hill-climb
From the author
Runs a global hill-climb optimization loop where the parent is always the current best candidate and the val significance gate decides acceptance. Use as the algorithm for most runs — the first run on a new project, binary pass/fail scorers, and small task sets. Pick how each iteration's reflection is focused with --focus all (every failing val task), cyclic (one task at a time), or hardest-first (lowest-scoring first). Switch to gepa when rollouts are expensive and per-task feedback is rich, or skillopt when you want an annealed edit budget.
Install
npx skills add skillberry-ai/cap-evolve --skill hill-climb -a claude-code
Installs to .claude/skills/hill-climb/ · Each command fetches the skill straight from GitHub with the open skills CLI.
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Similar skills
Closest to this one by what they do and when they are used.
- skilloptskillberry-ai/cap-evolveRuns the SkillOpt single-lineage optimization loop, which organizes a hill-climb into epochs over mini-batches of train tasks under a textual learning rate — an integer edit budget that decays on a constant|linear|cosine schedule — and ends each epoch with one extra gated consolidation step. Parent is always the current best; acceptance is the val significance gate. Use when a run should anneal from broad early edits to small late ones and consolidate once per epoch, rather than hill-climb's one-shot whole-trainset proposals or gepa's Pareto frontier.Writing★ 66
- gepaskillberry-ai/cap-evolveRuns the GEPA optimization loop (arXiv:2507.19457) — sample-efficient reflective Pareto search. A cheap train-minibatch pre-gate decides whether a proposal is worth an expensive val evaluation, and parents are sampled from a per-instance frontier so specialists survive instead of being averaged away. Use when rollouts are expensive and the scorer returns informative per-task feedback, and you want the most quality per evaluation. Use hill-climb instead for a first baseline run or for feedback-poor binary pass/fail tasks.Science★ 66
- gateskillberry-ai/cap-evolveApply the acceptance decision that keeps optimization honest — always on the val split, by default requiring the improvement to exceed the significance bar (Δ > k·SE) so noise is not mistaken for progress. Use to inspect or reproduce a single accept/reject decision; the algorithms apply it internally every iteration.Other★ 66
- gauntletoprogramadorreal/optimus-claudeRuns a Gauntlet Loop: turns an ambitious goal into a builder/critic prompt judged against a concrete quality bar, confirms with the user, then leads the run until the output beats the bar or the user stops it — or exports a paste-ready /goal prompt for a fresh session. Use for long-horizon goals with an inspectable reference. Long-running; spawns many subagents, edits project files, and commits and pushes finished pieces to a feature branch.Writing★ 75
- thinking-jobs-to-be-donetjboudreaux/cc-thinking-skillsDeciding what to build or why adoption fails. Recover the progress users hire a solution for under a circumstance, then rank by outcome and competing workarounds.Other★ 1.7K
- using-cap-evolveskillberry-ai/cap-evolveFront door for cap-evolve: routes an optimization request to the right pipeline phase. Use when someone wants an agent, skill, system prompt, tool surface, or MCP toolset to score higher on an eval, benchmark, or task suite — "optimize my skill", "raise the pass rate on these tasks", "my agent keeps failing these cases", "get this prompt's accuracy up on my evals" — even when they never say "optimize", and whenever a .capevolve/ project or an unfinished run is in the tree. Routes to intake, the check gate, a resumed run, or the report; optimizes nothing itself. Not for making code or a query faster, and not for rewording one prompt with no eval to score it against. When the user names a phase or algorithm outright (baseline, gate, hill-climb, gepa), use that skill directly.Engineering★ 66
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