The Challenge
Writing high-quality Claude Code skills manually takes 2 hours and costs ~$87 in labor. Quality varies wildly. Expert authors hit 80-90/100, juniors produce 40-60/100. There's no validation, so bad skills ship.
I kept repeating the same skill-writing patterns. Research the domain, structure the YAML, write documentation, validate constraints. Every time slightly different, every time time-consuming.
The question: could I automate skill generation while actually improving quality?
The Approach
Instead of a simple generator, I built a 9-stage pipeline with quality gates at every step:
Stage 0: Viability Check - Rejects low-quality requests before wasting tokens Stage 2: Constraint Validation - Detects overpromising and forces honest capability assessment Stage 4: Auto-Fix Loop - Iterates until 6 deterministic validation rules pass Stage 5: Final Validation - YAML structure, safe characters, naming conventions
The key insight: validation gates are more important than generation quality. You can always regenerate, but you can't ship bad skills.
The Solution
Pipeline Architecture - 9 stages with parallel processing where possible - Haiku for structured tasks (50% of tokens, cheap) - Sonnet for creative tasks (50% of tokens, quality) - Auto-fix loop with up to 3 iterations - 6-rule validation system
Output Package - SKILL.md: 10.2KB with complete YAML frontmatter - README.md: Installation and usage instructions - Metadata: Properly formatted and validated - ZIP: Ready for immediate installation
Cost Optimization - Model routing by task type - Token-efficient prompts - Retry logic with exponential backoff - ~24,400 tokens per generation
The Outcome
The numbers speak for themselves:
| Metric | Manual | Factory |
|---|---|---|
| Time | 2 hours | 7.5 min |
| Cost | $87.08 | $0.20 |
| Quality | 40-90 | 100/100 |
| Consistency | Variable | Perfect |
ROI: 43,440% - For every $0.20 spent, $86.88 in value returned.
At 100 skills: saves $9,315 and 174 hours. At 1,000 skills: saves $93,150 and 1,742 hours.
What I Learned
The meta-insight: automation that includes validation beats manual work that doesn't. The factory isn't just faster. It's more reliable because bad outputs can't escape the pipeline.
Also learned: model routing matters. Using Haiku for structured extraction and Sonnet for creative generation cuts costs 60% without quality loss.
The factory paid for itself after ~10 skills. Everything after that is pure ROI.