SuperAPI AI Directory

money-retro

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Updated 10-16 07:22

About this skill

/money-retro — Review and lesson distillation

An attempt without a review is just gambling. This step aligns "what actually happened" with "what was initially expected", making recommendations more and more accurate.

Cross-platform notes

allowed-tools is a Claude Code permission declaration; Codex can ignore it and use the local file tools available in the current session to accomplish the equivalent action.

Process

Step 1 — Retrieve the original expectations

Read .money-state.json, find the plan and income_expectation for this opportunity (written earlier by money-plan/money-verify).

Step 2 — Ask about the actual situation

  • How much time was actually invested?
  • How much money was actually received? (Has the first payment arrived? Through what channel?)
  • During the process, were there any anti-fraud red flags (asked to pay/switch to private transactions/delayed payment)?
  • Gut feeling: willing to continue? Where are you stuck?

Step 3 — Align expectations vs. actuals (the core of the calibration loop)

Retrieve the active.predictionwritten earlier by money-plan, and reconcile it item by item with the actuals: expected investment/actual investment, expected time to see money/actual, expected income/actual. Give an honest judgment:continue / adjust approach / cut losses and give up. For large gaps, figure out whether it's an opportunity problem or an execution problem. If any stop-loss line or anti-fraud red flag was triggered → clearly recommend cutting losses, and record this lesson as a "personally encountered pitfall".

Step 4 — Persist + feed back into lessons.md (close the calibration loop)

  • In .money-state.json 's retro_log[] append an entry: {date, opportunity_id, predicted, actual, verdict_update, lesson}.
  • Update this opportunity's status (running / done / dropped).
  • Write/append reusable lessons to the current working directory's lessons.md(create it if it doesn't exist): one per line, in two categories—
    • 对我有效: (e.g.: "Xiaohongshu image-text vertical X started getting private message inquiries in week 3")
    • 亲历坑: (e.g.: "annotation-type hourly pay is indeed low, not worth continuing"/"a certain platform delayed payment")
    • This lessons.md will be read by money-find / money-verify at Step 0,making this method more and more accurate for this user—this is the key to closing the calibration loop, don't skip it.

Wrap-up

  • Continue → give a small goal for the next stage.
  • Cut losses → suggest using money-find to change direction, and carry this lesson into the next screening.