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/google-cloud-ai/alphaevolve-on-googlecloud/alpha_evolve_orchestratornpx skills add Google-Cloud-AI/alphaevolve-on-googlecloud --skill alpha_evolve_orchestratorgit clone --depth 1 https://github.com/Google-Cloud-AI/alphaevolve-on-googlecloudWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/google-cloud-ai/alphaevolve-on-googlecloud/alpha_evolve_orchestrator)<a href="https://agentmods.dev/skills/google-cloud-ai/alphaevolve-on-googlecloud/alpha_evolve_orchestrator"><img src="https://agentmods.dev/badge/skills/google-cloud-ai/alphaevolve-on-googlecloud/alpha_evolve_orchestrator.svg" alt="Measured on agentmods" height="20"></a>What 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 | $0.00120 | $0.03484 |
| Opus 5 | $0.00060 | $0.01742 |
| Sonnet 5 | $0.00024 | $0.00697 |
| Haiku 4.5 | $0.00012 | $0.00348 |
Grade A, and why
alpha-evolve-orchestrator scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
> `https://discoveryengine.googleapis.com` (e.g., via `curl` or equivalent How it starts
The opening of the file, as written. The whole thing — 373 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alpha Evolve Orchestrator
You orchestrate the full AlphaEvolve experiment lifecycle by chaining four sub-skills in sequence: Design, Runner, Monitor, and Post-Experiment. Your job is to determine where the user is in the process and seamlessly hand off between phases.
Critical Rules
- Detect the entry point. Not every user starts from scratch. Determine which phase to begin from based on what the user provides (see Entry Point Detection below).
- Never skip a required gate. Each phase has a completion gate. Do not advance to the next phase until the current gate is satisfied.
- Track state across phases. You are responsible for passing handoff artifacts between phases. Record them explicitly so nothing is lost.
- Be concise. Do not narrate your internal reasoning. State what you are doing, show results, ask questions when needed.
- Never execute user code directly. Delegate all code execution to the sub-skills, which use sandboxed evaluation.
- Never initiate version control workflows or search for bugs. Do not run version control commands, search bug trackers, draft commit messages, or ask for Bug IDs. These are irrelevant to the optimization task. Experiment files are local working artifacts. Only create a commit if the user explicitly asks.
- Never ask "what do you mean by optimize?" when the user says "optimize
my code at
<path>". This is a clear request for AlphaEvolve optimization. Proceed directly to Phase 1 Design. Only ask for clarification when the request is genuinely ambiguous (e.g., "help me with this code").
Entry Point Detection
When the user invokes this skill, determine where to start based on what they provide:
Start at Phase 1 (Design) if:
- The user describes a problem in natural language ("make this sorting function faster", "optimize this packing algorithm")
- The user provides source code to optimize but no evaluator
- The user says "set up an experiment" or "design an experiment"
- The user says "optimize my code at
<path>" — this is a clear request for end-to-end optimization. Do NOT ask "what do you mean by optimize?" — proceed directly to Phase 1 Design. - No experiment artifacts exist yet
What ships with it
2 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.
- 5d ago First seen · 373 lines · 120 tokens per session scan A bd98526b43f7
alpha-evolve-orchestrator is a skill published in the GitHub repository Google-Cloud-AI/alphaevolve-on-googlecloud (106 stars, last pushed 2d ago), licensed Apache-2.0. It adds 120 tokens to every session and 3,484 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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