CodeWithLLM-Updates
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Open-source skills for controlling AI
https://github.com/mattpocock/skills
These Skills turn AI from an “improvising assistant” into a controlled engineering tool. They solve real pain points of working with AI: reduce chaos and hallucinations, save tokens, scale well, and increase predictability. LLMs quickly “get dumber” due to attention degradation in long sessions, so the approach deliberately breaks work into smaller pieces, preserves what is already understood, and passes context through documents instead of keeping everything in one long session.

Knowledge is transferred via CONTEXT.md and ADRs — the agent and the team speak the same language. Skills must be invoked manually (this is by design) and require some setup time. They work best with strong models + a solid agent harness (Claude Code, etc.). The biggest gains come on medium and large tasks where quality, reproducibility, and control over context matter.

Matt Pocock’s video is a practical tutorial on his popular open-source skills repository. He shows the full cycle of working with AI agents (Claude Code and others) for real engineering.

https://www.youtube.com/watch?v=M6mYodf0dJM

It covers installation and repository setup (/setup-matt-pocock-skills) — choosing an issue tracker (GitHub, Linear, local markdown files, etc.), triage labels, and domain documentation (CONTEXT.md + ADRs).

Core workflow:

Demonstrated via a refactoring / legacy-cleanup example: Grill with docs → the agent explores the namespace, asks 6–20 questions → shared plan → to-spec → to-tickets or implement (if it fits in the smart zone).