gh
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zoltanmaric/coppersushi692026年10月14日 更新
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forecast-experiment
Records hypotheses, evidence and decisions when proposing, running or evaluating forecast experiments, including backtests and feature or model comparisons. Excludes routine tests and unchanged forecast operation.
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SKILL.md（原文）
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Forecast experiments
Read the evaluation contract and point-in-time contract before designing a comparison.
Before execution
Save a proposal with the MLflow experiment record before launching the run. Record:
- The hypothesis and intended change.
- Motivating experiment IDs and prior results inspected; use none for the first baseline.
- The comparison baseline, fixed evaluation dates and metrics, and what result would support the hypothesis.
Use saved artifacts for the proposal and conclusion, and tags for related experiment/run IDs. Link execution runs for folds, variants and retries to the research record; distinguish these from experiments that motivated the idea. If proposing only, save the proposal without launching work.
During execution
Link the exact run manifests for inputs, code, environment, configuration and seeds; do not copy their contents into prose. Retain failed and interrupted attempts with their status. Preserve the original proposal; start a linked experiment when the hypothesis or evaluation protocol changes.
After execution
Append the results, evidence links, limitations, conclusion and next decision: keep, reject or investigate further. Compare against the baseline on matching evaluation rows. Record losses and failures as well as gains. If reviewing an existing run without a prior proposal, mark the rationale as retrospective; do not invent a pre-run hypothesis.
Identify evaluation periods whose results informed experiment choices as development data, not untouched holdouts. Keep experiment history in MLflow; put durable accepted design decisions in the wiki with experiment IDs. Follow this procedure manually when automation is absent.
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