alpha-evolve
Use when the user wants to evolve an ML model/program through population-based search rather than a single sequential refine loop — a generational evolution where parallel proposers each apply one small SEARCH/REPLACE diff to a parent, scored by a cascade-evaluated training run, and children are kept in a MAP-Elites archive across islands (with migration + checkpointing) so diverse high performers survive. A finite, bounded-parallelism re-creation of AlphaEvolve/OpenEvolve, bent for ML autoresearch. Runs to a fixed compute budget or until interrupted. Not for the sequential single-thread autor
npx skills add gaasher/Agent-Loop-Skills --skill alpha-evolve --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Alpha-Evolve > Reference (read if you need the algorithm's details): AlphaEvolve — https://arxiv.org/abs/2506.13131 · > OpenEvolve (open-source impl) — https://github.com/algorithmicsuperintelligence/openevolve A **population-based evolutionary** loop over a program. The artifact is the editable model code; a **child** is one analysis-informed **SEARCH/REPLACE diff** to a parent, and the feedback signal is a **cascade-evaluated training run** (`<metric>`, smoke→full). Children are placed in a **MAP-Elites archive across islands** (complexity × diversity axes), so a child survives by being either better or more novel, not just better. The discipline this enforces: **diversity is preserved, not collapsed** — diverse high performers co-exist instead of one local optimum winning. You are the controller: sample a parent + inspirations, spawn parallel Mutators to propose and evaluate children, place them, migrate between islands, checkpoint. Loops to a fixed compute budget or until interrupted. ## When to use Use this for parallel, diversity-preserving search over a model/program where many variants explore at once and the archive keeps the illuminated frontier. Default to broad island
- When to use
- Setup
- The controller (loop)
- Ledger
- Constraints
What does the alpha-evolve skill do?
Use when the user wants to evolve an ML model/program through population-based search rather than a single sequential refine loop — a generational evolution where parallel proposers each apply one small SEARCH/REPLACE diff to a parent, scored by a cascade-evaluated training run, and children are kept in a MAP-Elites archive across islands (with migration + checkpointing) so diverse high performers survive. A finite, bounded-parallelism re-creation of AlphaEvolve/OpenEvolve, bent for ML autoresearch. Runs to a fixed compute budget or until interrupted. Not for the sequential single-thread autor
How do I install it?
Run `npx skills add gaasher/Agent-Loop-Skills --skill alpha-evolve --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From gaasher/Agent-Loop-Skills, a repository with 146 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.