Hill Climb

hill-climb — one loop, three focus schedules

Every iteration: take the current best candidate, ask the optimizer to propose an edit (its prompt emphasizes a focus set of train tasks), score the result on val, and accept only if it clears the significance gate. The accepted candidate becomes the new best. The test split is never touched here — that is finalize.

The three former hill-climb skills were byte-identical except for one constant; they are now one skill with --focus:

| --focus | what each iteration emphasizes | when to use | |---|---|---| | all (default) | the whole train set — find the single edit that lifts the most tasks | broad capability gaps; the usual choice | | cyclic | one train task at a time, cycling through them | many distinct, unrelated failure modes | | hardest-first | train tasks ranked by baseline score ascending (lowest first), then cycling | a few very hard tasks dominate the gap |

Why only the focus changes: the parent-selection rule (always the current best), the gate, and the honesty guarantees are identical across schedules — only the attention differs. Keeping one loop means a fix to the gate or memory wiring can never drift between variants.

Inputs / outputs (manifest tokens)

• needs: scores + traces (the per-task val results to reflect on) and

candidate (the parent to extend).

• provides: candidate (the accepted best).

Standalone use

python scripts/run.py --run-dir .capevolve/run_X --project .capevolve/project \
  --optimizer 'python .../run-optimizer/scripts/run.py --name mock --workdir {workdir} --prompt {prompt}' \
  --focus hardest-first --max-iterations 10 --n-trials 4

--resume continues from the run's current best (reading its val from rollouts) instead of the baseline. --no-regression adds a SWE-bench-style dual gate: reject a candidate that breaks any val task the parent already passed, even if the mean improves.

Back-compat: --focus all-at-once is accepted and treated as all.

References

• references/focus-schedules.md — how each schedule builds its focus set.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: skillberry-ai
• Source: skillberry-ai/cap-evolve
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.