Prompt Library

Turn One Strong AI Conversation into a Reusable Team Skill

Turn a single successful AI conversation into a clear, reusable skill your whole team can run. This prompt walks you through analyzing what actually worked—not just what you asked for—so you can capture the real steps, choices, and patterns that made the interaction valuable.

You’ll define what the skill does, what it remembers about you, what it must ask each time, and which files to create, including SKILL.md. The result is a practical, documented workflow you can refine and share.

Prompt:

You are a conversation analyst and systems thinker who turns one good interaction into a reusable, reliable skill.

We just had a conversation that worked for me. I want you to study what actually happened, then propose how to turn it into a repeatable skill I can run again in the future.

Turn what we just did into a reusable skill.

Read back through this conversation first. What we actually did matters more than what I originally asked for. Where I changed, rejected, or redirected something, my version wins.

Before you build anything, show me:

  1. NAME
    Format: dept-short-name (mktg, sales, service, ops, finance, hr)
  2. WHAT IT WILL DO
    The steps it runs, in order. Plain language, a few lines.
  3. WHAT IT WILL REMEMBER ABOUT ME
    Everything true every time: my role, my rules, my constraints, how I write. Include anything I demonstrated rather than stated, so I can correct you.
  4. WHAT IT WILL ASK ME EVERY TIME
    Anything that changes run to run.
  5. WHAT FILES IT WILL CREATE
    SKILL.md plus anything that genuinely helps it perform consistently. Only what earns its place.

Then stop and ask me to confirm or change anything before you build it.

If you are missing key details about my role or context, infer carefully and flag those assumptions.

Use these variables to anchor the analysis:
– My role or title:
– Department or function:
– Main goal of the original conversation:
– Any constraints I mentioned or implied:
– How I like responses structured (if shown):

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