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hillclimb

hillclimb

github.com/yeachan-heoOther40.7k8 installsUpdated today
Repository

About hillclimb

hillclimb is a skill for iterative, evidence-based improvement of a measurable metric. It guides teams through fixed-baseline experiments, keeping verified wins and reverting losses.

Key features

  1. 01Define a measured baseline
  2. 02Record each experiment round
  3. 03Test one hypothesis at a time
  4. 04Keep wins and revert losses
  5. 05Report progress and plateaus

Use cases

  1. 01Reduce cold-start time over multiple rounds
  2. 02Improve a flaky test rate against a fixed measure

Install

Copy the command and run it in a terminal.

$npx skills add https://github.com/yeachan-heo/oh-my-claudecode --skill hillclimb

After installing, type/hillclimbin Claude Code to confirm it loaded.

Details

From the project README

Hillclimb

Climb one metric scientifically: each round is a hypothesis, a measurement against a fixed baseline, and a decision — keep the win, revert the loss. One accepted commit per verified win; the log is the climb's memory.

When to use

  • a measurable target will take multiple rounds ("cut cold-start to <2s", "get flaky suite under 1%")
  • the user names a metric to improve, not a feature to build

When not to use

  • a one-off fix with a known cause — debug or execute
  • no measurable baseline exists yet — build the measurement first (that is round zero, not skippable)

The loop

  1. Fix the baseline: the metric's definition, the exact command that measures it, and the current value — measured, not estimated (benchmark-checklist discipline). The baseline command never changes mid-climb; if it must, the climb restarts.
  2. Log the target and the climb in .omc/state/decision-trail-{task-slug}.tsv via the show-me-your-work trail: one row per round.
  3. One hypothesis per round: what you believe will move the metric and why. State it before touching code.
  4. Implement on a branch, measure with the baseline command, and apply benchmark-checklist before believing any number.
  5. Decide: a verified win is committed alone (one commit per win, so any regression bisects to a single round); a loss or wash is reverted and logged with what was learned. Neither counts as failure — both are data.
  6. Repeat until the target is reached or the hypothesis space is exhausted — then report the plateau honestly instead of grinding.

Rules

  • Never stack two hypotheses in one round; a stacked win cannot be attributed.
  • Never move the baseline, relax the check, or cherry-pick the favorable run — a climb that cheats its measurement is worse than no climb.
  • Every decision row names its measurement evidence; every claim is labeled measured, inferred, or guess (principles skill, Explain the number).
  • The final reply reports: rounds run, wins kept, the measured delta from the original baseline, and what was tried and rejected.

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