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/finetuningnpx skills add evo-hq/evo --skill finetuninggit 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/finetuning)<a href="https://agentmods.dev/skills/evo-hq/evo/finetuning"><img src="https://agentmods.dev/badge/skills/evo-hq/evo/finetuning.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 | $0.04106 |
| Opus 5 | $0.00048 | $0.02053 |
| Sonnet 5 | $0.00019 | $0.00821 |
| Haiku 4.5 | $0.00010 | $0.00411 |
Grade A, and why
finetuning 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 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.
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 — 234 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Finetuning
Priors, not rules. Only firm guardrails: held-out eval you never train on, no leakage, trust evo's recorded numbers over the run's self-report. Override anything else against the gate.
Pick the technique by reward shape
Decide on the reward first, technique second. Choosing the comfortable technique over the matching one is the most common failure.
| Reward shape | Technique |
|---|---|
| Verifiable (exact match, unit tests, parser-decidable) | RL (GRPO / RLOO / PPO) — reward includes format, so the model learns to emit verifier-acceptable shape |
| Preference pairs (chosen vs rejected) | DPO / KTO / ORPO — cheaper than full RL, no rollouts |
| Demonstrations only (curated traces, chat data) | SFT — install format/tone/capability the base lacks |
| Have a scorer + want SFT stability | RFT — sample, filter by reward, SFT on survivors |
"SFT-then-RL" is not a law. For a competent base model on a verifiable benchmark, RL-from-base often beats SFT-then-RL end-to-end.
Research the literature before the first commit
The decision tree above is the structural prior. The empirical answer for this model on this benchmark usually has a recent paper, blog, or HF Space recipe behind it -- and what beats baseline on a 4B base model in 2026 is not what the agent's pre-training data captures. Before picking the technique for exp_0001 (the first experiment after baseline), invoke evo:ideator with a literature brief:
Task(
subagent_type="evo:ideator",
prompt="brief=literature\n"
"model_family=<e.g. Qwen3-4B-Base, Llama-3.1-8B-Base>\n"
"benchmark=<name + URL/paper if known>\n"
"objective=<one line: what beats baseline looks like>\n"
"constraints=<budget, data sources allowed, gated models forbidden, etc>"
)
The ideator returns ranked proposals with references (arXiv, HF Hub, GitHub, blogs). Read them before picking from the reward-shape table. A paper showing GRPO-from-base works on <model_family> for a similar verifiable benchmark beats applying the table cold.
What ships with it
8 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 · 234 lines · 95 tokens per session scan A ea19415079d8
finetuning is a skill published in the GitHub repository evo-hq/evo (1,441 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 95 tokens to every session and 4,106 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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