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 skills add naity/FM4Life --skill evo2git clone --depth 1 https://github.com/naity/FM4LifeWrote 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/naity/fm4life/evo2)<a href="https://agentmods.dev/skills/naity/fm4life/evo2"><img src="https://agentmods.dev/badge/skills/naity/fm4life/evo2/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/skills/naity/fm4life/evo2"><img src="https://agentmods.dev/badge/skills/naity/fm4life/evo2.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 | $0.00153 | $0.02860 |
| Opus 5.5 | $0.00061 | $0.01144 |
| Sonnet 5.5 | $0.00031 | $0.00572 |
| Haiku 4.5 | $0.00015 | $0.00286 |
Grade A, and why
evo2 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 — 289 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evo2: Genome-Scale DNA Foundation Model
Overview
Evo2 is a DNA language model from Arc Institute that operates at single-nucleotide resolution with up to 1 million base pair context. It is trained on 8.8 trillion tokens from OpenGenome2 — sequences spanning all domains of life.
This is a DNA model, not a protein model. Input is raw nucleotide sequence (A/C/G/T). For protein tasks, use ESM2 or ESM-C instead.
Core capabilities:
- Variant effect scoring — zero-shot log-likelihood scoring of SNPs, indels, regulatory variants
- Sequence embeddings — genomic representations for downstream ML (classification, regression)
- Sequence generation — autoregressive DNA sequence design from a prompt
- Positional entropy — per-nucleotide uncertainty across a sequence
- Perplexity analysis — sliding window perplexity for detecting unusual regions
Architecture: StripedHyena 2 — hybrid attention + gated convolutions. Not a standard Transformer. Attention only at layers 3, 10, 17, 24, 31; the rest are Hyena convolution blocks.
Installation
# Full install (all models including 20B/40B)
conda install -c nvidia cuda-nvcc cuda-cudart-dev
conda install -c conda-forge transformer-engine-torch=2.3.0
pip install flash-attn==2.8.0.post2 --no-build-isolation
pip install evo2
# Light install (7B models only — no FP8 required)
pip install flash-attn==2.8.0.post2 --no-build-isolation
pip install evo2
Requirements: Python 3.11–3.12, CUDA 12.1+, Linux (WSL2 with caveats).
Model Selection
| Checkpoint | Context | Params | Hardware | Use case |
|---|---|---|---|---|
evo2_1b_base |
8K | 1B | Any GPU | Testing, quick iteration |
evo2_7b_base |
8K | 7B | Any GPU | Good quality, short contexts |
evo2_7b_262k |
262K | 7B | Any GPU | Mid-range genomic context |
evo2_7b |
1M | 7B | Any GPU | Best default |
evo2_20b |
1M | 20B | H100 (FP8) | High accuracy |
evo2_40b |
1M | 40B | Multi-H100 | Maximum accuracy |
evo2_7b_microviridae |
8K | 7B | Any GPU | Phage/viral sequences |
What ships with it
3 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.
- 1mo ago First seen · 289 lines · 0 tokens per session scan A 9ec7989465d2
evo2 is a skill published in the GitHub repository naity/FM4Life (2 stars, last pushed 6mo ago), licensed MIT. It adds 153 tokens to every session and 2,860 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 2026-08-31.
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