← ClaudeAtlas

hillclimblisted

Tune anything measurable — retrieval quality, latency, eval scores, a prompt — by disciplined one-variable-at-a-time experiments with a kept/reverted log. Use when: (1) improving a metric, (2) tuning retrieval/RAG or performance, (3) iterating on a prompt against a dataset. Triggers: "tune", "improve the score", "optimize", "why did it get worse", "iterate on".
gnibu/bento · ★ 0 · AI & Automation · score 68
Install: claude install-skill gnibu/bento
# Hillclimb The failure mode: change three things at once, the number moves, you don't know which tweak did it — or whether it was noise. Next week it drops and you can't retrace. One variable at a time, logged, keep-or-revert. ## Steps (paste as todos) 1. **Freeze the harness first.** Fix the dataset, the metric, and how it's measured *before* touching the thing you're tuning. If the ruler moves, no result means anything. 2. **Record the baseline.** Run the frozen harness once. Write the number down. 3. **Change ONE variable.** One knob per experiment. Never stack untested changes. 4. **Measure against baseline.** Same harness, same input set. 5. **Keep or revert — and log it either way.** Keep only a real gain (significant, not noise; re-run if the sample is small). Log the attempt, the delta, and the decision. A reverted attempt is still knowledge — write it down so you don't retry it. 6. **New baseline on a keeper**, then back to step 3. 7. **Don't stop on a lucky first hit.** Run enough iterations that one good roll can't fool you. State the stop condition up front (target metric, or N no-improvement rounds). ## Guardrails - Regression check on every keeper: a gain that breaks something else is not a gain. - If two knobs seem coupled, that's a finding — still change one, note the interaction. - The log is the deliverable as much as the final number. No log = you learned nothing transferable.