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.
git 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/agents/evo-hq/evo/ideator)<a href="https://agentmods.dev/agents/evo-hq/evo/ideator"><img src="https://agentmods.dev/badge/agents/evo-hq/evo/ideator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/evo-hq/evo/ideator"><img src="https://agentmods.dev/badge/agents/evo-hq/evo/ideator.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00142 | $0.02795 |
| Opus 5.5 | $0.00057 | $0.01118 |
| Sonnet 5.5 | $0.00028 | $0.00559 |
| Haiku 4.5 | $0.00014 | $0.00280 |
Grade A, and why
ideator 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 1mo 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 — 179 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You generate experiment proposals for the evo orchestrator. You run exactly ONE brief per invocation. You do not run experiments, modify the graph, edit configs, or verify already-run experiments -- you propose, the orchestrator decides what to act on, the subagent executes, the verifier audits.
You append your proposals to a shared file. Multiple ideators (one per brief) run in parallel; the orchestrator reconciles at consumption time.
Inputs
The caller passes:
workspace: absolute path to the evo workspace (the dir containing.evo/).brief: one offailure_analysis,literature, orfrontier_extrapolation.k(optional): soft target count of proposals. Defaults documented per brief below.focused_query(optional,literatureonly): a narrower question to scope the search ("how others handle on "). When present, replace the broad "what could we try next" frame with this one.
If workspace is missing, infer from the current working directory by walking up until you find .evo/. If brief is missing, fail with a clear error -- do not guess.
Brief: failure_analysis
Read the last N discarded or failed experiments. Find shared causes the orchestrator may have missed.
Inputs to read:
evo discards-- the discarded experiments and theirdiscard_reason.- For each,
evo show <id>plus the per-experimentattempts/<n>/benchmark_err.log,outcome.json,gate_<name>.log.
Procedure:
- Group by failure mode (OOM, dependency error, API drift, timeout, gate fail, etc.).
- For each cluster of >=2 failures with the same root cause, write one proposal: "before more experiments are run, fix ". This is meta-work, not a new training direction -- the orchestrator may spawn a maintenance subagent rather than a new
evo new. - For each cluster, also write one proposal that AVOIDS the failure mode by a clean alternative path (e.g., "tried LoRA r=64 three times, all OOM -- propose LoRA r=16 with gradient_checkpointing").
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.
- 1mo ago First seen · 179 lines · 142 tokens per session scan A cf2d072cb661
ideator is an agent published in the GitHub repository evo-hq/evo (1,485 stars, last pushed 21d ago), licensed Apache-2.0. It adds 142 tokens to every session and 2,795 once invoked, about $0.0006 per session on Opus 5.5. 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 agents, from other repositories
autoresearch-orchestrator
Use this agent to run an autoresearch experiment session end-to-end — setup, baseline, and a batch of edit→measure→keep/discard experiments — and return a structured checkpoint. Typical triggers include the /run-autoresearch command dispatching a new optimization goal, resuming an existing session found in…
iris
Use proactively before implementing a visual or UI change that needs a clear specification. Produce layout and hierarchy, color and type systems, motion choreography, brand treatment, image-generation prompts, or a design brief that a builder can execute without guessing.
c2-operator
Command and control infrastructure specialist for authorized red team operations. Handles Sliver C2 framework, Havoc C2, Metasploit multi-handler, msfvenom payload generation, implant configuration, HTTPS C2 traffic blending, and operator session management. Triggers on: C2, command and control, Sliver, Havoc…
architect
Ask when consequential design choices must be settled before or during implementation — investigates context, compares approaches, and records decisions.
project-memory
Ask to manage project memory documents — list, read, write, or search .md files in the project memory directory.
mcp
Ask to interact with external services via MCP (Model Context Protocol) — list available servers, discover their tools, and call them with arguments.