Skill Feedback Cycle

Skill Feedback Cycle

An iterative quality loop for improving Claude Code skills: invoke → watch → give feedback → skill updates itself → repeat. Step 6 of the Six-Step Skill Building Framework, and the mechanism by which a skill that initially produces generic output converges on production-grade output over 10–30 runs.

Key points

  • Each cycle: invoke the skill, watch it work, identify what to correct, give feedback, skill updates its own SKILL.md [src-013]
  • “The first couple times you run a skill, you may feel like it’s very AI generated. But by the time you’ve run that skill 10, 20, 30 times, every single time it gets better.” [src-013]
  • The skill itself should contain the feedback-loop instruction — asking the human for a quality score and patch notes at end of each run [src-013]
  • This is an application of the Curiosity Rule: never accept output passively; always interrogate why it made the choices it did [src-013]
  • In Hermes, Nate frames skills as the “how to do it again” half of the assistant. If the user corrects Hermes on the same workflow repeatedly, the correction should become a skill or a skill patch rather than another one-off prompt [src-074].
  • Hermes can create, update, and discover skills through the skills hub/community pattern, but Nate still treats human review as necessary because trigger frontmatter and procedural detail determine whether the right skill fires [src-074].

Related concepts

Source references

  • [src-013] Nate Herk — “Build & Sell Claude Code Operating Systems (2+ Hour Course)” (2026-05-01)
  • [src-074] Nate Herk — “Hermes Agent: Zero to Personal AI Assistant (1 Hour Course)” (2026-05-10)

Robin Cartier perspective

This page is part of Robin Cartier's working AI knowledge graph: a practical research layer for production AI, recommendation systems, experimentation, GEO, and agentic web readiness.

The useful next step is to connect this concept back to applied product leadership and operating models.

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