AI SummaryCreate production-ready AI skills by extracting domain expertise and system ontologies, ensuring reliable performance in real-world applications. Ideal for developers building AI assistants that need deep contextual knowledge.
Install
Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to install the "create-expert-skill" skill in my project. Please run this command in my terminal: # Install skill into the correct directory (2 files) mkdir -p .claude/skills/create-expert-skill && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/create-expert-skill/SKILL.md "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/data-ai/create-expert-skill/SKILL.md" && curl --retry 3 --retry-delay 2 --retry-all-errors -o .claude/skills/create-expert-skill/metadata.json "https://raw.githubusercontent.com/diegosouzapw/awesome-omni-skill/main/skills/data-ai/create-expert-skill/metadata.json" Then restart Claude Code (or reload the window in Cursor) so the skill is picked up.
Description
Create production-ready skills from expert knowledge. Extracts domain expertise and system ontologies, uses scripts for deterministic work, loads knowledge progressively. Use when building skills that must work reliably in production.
Templates & Examples
See resources/templates/ for: • Minimal skill template • Enhanced skill template • Script template See resources/examples/ for domain-specific patterns.
Expert Skill Creation
Transform expert knowledge into production-ready skills that combine domain expertise with system-specific understanding.
Why Skills Fail in Production
AI assistants fail not because they lack intelligence, but because they lack: • Domain Expertise — Industry-specific rules, edge cases, unwritten conventions • Ontology Understanding — How YOUR systems, data structures, and workflows actually work Both are required. Domain knowledge without system context produces generic output. System knowledge without domain expertise produces structurally correct but semantically wrong results.
Workflow
` Assess → Discover (Expertise + Ontology) → Design → Create → Refine → Ship `
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