frankenstein

What This Skill Does

The Frankenstein skill is a high-level orchestration tool designed for OpenClaw agents to perform advanced synthesis of multiple existing AI skills. Instead of settling for a single, potentially limited solution, Frankenstein systematically polls the global AI ecosystem—including ClawHub, GitHub, skills.sh, and skillsmp.com—to identify all available tools for a specific task. By leveraging deep reasoning models like Opus, the agent performs a cross-analysis of methodologies, features, and code quality. It utilizes the skill-auditor utility to ensure that only secure, high-quality components are considered, and executes code within the sandwrap environment to prevent system interference. The skill culminates in the creation of a 'Frankenstein' draft, which represents a best-of-breed implementation, refined through a mandatory three-pass vetting loop to ensure robustness and structural integrity.

Installation

To install this skill, run the following command in your terminal: clawhub install openclaw/skills/skills/rubenaquispe/frankenstein

Use Cases

Frankenstein is best utilized in the following scenarios: (1) When you encounter multiple disparate skills for a single task (e.g., three different SEO auditing tools) and want a unified, superior version. (2) When you require a bespoke solution that combines specific, advanced features from various niche skills into one master agent. (3) When you are building a complex automated workflow and need to assemble a comprehensive skill from smaller, proven fragments found across public repositories.

Example Prompts

1. "Frankenstein me an SEO audit skill that combines the speed of the basic clawhub SEO tool with the deep-link analysis from the GitHub repository modules."
2. "I need a sentiment analysis skill for financial news. Go find the best implementations on skills.sh and GitHub, audit them for security, and build me a combined master skill."
3. "Create a comprehensive code-review agent by synthesizing the best linting logic from existing developer tools found on ClawHub and skillsmp.com."

Tips & Limitations

• Model Selection: Always prioritize high-reasoning models (Opus). Using smaller models will result in shallow comparisons and degraded synthesis quality.
• Security First: The skill-auditor is non-negotiable. If a source skill scores below 7, do not attempt to force it into the synthesis.
• Depth vs. Breadth: Searching more sources generally yields better results, but ensure your system has sufficient memory to handle multiple concurrent skill analyses.
• Vetting: Never skip the three-pass vetting loop; it is the primary mechanism for catching logical conflicts between merged codebases.