How I Architected 84 Custom Skills for Claude Code to Automate My Daily Engineering

An AI Product Manager and Full-Stack AI Engineer at a B2B SaaS platform has architected 84 custom skills for Anthropic's Claude Code to automate daily engineering tasks. The modular skill architecture partitions engineering knowledge into isolated, self-contained domain skills stored in ~/.claude/skills/, addressing issues like context window degradation and token bloat from monolithic prompts. The system dynamically injects only the relevant skill into active memory when Claude detects a specific domain.

I've been using Anthropic's Claude Code and autonomous coding agents in production daily as an AI Product Manager & Full-Stack AI Engineer at a B2B SaaS platform. Like many engineers, my initial workflow consisted of pasting massive, 50-line system prompts into every new session: This approach quickly broke down. Monolithic prompts lead to context window degradation , token bloat, and subtle hallucinations where the LLM forgets critical constraints halfway through a refactor. To solve this, I designed a Modular Skill Architecture . Today, I want to break down how it works, the anatomy of a skill file, and how you can implement this in your own projects. Instead of loading all domain rules at once, we partition our engineering knowledge into isolated, self-contained Domain Skills stored in ~/.claude/skills/ or ~/.gemini/config/skills/ . Each skill contains: When Claude detects you are tackling a specific domain e.g., writing unit tests or debugging an agent , it dynamically indexes and injects only the necessary skill into active memory.