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First Experience Card ​

OME becomes valuable when the library contains lessons from your own real corrections. Create the first one through reflect and draft approval: the agent reflects, you approve or refine, then you confirm what enters the library.

1. Start a retrospective ​

Copy this to your agent:

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Create an OME retrospective run for one reusable coding lesson:

1. Use the OME reflect flow to scan accessible coding-session sources deeply enough to find real user corrections, not just the last few messages.
2. Produce no more than 3 experience drafts.
3. Only keep lessons that would change a future agent action.
4. Give me only the draft approval page and a short summary, not JSON or internal files.

--focus is a lens, not a shortcut. Unless you explicitly limit the source set, the agent should still inspect all accessible session sources relevant to the focus.

2. Approve And Refine ​

Open the generated draft approval page. Ask:

  • Would this situation happen again?
  • Would seeing this card change the agent's next action?
  • Is it specific enough to avoid broad keyword matches?
  • Does it have clear ignore cases?

Reply in plain language:

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Approve the first one.
The second is too broad; refine it with the boundary I just gave.
Merge these two.
Do not add it yet; here is a counterexample.

3. Confirm Library Add ​

When it looks right, tell the agent:

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Add the approved experiences to the library.

Nothing is recalled in future tasks until you explicitly confirm adding it.

4. Verify Recall ​

Ask the agent to verify with a realistic future prompt, not the exact wording from the card:

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Verify recall for the approved card with a realistic future task.
Tell me whether it matches too broadly or too narrowly.

The result should be precise. If a card matches too broadly, ask the agent to refine it before adding more cards.

Local-first experience recall for AI coding agents.