A structured process for creating a Claude Code skill from scratch. Nate Herk presents this before every live skill build, ensuring the skill has clear intent, process, guardrails, and a self-improvement loop.
The six steps
- Name and trigger — what you type to invoke the skill, and when it should fire automatically [src-013]
- One-sentence goal — a single declarative sentence describing what success looks like [src-013]
- Step-by-step process — the numbered procedure the skill follows, written in imperative language [src-013]
- Reference files — any scripts, templates, or external data the skill needs, stored in the skill folder [src-013]
- Guardrails and rules — what the skill must never do, edge-case handling, output quality constraints [src-013]
- Self-improvement feedback loop — instructions for the skill to ask for feedback after each run and update itself [src-013]
Key points
- Steps 1–3 define the contract; steps 4–6 make it production-grade [src-013]
- Without step 6, the skill never improves; the Skill Feedback Cycle turns this into a continuous quality loop [src-013]
- Skills should stay under ~500 lines (Anthropic guidance); use reference files for overflow [src-013]
Related entities
- Claude Code Skills — the entity this framework builds
Related concepts
- Skill Feedback Cycle — the iterative quality loop in step 6
- Progressive Context Loading — why reference files in step 4 load on demand
- Global vs Project Skills — where the resulting skill file lives
- Four C’s of an AI Operating System — skills are the Capabilities layer (C3)
Source references
- [src-013] Nate Herk — “Build & Sell Claude Code Operating Systems (2+ Hour Course)” (2026-05-01)
Recommended next
Keep reading from this thread
From 494 indexed pages and articles.
- Wiki concept Skill Feedback Cycle An iterative quality loop for improving Claude Code skills: invoke → watch → give feedback → skill updates itself → repeat. Related by 013
- Wiki concept Curiosity Rule The discipline of never accepting AI output without asking why it was built that way — treating AI as a mentor that pushes back rather than Related by 013
- Insight AI Beyond POCs How enterprise AI moves beyond proofs of concept through ownership, governance, measurement, adoption, and production operating models Readers have engaged with this next