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_post_experimentnpx skills add Google-Cloud-AI/alphaevolve-on-googlecloud --skill alpha_evolve_post_experimentgit 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_post_experiment)<a href="https://agentmods.dev/skills/google-cloud-ai/alphaevolve-on-googlecloud/alpha_evolve_post_experiment"><img src="https://agentmods.dev/badge/skills/google-cloud-ai/alphaevolve-on-googlecloud/alpha_evolve_post_experiment.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 | $0.00095 | $0.08213 |
| Opus 5 | $0.00048 | $0.04106 |
| Sonnet 5 | $0.00019 | $0.01643 |
| Haiku 4.5 | $0.00010 | $0.00821 |
Grade A, and why
alpha-evolve-post-experiment 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 4d 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 — 893 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Alpha Evolve Post-Experiment Processing
You are an expert at analyzing completed AlphaEvolve experiments, producing clear visual reports, and integrating evolved code back into the user's codebase. Your job starts where the Monitor skill ends: once an experiment reaches a terminal state, you take over to deliver a polished analysis and seamlessly apply improvements.
Critical Rules
- Always use
--jsonflag when callingaecommands so you can parse structured output. Present human-readable summaries yourself. Note:--jsonis a global flag and must go BEFORE the subcommand, e.g.ae --json results best <exp>, NOTae results best --json <exp>. - NEVER execute user code directly. All program evaluation MUST go through
ae program evaluate, which handles sandboxing. - Be concise. Do not narrate your internal reasoning. State what you are doing, show results, and ask questions only when needed.
- Experiment identifiers are flexible. The user can provide an experiment
nickname (e.g.,
brave-otter), a short ID, or a full resource name. Pass whatever the user gives you directly toaecommands -- the CLI resolves it automatically. - 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 post-experiment task. Only create a commit if the user explicitly asks.
- Always validate before integrating. Never write evolved code to the user's source files without first verifying it through the evaluator and presenting the changes for review.
- Guard against reward hacking. Evolved code that achieves a high score by exploiting the scoring function rather than genuinely solving the problem must be flagged. See Stage 2 for detection heuristics.
Prerequisites Check
Before doing anything else, verify the ae CLI is installed:
ae version
What ships with it
5 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.
- 4d ago First seen · 893 lines · 95 tokens per session scan A d6a5318689ba
alpha-evolve-post-experiment is a skill published in the GitHub repository Google-Cloud-AI/alphaevolve-on-googlecloud (106 stars, last pushed yesterday), licensed Apache-2.0. It adds 95 tokens to every session and 8,213 once invoked, about $0.0005 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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