Evo is an agent plugin that turns a codebase into an automated experiment loop, discovering metrics, changing code, and testing whether those changes improve the results. It is used to optimize software through parallel subagents, tree-based exploration, shared experiment records, and optional regression or safety gates. The catalogue entries provide agent skills, hooks, commands, and other workflow components for operating Evo.
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/evo-hq/evo/infra-setupnpx skills add evo-hq/evo --skill infra-setupgit clone --depth 1 https://github.com/evo-hq/evoWrote 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/evo-hq/evo/infra-setup)<a href="https://agentmods.dev/skills/evo-hq/evo/infra-setup"><img src="https://agentmods.dev/badge/skills/evo-hq/evo/infra-setup.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 5.1 | $0.00025 | $0.00990 |
| Opus 5 | $0.00013 | $0.00495 |
| Sonnet 5 | $0.00005 | $0.00198 |
| Haiku 4.5 | $0.00003 | $0.00099 |
Grade A, and why
infra-setup 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 6d 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Infra Setup
Use this when the user wants to change where experiments run: local worktrees, pool slots, or a remote provider such as Modal, E2B, Daytona, AWS, Azure, SSH, manual, or a custom dotted-path provider.
Goals
- Be explicit about the target backend/provider.
- Check prerequisites before mutating evo config.
- Never install provider SDKs silently.
- Give one actionable auth command per provider.
- Keep provider credentials separate from benchmark runtime env.
Flow
- Identify the target:
worktreeorpoolmeans local backends.modal,e2b,ssh:..., or another remote spec meansbackend=remote.
- If the target is remote, parse the provider choice the same way evo CLI does:
modale2bdaytonaawsazuremanualssh:user@host[:port]- another built-in provider name
- dotted import path for a custom provider
- Check whether
evois on PATH and whether it is the expectedevo-hq-clipackage (evo --version). If the provider SDK is missing, evo's provider loader prints the provider-specific extra or SDK package to install; use that message rather than guessing. - For SDK-backed providers, verify the SDK import only when you can run the check in the same environment that owns the
evoexecutable. If missing, ask the user before installing it.- If
evowas installed withuv toolorpip/venv, prefer the matching extra onevo-hq-cli:uv-tool:uv tool install --reinstall 'evo-hq-cli[<provider-extra>]'venv/pip:python -m pip install 'evo-hq-cli[<provider-extra>]'
- If
evowas installed withpipx, inject the provider SDK into the sameevo-hq-clienvironment:pipx:pipx inject evo-hq-cli <provider-sdk>
- If
- Check auth and show exactly one provider-specific auth command or setup step. Use
references/provider-matrix.md. - Once prerequisites are satisfied, run the explicit config command:
evo config backend remote --provider <provider> --provider-config ...
What ships with it
1 file 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.
- 6d ago First seen · 88 lines · 25 tokens per session scan A c1ffd4aefc1f
infra-setup is a skill published in the GitHub repository evo-hq/evo (1,441 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 25 tokens to every session and 990 once invoked, about $0.0001 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.
Other skills, from other repositories
codex-autoresearch
Triage improvement work and run or resume accepted measured loops in a local project. Architecture, documentation, UX, product study, open research, taste, and one-shot fixes stay direct unless the user explicitly requests repeated measurement with a complete experiment contract.
arbor-agent-setup-intake
Setup, intake, preflight, and launch-contract phase for open-source Arbor runs. Use when confirming a target project, metric, baseline, dev/test split, config/plugin settings, branch guard, session directory, or when translating a user goal into the precise contract consumed by the coordinator.
arbor-research-agent
Public entrypoint for the Arbor skill suite. Use when a user wants to run an Arbor-style autonomous research or optimization workflow from a natural-language goal, including initial clarification of objective, target project, data, metric, evaluation, permissions, budget, run mode, and then automatic bootstrapping…
arbor-agent-executor
Executor-dispatch phase for Arbor. Use when implementing an Idea Tree node through RunExecutor or RunExecutorParallel semantics: isolated git worktree, executor prompt construction, eval metadata injection, RunTraining policy, smoke/full evaluation, report parsing, artifact persistence, tree update, and insight…
arbor-agent-ideate
Strict IDEATE-stage skill for Arbor. Use immediately after TreeView(format="constraints") when drafting Idea Tree nodes, enforcing the ideadrafting and firstprinciplesprobe behavior, depth-aware idea levels, four-line TreeAddNode hypotheses, conflict checks, and self-filtering against shallow tweaks.
arbor-agent-merge-eval
Merge and evaluation discipline for Arbor. Use for TreeSetMeta metadata, Bdev/Btest separation, eval command templates, score parsing, GitMergeBranch behavior, protected paths, required outputs, metricdirection, trunk/test score updates, medal detection, and final evaluation before stopping.