The speaker proposes a structured approach to building AI skills by separating components into distinct files. This includes a main `skill.md` for instructions, separate files for personal context and examples, an `evals.md` for quality checks, and a `memory.md` for long-term learning.
A core concept is creating a loop where an AI's work is checked by a separate evaluation agent. If the output fails any checks, it's sent back to the original agent for revision, a process that repeats until all evaluation criteria are passed.
The speaker strongly advises against using numerical scoring for AI evaluation, claiming models fabricate justifications and cannot reliably distinguish between scores like 4/5 and 5/5. Instead, he recommends a system of concrete, objective pass/fail checks.
Despite the sophisticated automation, the speaker asserts that AI-generated output is typically only 80-90% complete. The final 10-20% requires human taste, judgment, and handcrafting to eliminate subtle errors and "AI slop," achieving a truly high-quality result.
A recurring concern is the tendency for AI to produce generic, verbose, and stylistically predictable content, termed "AI slop." The speaker builds specific checks into his evaluation process and even uses a dedicated 'Skill Editor' skill to identify and remove filler words, clichéd phrasing, and poor formatting.
Keep pulling the thread on Claude Code.