- name
- frankenstein
- version
- 1.2.0
- description
- Combine the best parts of multiple skills into one. Searches ClawHub, GitHub, skills.sh, skillsmp.com and other AI skill repos. Analyzes each safely, compares features, and builds a combined 'Frankenstein' skill with the best of each. Uses skill-auditor for security scanning and sandwrap for safe analysis. Use when: (1) Multiple skills exist for same purpose, (2) Want best-of-breed combination, (3) Building a comprehensive skill from fragments.
Frankenstein
Model Requirements
Default: Opus (or best available thinking model)
Frankenstein requires deep reasoning to:
- Compare multiple skill approaches
- Identify subtle methodology differences
- Synthesize the best parts creatively
- Catch security/quality issues others miss
Only use a smaller model if user explicitly requests it for cost reasons. The synthesis quality depends heavily on reasoning depth.
Create monster skills by combining the best parts of existing ones.
Quick Start
Frankenstein me an SEO audit skillHow It Works
Step 1: Search All Sources
Search EVERY AI skills repository for matching skills:
1. ClawHub (primary)
clawhub search "[topic]" --registry "https://clawhub.ai"2. GitHub
Search: "[topic] AI skill" OR "[topic] claude skill" OR "[topic] agent skill"
Look for: SKILL.md, CLAUDE.md, or similar agent instruction files3. skills.sh
https://skills.sh/search?q=[topic]4. skillsmp.com (Skills Marketplace)
https://skillsmp.com/search/[topic]5. Other sources to check:
- Anthropic's skill examples
- OpenAI GPT configurations (convert to skill format)
- LangChain agent templates
- AutoGPT/AgentGPT skill repos
Gather all candidates before filtering. More sources = better Frankenstein.
Step 2: Security Scan
Run each skill through skill-auditor. Skip any with HIGH risk scores.
For each skill found:
- Install to temp directory
- Run skill-auditor scan
- Score >= 7 = SAFE (proceed)
- Score < 7 = RISKY (skip with warning)
Step 3: Safe Analysis
Analyze safe skills in sandwrap read-only mode.
For each safe skill, extract:
- Core features (what it does)
- Methodology (how it approaches the problem)
- Scripts/tools (reusable code)
- Unique strengths (what makes it special)
- Weaknesses (what's missing)
Step 4: Compare
Build comparison matrix:
| Feature | skill-A | skill-B | skill-C | WINNER |
|---|---|---|---|---|
| Feature 1 | Yes | No | Yes | A, C |
| Feature 2 | Basic | Advanced | None | B |
| Feature 3 | No | No | Yes | C |
Step 5: Synthesize
Take the winning approach for each feature:
- Feature 1 methodology from skill-A
- Feature 2 implementation from skill-B
- Feature 3 approach from skill-C
Step 6: Build Initial Draft
Use skill-creator to assemble the Frankenstein skill:
- Combine winning features
- Resolve conflicts (if two approaches clash)
- Write unified SKILL.md
- Include scripts from winners
- Document sources
Step 7: Vetting Loop (CRITICAL)
Run plan → test → improve loop until 3 stable passes:
Pass 1:
1. Read draft
2. Try to break it (find holes, contradictions, gaps)
3. Document issues
4. Fix them
Pass 2:
1. Read improved version
2. Actively try to find MORE issues
3. Fix any found
Pass 3+:
Continue until you genuinely try to improve
but can't find significant issuesWhat to look for each pass:
- Missing features that sources had
- Contradictions between combined approaches
- Vague instructions that aren't actionable
- Token waste (verbose where concise works)
- Security gaps
- Broken references to files/scripts
Document in VETTING-LOG.md:
- Each pass number
- Issues found
- Fixes applied
- Why considered stable
Only proceed when:
- 3 consecutive passes with no major issues
- Minor issues documented as known limitations
Step 8: Human Review
Present the vetted skill for approval:
- Show what came from where
- Highlight conflicts resolved
- Show vetting summary
- Ask for final OK before saving
Output
Creates a new skill with:
- Best features from all analyzed skills
- Clear attribution (credits source skills)
- Security-scanned components only
- Unified documentation
Example Session
User: "Frankenstein me an SEO audit skill"
Frankenstein:
Searching ClawHub for "SEO audit"...
Found 5 skills:
1. seo-audit (coreyhaines31)
2. audit-website (squirrelscan)
3. seo-optimizer (local)
4. technical-seo (unknown)
5. seo-checker (unknown)
Security scanning...
✓ seo-audit: SAFE (8/10)
✓ audit-website: SAFE (7/10)
✓ seo-optimizer: SAFE (9/10)
✗ technical-seo: RISKY (4/10) - skipping
✗ seo-checker: RISKY (3/10) - skipping
Analyzing 3 safe skills...
Comparison:
| Feature | seo-audit | audit-website | seo-optimizer |
|----------------------|-----------|---------------|---------------|
| Methodology depth | ★★★★★ | ★★★ | ★★★★ |
| Automated rules | None | 230+ | 50+ |
| Auto-fix capability | No | No | Yes |
| CLI tool | No | squirrel | No |
| E-E-A-T coverage | Yes | Yes | Partial |
Recommended combination:
- Methodology: seo-audit (best framework)
- Rules engine: audit-website (230+ rules via squirrel CLI)
- Auto-fix: seo-optimizer (automated fixes)
- E-E-A-T: seo-audit (comprehensive)
Build this Frankenstein? [Yes/No]Dependencies
This skill uses:
- clawhub CLI (search/install)
- skill-auditor (security scanning)
- sandwrap (safe analysis)
- skill-creator (building)
Spawning Sub-Agents
When spawning analysis sub-agents, always use Opus (or best thinking model) unless user explicitly requests otherwise:
sessions_spawn(
task: "FRANKENSTEIN ANALYSIS: [topic]...",
model: "opus"
)Cheaper models miss nuances between skills and produce shallow combinations.
Limitations
- Only combines publicly available skills
- Skips skills that fail security scan
- Cannot resolve deep architectural conflicts
- Human judgment needed for final synthesis
- Quality depends on available skills
Credits
When a Frankenstein skill is built, it includes attribution:
## Sources
Built from best parts of:
- seo-audit by coreyhaines31 (methodology)
- audit-website by squirrelscan (rules engine)
- seo-optimizer (auto-fix)