tune-parameters
Skill1 starsChanged 5 months agoMITView on GitHub
Choose a threshold or parameter by reading the shape of the metric surface across a sweep, not by grabbing the single best grid cell. Use whenever picking a numeric knob (threshold, weight, window size, learning rate).
In this repository4 other files it gives its agents.
- CLAUDE.mdCLAUDE.md
- cc-task-benchSkill
- llm-evalSkill
- verify-refactorSkill
The file
Files
Goal: pick a parameter that is robust, not overfit to noise.
- Sweep, don't guess. Vary the parameter ($ARGUMENTS if given) across a sensible grid. Record the target metric at each point. Don't hand-pick a value and don't stop at the first good number.
- Read the surface shape:
- Smooth — metric changes gradually as you vary the knob → a real effect. Pick a robust point in the middle of the good region, not its edge.
- Lone spike — one great cell with worse cells all around it → overfit to noise. Discard it.
- Validate across the full history / dataset, not just the recent or easy regime. A value that only holds under current conditions will break when conditions change.
- Report: the chosen value, the shape you observed (smooth region vs. spike), and the range over which the metric stays acceptable (the safety margin).
Anti-patterns to flag if you see them: argmax over a noisy grid; tuning and validating on the same slice; a threshold justified by a single impressive data point.
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