Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/gaasher/agent-loop-skills/alpha-evolvenpx skills add gaasher/Agent-Loop-Skills --skill alpha-evolvegit clone --depth 1 https://github.com/gaasher/Agent-Loop-SkillsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00159 | $0.03471 |
| Opus 5 | $0.00079 | $0.01736 |
| Sonnet 5 | $0.00032 | $0.00694 |
| Haiku 4.5 | $0.00016 | $0.00347 |
Grade A, and why
alpha-evolve scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 188 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 coverage; if quality stalls, bias selection toward exploiting top elites; if coverage stalls, bias toward empty cells. Not for the sequential autoresearch loops (one change at a time), and not for fixing a known anomaly.
The cast (both in this folder): roles/Mutator.md produces + cascade-evaluates one child (the
generation step); schemas/result.schema.json is the result a Mutator returns.
Setup
Resolve bindings interactively. If loop.run.yaml exists in the working dir, load it, confirm the
values in one line, and skip to the loop. Otherwise: on Claude Code (the AskUserQuestion tool is
available, <host> = claude-code) infer a likely value for each binding and present it as the
recommended option; on other hosts (<host> = other) ask each as a quoted plain-text prompt. Then
write loop.run.yaml (format: examples/run.example.yaml) and confirm the values before creating any
other files. <host> also decides execution: Claude Code spawns real Agent Mutators in parallel
(capped at <concurrency>); other hosts degrade to running a generation's children serially (identical
algorithm).
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 188 lines · 159 tokens per session scan A e190cfdeb587
alpha-evolve is a skill published in the GitHub repository gaasher/Agent-Loop-Skills (161 stars, last pushed 2mo ago), licensed MIT. It adds 159 tokens to every session and 3,471 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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