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 solublempnngit 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/solublempnn)<a href="https://agentmods.dev/skills/aipoch/open-science/solublempnn"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/solublempnn/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/solublempnn"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/solublempnn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00096 | $0.01072 |
| Opus 5.5 | $0.00038 | $0.00429 |
| Sonnet 5.5 | $0.00019 | $0.00214 |
| Haiku 4.5 | $0.00010 | $0.00107 |
Grade A, and why
solublempnn 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:
- solublempnn — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SolubleMPNN
SolubleMPNN is not a separate package — it is the ProteinMPNN architecture
retrained on a soluble-PDB subset, which shifts the output distribution away
from the surface hydrophobics that the full-PDB model happily places (because
many of them are buried at crystallographic or membrane interfaces in the
training set). Reach for it when the goal is soluble yield in a heterologous
host; stick with proteinmpnn when native-like recovery matters more, since
the soluble prior trades a few points of recovery for the surface bias. Code
and weights are MIT (github.com/dauparas/ProteinMPNN, soluble_model_weights;
also exposed via github.com/dauparas/LigandMPNN). The model is small enough to
run on CPU — for a handful of sequences on one backbone that is seconds and
usually faster than dispatching; a GPU helps for batched campaigns. Either way
the repo is cloned in-job (no PyPI dist; checkpoints bundled).
Running it
pip install torch numpy # if not already present
git clone --depth 1 https://github.com/dauparas/ProteinMPNN.git proteinmpnn
cd proteinmpnn
python protein_mpnn_run.py \
--pdb_path backbone.pdb --pdb_path_chains "A" \
--out_folder out --num_seq_per_target 16 \
--sampling_temp "0.1" --use_soluble_model
The runner uses repo-relative imports, so the cd line is load-bearing —
invoking the script by absolute path from elsewhere fails with
ModuleNotFoundError. If you want threaded designed-sequence PDBs as well,
the LigandMPNN runner accepts --model_type soluble_mpnn (see ligandmpnn
for that path; it needs ProDy in addition to torch). The flag surface is
otherwise identical to proteinmpnn (or ligandmpnn for the second form),
including the string-typed temperature and the fixed-position JSONL keyed by
PDB stem — see proteinmpnn for the parsing quirks. The repo
ships soluble weights at v_48_010 and v_48_020 only; asking for
--model_name v_48_002 --use_soluble_model errors on a missing checkpoint, so
leave --model_name at its default.
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 First seen · 88 lines · 96 tokens per session scan A c5c9b5cba266
solublempnn is a skill published in the GitHub repository aipoch/open-science (5,390 stars, last pushed today), licensed Apache-2.0. It adds 96 tokens to every session and 1,072 once invoked, about $0.0004 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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