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Course Description

Title

Custom Skill Developer: Building Autonomous Agents for Claude Code

Audience

Intermediate to advanced users of Claude Code who want to extend its capabilities by creating custom skills — autonomous agent definitions that Claude executes step-by-step to accomplish complex, repeatable tasks. Readers should be comfortable with markdown, YAML, and basic command-line operations. No traditional programming experience is required; this guide is designed for AI-first builders who work at the speed of thought.

Prerequisites

  • Working installation of Claude Code CLI
  • Familiarity with markdown syntax
  • Basic understanding of YAML configuration files
  • A GitHub account for publishing and distribution
  • Experience using at least 2-3 existing Claude Code skills

Course Summary

This knowledge base teaches you to design, build, test, and deploy custom Claude Code skills. Skills are markdown-defined autonomous agents that give Claude structured workflows, quality standards, and domain expertise. You will learn the complete skill anatomy — from YAML frontmatter and trigger conditions to multi-step workflows with quality scoring rubrics. The guide covers advanced patterns including meta-skill routers (which consolidate multiple sub-skills under one entry point to work within Claude Code's 30-skill limit), token-efficient lazy loading, session logging for continuity across context windows, and pipeline orchestration where skills chain together in dependency order. By the end, you will be able to create production-quality skills for any domain and distribute them to other Claude Code users.

Topics Covered

  1. Skill Fundamentals — What skills are, how Claude Code loads and executes them, the relationship between skills and the system prompt
  2. The Skill Ecosystem — Surveying existing skill categories (book generation, analysis, specialized), understanding the 30-skill limit, skill discovery mechanisms
  3. Building Your First Skill — Creating a minimal SKILL.md, testing it locally, iterating on behavior
  4. SKILL.md Deep Dive — Complete anatomy of the skill definition file: frontmatter schema, section conventions, step numbering
  5. YAML Frontmatter — The name, description, license, and allowed-tools fields; how descriptions appear in Claude's system prompt
  6. Workflow Design — Step 0 environment setup, sequential vs. parallel steps, user dialog triggers, conditional branching, error handling
  7. Quality Scoring Systems — Designing 1-100 point rubrics, weighted sub-categories, threshold-based proceed/stop logic, quality gates
  8. Meta-Skill Routing — Consolidating related skills under a router, keyword-based routing tables, the references/ directory pattern, decision trees for ambiguous requests
  9. Token Efficiency — Lazy loading of reference documents, tiered information retrieval (MCP → shell → file read), skip-if-complete detection, minimal context strategies
  10. Session Logging — Writing structured session logs, enabling cross-session continuity, log format conventions, state tracking with JSON files
  11. Pipeline Orchestration — Chaining skills in dependency order, checkpoint patterns, the 12-step intelligent textbook pipeline as a case study
  12. Data Format Skills — Skills that transform data (CSV → JSON, learning graphs, metadata schemas), working with Python helper scripts
  13. Code Generation Skills — Skills that produce executable code (MicroSims, scripts, configurations), template patterns, output validation
  14. Analysis & Validation Skills — Skills that score, audit, or report on content quality, DAG validation, metrics generation
  15. Installation & Registry — Global vs. project-local installation, symlink patterns, the skill listing system, MCP server integration
  16. Testing & Debugging — Manual testing workflows, common failure modes, debugging skill behavior, iteration strategies
  17. Publishing & Distribution — Packaging skills for sharing, GitHub-based distribution, versioning conventions, documentation requirements

Learning Outcomes (Bloom's Taxonomy)

By completing this knowledge base, learners will be able to:

  • Remember: Identify the required components of a SKILL.md file and list the standard sections
  • Understand: Explain how Claude Code discovers, loads, and executes skill definitions during a session
  • Apply: Create a complete, functional skill that follows all conventions and passes quality validation
  • Analyze: Diagnose why a skill is not behaving as expected by tracing execution through its workflow steps
  • Evaluate: Assess whether a skill design is token-efficient, maintainable, and follows best practices using the quality scoring framework
  • Create: Design and publish a meta-skill router that consolidates multiple related sub-skills with lazy-loaded reference documents

Estimated Scope

  • 17 chapters across 5 sections
  • ~200 concepts in the learning dependency graph
  • Target reading level: professional/technical (accessible to non-engineers who build with AI)
  • Interactive elements: MicroSims for skill workflow visualization, routing decision trees, quality score calculators