Open Science is a local-first, model-agnostic workbench for reproducible scientific research. Scientists use its AI agents, Python and R execution, data connectors, and traceable outputs for tasks such as literature review, analysis, simulation, and visualization across macOS, Windows, and Linux.
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 aipoch/open-science --skill evo2git clone --depth 1 https://github.com/aipoch/open-scienceWrote 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/aipoch/open-science/evo2)<a href="https://agentmods.dev/skills/aipoch/open-science/evo2"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/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/aipoch/open-science/evo2"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/evo2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00083 | $0.01472 |
| Opus 5.5 | $0.00033 | $0.00589 |
| Sonnet 5.5 | $0.00017 | $0.00294 |
| Haiku 4.5 | $0.00008 | $0.00147 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- evo2 — 98% identical, 68 lines differ
How it starts
The opening of the file, as written. The whole thing — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Evo 2 — DNA Language Model
Prerequisites
| Requirement | Minimum | Recommended |
|---|---|---|
| Python | 3.11 | 3.12 (<3.13) |
| CUDA | 12.1+ | 12.4+ |
| GPU VRAM | 24 GB (7B bf16) | 80 GB (40B) |
| RAM | 32 GB | 128 GB |
How to run
Installation
pip install evo2
# Weights pulled from Hugging Face on first model load.
Loading and scoring
from evo2 import Evo2
model = Evo2("evo2_7b") # or "evo2_40b" — see model table
seqs = ["ATCG" * 50, "GGGCTTAA" * 25]
ll = model.score_sequences(seqs) # → list[float], mean per-token log-likelihood
print(ll)
Generation
out = model.generate(
prompt_seqs=["ATGAAAGCT"],
n_tokens=256,
temperature=0.7,
)
print(out.sequences[0])
Models
| Name | Params | Context | VRAM (bf16) | Notes |
|---|---|---|---|---|
evo2_7b |
7 B | 1 M nt | ~22 GB | Default; fits on a single 24 GB+ GPU |
evo2_40b |
40 B | 1 M nt | ~78 GB | H100 80 GB or multi-GPU |
evo2_1b_base |
1 B | 8 K nt | ~6 GB | FP8 path requires sm_89+ (H100) |
Output format
score_sequences returns a list[float] (or np.ndarray) of mean log-likelihoods,
one per input sequence. More negative ⇒ less likely under the model. For variant
effect, compute Δll = ll_alt - ll_ref over a fixed window.
generate returns a GenerationOutput with .sequences (list[str]), .logits
(list[Tensor]), and .logprobs_mean (list[float]) — always populated, no flag required.
Decision tree
Need a DNA model?
│
├─ Per-base/per-sequence likelihood, generation → Evo 2 ✓
├─ Predict experimental tracks (expression, accessibility) → borzoi
└─ Protein, not DNA → fair-esm2 / esmfold2
Remote compute
7B/40B inference is GPU-bound (≥24 GB / 80 GB VRAM). Read
compute_details({provider, mode:'read'}) for an environment with evo2 +
flash-attn and a pre-cached HF weight mount, then submit:
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.
- 1mo ago Changed · -6 lines 52cf901c5321
- 1mo ago First seen · 152 lines · 83 tokens per session scan A 95f2a4439715
evo2 is a skill published in the GitHub repository aipoch/open-science (5,301 stars, last pushed today), licensed Apache-2.0. It adds 83 tokens to every session and 1,472 once invoked, about $0.0003 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.
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