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Skills

What's the difference between an AI that answers questions and one that runs your workflow?

A skill packages a repeatable workflow into a single slash command. Instead of re-prompting the same instructions every session, you invoke /review-pr or /commit — the AI follows a predefined set of steps, quality gates, and output formats. One command. Consistent results.

How Skills Work​

AI coding tools like Claude Code expose skills as slash commands. Each skill contains:

  • Instructions — the steps the AI must follow
  • Quality gates — what "done" looks like before the AI reports success
  • Argument hints — what input the skill expects (a file path, a PR number, free text)
  • Trigger phrases — natural language that activates the skill automatically

The pattern is composable. A publishing workflow might invoke /fix-links, then /review-pr, then /commit — all from a single /publish command. Small, correct skills chain into larger workflows.

The Market Pattern​

Any instruction a team repeats more than three times is a candidate for a skill.

Without skills — re-prompt every session, inconsistent output, knowledge lives in one person, slow onboarding.

With skills — one invocation per workflow, gates enforce quality, skill is shareable across the team, new hires invoke rather than learn.

Skills shift AI from a question-answering tool to a workflow execution engine. The difference is whether judgment is embedded in the skill or re-applied every time.

Skill Maturity​

Not all skills are equal. A maturity model applies:

L0 — Idea — exists as a repeated manual prompt

L1 — Defined — written down with steps and gates

L2 — Connected — invoked consistently across sessions

L3 — Tested — has caught at least one error via a gate

L4 — Proven — zero manual interventions, measurable improvement

L4 skills are institutional knowledge, encoded. They outlast any individual contributor.

Context​

Questions​

What's the highest-value repeatable workflow in your current work that hasn't been packaged as a skill yet?

  • How do you decide when a workflow is stable enough to package as a skill versus keeping it as an improvised prompt?
  • What's the difference between a skill that saves time and one that just moves the work somewhere else?
  • If L4 skills encode institutional knowledge — what happens to that knowledge when an AI tool is deprecated?