Descriptions:
Peter Yang walks through building a complete, production-ready AI skill inside Claude Code from scratch — a reusable ‘edit post’ skill designed to improve long-form newsletter drafts based on personal writing style and examples. The tutorial is structured around five concrete steps: creating the skill using real writing samples and personal context, defining trigger conditions via a description file, manual testing, building an automated evaluation system, and adding a memory file so the skill learns and improves across sessions.
The most technically interesting section covers the multi-agent eval loop. Rather than asking Claude to rate output on a numeric scale (which Yang argues is unreliable), the skill uses a battery of pass/fail checks covering intro hook quality, absence of AI slop patterns like em-dashes and filler words, voice authenticity, substance, and calls to action. A separate Claude agent with a clean context window runs these evals to avoid bias from prior edits — and if any check fails, the original agent iterates until all checks pass.
The video includes several non-obvious tips: keeping examples separate from the core skill.md file improves performance and protects privacy when sharing skills, providing multiple diverse examples prevents overfitting, and a dedicated ‘skill improver’ meta-skill can be used to clean up any existing skill. The entire workflow is built live in Claude Code, using dictation via Whisper Flow for prompting, making the tutorial a practical reference for anyone looking to build robust, self-improving AI workflows with Anthropic’s tooling.
📺 Source: Peter Yang · Published June 03, 2026
🏷️ Format: Tutorial Demo
