# Teach ChatGPT Work Once ## 1. Get real work done with ChatGPT. **Teach ChatGPT Work once. Reuse what works.** Start with real work. Give ChatGPT the context you would give a teammate, work through an example, and correct what matters. Draft a Skill while those examples and corrections are still fresh. Then test the draft on new work before you trust it. The human guide for this part of ChatGPT Work is `/#home-stage-skill`. **Speaker note:** Start with a real task, not a request to create a Skill. The goal is to teach the work, capture what matters while the context is fresh, and prove it on another example. ## 2. The mistake: writing the Skill before doing the work. **Wrong order:** WRITE THE SKILL FIRST → guess the workflow → correct the results after the fact. If ChatGPT has not seen the actual task, trusted sources, examples, or your feedback, it cannot know which judgment the Skill needs to remember. Starting with the Skill means packaging a guess. **Speaker note:** Point to the red first step. If ChatGPT has not seen your work, examples, or corrections, the instructions are mostly guesses. ## 3. Why a Skill written upfront gets the work wrong. ChatGPT cannot infer: - Which sources you trust. - Who the work is for. - What a good answer looks like. - Which decisions need your judgment. - What must never be changed, sent, or shared. Teach those things through real work before asking ChatGPT Work to write a reusable set of instructions. **Speaker note:** Explain that better prompting is not the same as guessing a complete process. ChatGPT first needs to see the job, the sources, and what you change. ## 4. Give ChatGPT the context you would give a teammate. Share the real task, the audience, the trusted sources, a useful example, what a good result looks like, and what still needs your approval. **Teaching starts here.** It starts when ChatGPT can see the work and the context around it, not when a Skill file appears. **Speaker note:** Bring the actual task, source, example, audience, and limits into the same conversation. Teaching starts when ChatGPT can see the work and understand what matters. ## 5. Work through one real example and make your corrections. Ask ChatGPT Work to help with the actual work. Say what is useful, what needs to change, and why. Notice the corrections that would matter again next time: - Check the right source first. - Use the right audience and quality bar. - Explain the decision instead of guessing it. - Stop before changing, sending, publishing, or sharing. **Speaker note:** Do the actual work together. Corrections to sources, quality, decisions, and approval boundaries show what a future Skill will need to remember. ## 6. Draft the Skill here, while the context is still fresh. Choose recurring work → Share the context → Work through a real example → Correct what matters → **DRAFT THE SKILL HERE** → Test on fresh examples → Improve and reuse. Ask ChatGPT Work to capture the method before you lose the real example, your corrections, and the reasoning behind them: > We just worked through this task. Draft a Skill that captures the sources, > decisions, and corrections that should carry into the next example. Leave > out details that only matter this time. Ask before changing, sending, > publishing, or sharing anything. This is the first **Skill draft**, not proof that the Skill is reliable. **Speaker note:** This is the exact creation moment: after ChatGPT has the context, completed the example, and received your corrections. Draft before starting a new conversation or losing the useful context, then make clear that the draft still needs to be tested. ## 7. Teach the review method, then reuse it. When you are learning a new application or review process, use the first few real examples to teach ChatGPT how the work should be evaluated. Correct the misses, explain why they matter, and keep the checks that should apply again. Once those judgments repeat, draft a Skill that captures the review method: which source to trust, what to inspect, how to distinguish a real issue from a preference, and when a person should decide. The next example starts further ahead, so you spend less time teaching the same system again. **Speaker note:** Explain that the first reviews are joint discovery. You are learning the application while teaching ChatGPT Work how to review it. After a few corrected examples, capture the reusable method and let fresh work prove that the same checks and judgment carry over. ## 8. Keep what repeats. Leave the one-time details behind. **Keep:** trusted sources, quality checks, review decisions, useful corrections, and the points where a person must approve the next step. **Leave out:** this example's dates, numbers, filenames, slide numbers, and preferences that will not apply next time. **Speaker note:** Keep the source checks, quality bar, and stop conditions. Leave out details that belong to one example instead of the job itself. ## 9. Test the draft on fresh examples. Try the Skill on different real work. Check that it still uses the right sources, handles a new example, protects what should not change, and produces a result you would actually use. **One successful rollout is evidence, not validation.** A Skill is reliable only after its useful judgment holds up on fresh examples. **Speaker note:** Run the draft against another real example with different inputs. A first draft captures what you taught; a fresh example shows whether the Skill can actually repeat it. ## 10. Choose what the work actually needs. - **Do once:** finish a one-time task and move on. - **Use a prompt or checklist:** repeat simple instructions or steps. - **Keep improving the workflow:** document sources, steps, and corrections. - **Draft a Skill:** capture reusable judgment while the real work is still fresh, then test the draft on new examples before trusting it. - **Route the issue:** ask the right person for help when a source, product, or tool is actually broken. **Speaker note:** Preserve the green highlight on the Skill option. Contrast it with a one-time task, simple checklist, or real tool issue, and explain that a Skill draft is not yet a validated Skill. ## 11. Leave with one real workflow to test again. Answer five questions: 1. What work do you repeat? 2. What context would someone need to do it well? 3. What do you keep correcting? 4. Which corrections apply next time? 5. What should stay under your control? Choose an owner, bring one real example, and note what a fresh-example test should prove. Draft the Skill while the useful teaching context is available. Decide whether to reuse it only after that next test. **Speaker note:** Choose one recurring job, bring a real example, and record the context and corrections. Leave knowing what to draft, what to test next, and what still needs your approval. ## Framework connection - Human guide: `/#home-stage-skill` - Agent companion: `knowledge-worker-codex/chapters/04-skill.md` - Ask Codey question: “When should I create a Skill?”