
Load the architecture step by step, matching the pdf processing skill to user requests, trigger the skill, and apply progressive disclosure via front matter and lightweight metadata.
Leverage progressive disclosure to engineer skills with startup metadata of 100 tokens and roughly 5k tokens for skill markdown bodies, using guide references, domain mapping, and conditional scripts patterns.
Learn progressive disclosure for structuring large skills, compare guide-with-references versus domain-based organization, and follow golden rules like under 500 lines, one-level markdown, and descriptive file naming for efficient cloud-based access.
Learn to set the right degree of freedom for cloud skills, balancing high, medium, and low freedom with examples like code reviews, templates, and exact commands to prevent risky deviation.
Plan the skill before coding by defining its domain, scope, and what it should and should not do, then select a pattern and degree of freedom for Cloud.
Define and implement a reusable code review skill by creating a skills directory and skills.md, outlining structured reviews with issues, severity, location, and actionable suggestions.
Master the plan-validate-execute design pattern, a production orchestration approach for verifiable intermediate outputs. Plan identifies inputs and scripts; validate checks structure and columns; execute writes a deterministic output after validation.
Build a production-quality, multi-file analytics skill with a Python CSV validator, organized folders, and reference files for finance and sales, using a plan-validate-execute workflow.
Define a CSV data analysis skill workflow by creating a markdown with front matter, domain mapping, validation, and structured outputs using pandas to summarize, visualize, and report.
Validate the skill end-to-end by analyzing a bad CSV, adding missing columns, validating, then analyzing a valid finance CSV to generate insights, summary, and data quality notes.
Run end-to-end tests with Playwright in a TypeScript setup, validate a dev server on port 3000, and generate a JSON test report.
Run an end-to-end playwright test suite with skill, introduce a negative condition by changing the port, and review the JSON report detailing the pass, fail, file, line, and error for diagnosis.
Claude Skills are the simplest way to make Claude reliably do what you need — every time, without re-explaining yourself. In this course, you will go from writing your very first SKILL file to building multi-file Skills with validator scripts, reference libraries, and structured workflows that Claude loads automatically.
The course is structured around four progressive modules, each grounded in hands-on labs. You will start with the fundamentals of the Skills architecture, move into building single-file and multi-file custom Skills, and finish with evaluation-driven best practices used in production environments. Every concept is paired with a working lab you can keep and adapt after the course.
What Will Students Learn?
- How Claude Skills work — the filesystem-based, demand-loaded model that makes them composable and efficient
- The exact YAML frontmatter rules that control when a Skill triggers and what Claude is allowed to do
- How to write a SKILL that is precise enough to trigger correctly and flexible enough to handle real-world variation
- The difference between Skills, MCP servers, and Agents — and how to choose the right tool for each job
- How to structure multi-file Skills with REFERENCE, domain sub-folders, and supporting scripts
- The plan-validate-execute pattern for Skills that interact with data files or external tools
- How to write and wire in helper scripts (Python) that reduce context noise and handle input validation
- How to evaluate a Skill against a structured rubric and iterate until it meets a production bar
- How to deploy Skills across Claude Code, claude ai, and the Claude API