2. [Issue] - [Count] skills affected
## Prioritized Recommendations
### P1 (Critical)
- [Recommendations]
### P2 (Important)
- [Recommendations]
### P3 (Nice-to-Have)
- [Recommendations]
## Token Optimization Opportunity
**Current Usage:** [X] MB/day
**Potential Savings:** [Y] MB/day ([Z]%)
**Annual Cost Savings:** $[amount]
## Next Steps
1. Address P1 issues
2. Optimize skills with low scores
3. Apply best practices to all skills
Quality Gates
Each workflow includes quality checks:
Creation Quality Gates
Optimization Quality Gates
Review Quality Gates
Best Practices Applied
This orchestrator ensures:
-
Progressive Disclosure
- Skills use PDA when appropriate
- Token-efficient design
- On-demand loading
-
Quality Standards
- Consistent structure
- Clear documentation
- Actionable examples
-
Best Practices
- Gerund naming
- Third-person descriptions
- Forward slash paths
- Single responsibility
-
Token Optimization
- Minimal SKILL.md size
- Reference files for details
- Scripts for mechanical work
Integration
This orchestrator works with:
- skill-generator - For skill creation
- skill-reviewer - For quality assessment
- skill-optimizer - For improvement
Advanced Features
Batch Operations
Process multiple skills:
> Audit and optimize all skills in .claude/skills/
# Runs @skill-reviewer on each
# Runs @skill-optimizer on those needing improvement
# Generates aggregate report
Skill Templates
Create from templates:
> Create a generator skill following the generator template
# Uses predefined pattern
# Applies best practices automatically
# Delivers consistent structure
Continuous Improvement
Ongoing quality management:
> Set up skill quality monitoring
# Establishes quality baseline
# Recommends regular reviews
# Tracks improvements over time
Examples
Example 1: Create Complete Skill
User: "Create a production-ready skill for processing JSON files with validation and transformation"
Workflow:
1. @skill-generator
Creates: json-processor/
├── SKILL.md (4KB orchestrator)
├── reference/
│ ├── json-schema.md
│ ├── transformations.md
│ └── validation.md
└── scripts/
├── validate.py
└── transform.py
2. @skill-reviewer
Score: 9/10
PDA Compliance: Yes
Status: Ready for use
3. Report
Skill created and validated
All quality gates passed
Ready for production use
Example 2: Optimize Poor Skill
User: "This 50KB skill is slow and expensive. Fix it."
Workflow:
1. @skill-reviewer
Score: 3/10
Issues: Monolithic, no PDA, token-inefficient
Recommendation: Major refactor
2. @skill-optimizer
Before: 50KB SKILL.md
After: 3KB SKILL.md + reference/ files
Savings: 84%
3. Validation
Functionality: Preserved
Performance: 5x faster
Cost: $12/day → $2/day
Example 3: Library Audit
User: "Review all my skills and tell me what needs improvement"
Workflow:
1. Discovery
Found 12 skills
2. @skill-reviewer (parallel)
3 Excellent (9-10)
5 Good (7-8)
3 Fair (5-6)
1 Poor (2)
3. @skill-optimizer (for 4 low-scoring skills)
Average improvement: 73% token reduction
4. Report
Overall quality improved from 6.5 to 8.2
Projected annual savings: $1,200
See Also
Sources
Based on:
