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/reportnpx skills add evo-hq/evo --skill reportgit 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/report)<a href="https://agentmods.dev/skills/evo-hq/evo/report"><img src="https://agentmods.dev/badge/skills/evo-hq/evo/report.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 | $0.00080 | $0.00979 |
| Opus 5 | $0.00040 | $0.00490 |
| Sonnet 5 | $0.00016 | $0.00196 |
| Haiku 4.5 | $0.00008 | $0.00098 |
Grade A, and why
report 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report
Report the current evo workspace from recorded state only. A report request is read-only, even if the user phrases it casually as "what happened?", "what got better?", "what should I pay attention to?", or "I just woke up".
Do not spend compute while reporting:
- Do not run
evo run,evo gate check, benchmark commands, or project eval scripts. - Do not run
python bench.py,python slurm_eval.py,sbatch,srun,squeue,sacct, orscancelto verify a result. - Do not create launcher, monitor, parsing, or analysis scripts.
- Do not edit files.
Use stored evo state instead: evo report, evo status, evo tree,
evo frontier, evo show <id>, evo diff <id>, and immutable artifacts under
.evo/run_*/experiments/<exp>/attempts/<NNN>/.
For chart requests, render the dashboard's scatter plot as a colored terminal block, one chart per run, sized to the current terminal.
What it shows
Mirrors the web dashboard's score scatter (left rail of evo dashboard):
- X = experiment creation order, Y = score
- Dot color by status: green = committed valid result, red = failed, purple = active, grey = pending / evaluated / discarded / pruned
- ★ marks the current best valid committed-result experiment.
prunedwithprune_kind=exhaustedcan still be best;prune_kind=invalidand its descendants cannot. - Yellow ring on dots that sit on the best-path spine (root → best)
- Yellow stair line traces cumulative-best across valid committed-result experiments
- ○ at the baseline for experiments that have no score yet (active / pending)
Every run in the workspace is rendered, stacked top-to-bottom, with a header line showing run_id · target · metric.
How to invoke
Run:
evo report
That is it. Print the output verbatim in your reply so the user sees the chart. Do not summarize the chart in prose — the visual is the point.
Flags:
--color always|never|auto— force or suppress ANSI color. Defaultauto(color when stdout is a TTY). Pass--color alwaysif you are piping through a host that strips TTY but renders ANSI in chat.--watch [SECONDS]— live-refresh mode (likenvidia-smi -l). Re-reads the workspace every N seconds (default 2) and redraws in place. Ctrl-C to exit. Use this when you want to babysit a running optimization without manually re-invoking the report.
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 · 86 lines · 80 tokens per session scan A deff121e50ff
report is a skill published in the GitHub repository evo-hq/evo (1,440 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 80 tokens to every session and 979 once invoked, about $0.0004 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-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-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-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.