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Solve First, Then Skill-ify

#concept

Summary

The core method both HR interviews teach: don't build a skill speculatively — solve the task with the AI once, correct it until the output is right, then say "now create a skill from this." The skill freezes a proven workflow, not a guess about one.

Current Understanding

The recurring beginner mistake is writing the skill first and then trying to "shove it somewhere." The working loop is: do your real task through the AI → watch the result → correct it → freeze the final state into a reusable skill. Two trigger heuristics exist in the corpus:

  • Eugene: turn any correction loop longer than ~3 messages into a skill.
  • Konstantin: auto-create a skill after >5 tool calls on a task (the hermes curator variant).

The payoff goes beyond reuse: a packaged skill is a handoff and de-risking asset — "a person with not even a third of your HR experience can deliver a decent result," which cuts onboarding and lets the expert take a vacation. This is how skills-as-memory gets populated in practice — the method side of that architecture, and the fix for "don't teach the AI abstractly."

Evidence

Contradictions / Uncertainty

Next Questions

  • What does a good "create a skill from this" prompt look like — does the corpus contain a concrete example transcript?
  • How do the ~3-message and >5-tool-call heuristics compare in practice; is one strictly better for non-programmers?