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
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aipoch/open-sciencenpx agentmods add skills/aipoch/open-science/ligandmpnnWrote 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/ligandmpnn)<a href="https://agentmods.dev/skills/aipoch/open-science/ligandmpnn"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/ligandmpnn/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/ligandmpnn"><img src="https://agentmods.dev/badge/skills/aipoch/open-science/ligandmpnn.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 Prompt Injection · line 113 Large whitespace padding was detected (a block of blank lines or a long run of spaces). This can push injected instructions below or to the right of the visible area so a human reviewer never sees them while the agent still reads them. Manual review of the hidden content is recommended.Fix: Remove the large whitespace padding (blank-line blocks or long space runs) and review any content hidden below or to the right of it. Keep skill files compact and reviewable so no instructions can be
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 | $0.00100 | $0.01682 |
| Opus 5.5 | $0.00040 | $0.00673 |
| Sonnet 5.5 | $0.00020 | $0.00336 |
| Haiku 4.5 | $0.00010 | $0.00168 |
Grade A, and why
ligandmpnn 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:
- ligandmpnn — 100% identical, 22 lines differ
How it starts
The opening of the file, as written. The whole thing — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LigandMPNN
LigandMPNN extends the ProteinMPNN graph with non-protein atoms — small
molecules, nucleic acids, and metals are visible to the network — so it is the
right inverse-folding tool whenever the design surface includes a bound ligand
or cofactor that vanilla proteinmpnn would ignore. The same run.py is also
the most convenient runner for the other MPNN families because, unlike the
original ProteinMPNN script, it threads designs back onto the input structure
and writes PDBs alongside the FASTA. Code and weights are MIT
(github.com/dauparas/LigandMPNN). The model is small enough to run on CPU —
for a handful of designs on one structure that is seconds and usually faster
than dispatching, so the normal path is local with
pip install torch numpy biopython ProDy ml_collections dm-tree; a GPU helps
for batched campaigns.
Running it
pip install torch numpy biopython ProDy ml_collections dm-tree
git clone --depth 1 https://github.com/dauparas/LigandMPNN.git ligandmpnn
cd ligandmpnn
sed -i 's/np\.int\b/np.int64/g' openfold/np/residue_constants.py # repo pins numpy 1.23; alias removed in >=1.24
bash get_model_params.sh ./model_params
python run.py \
--model_type ligand_mpnn \
--checkpoint_ligand_mpnn ./model_params/ligandmpnn_v_32_010_25.pt \
--pdb_path complex.pdb \
--out_folder out \
--batch_size 8 --number_of_batches 4 \
--temperature 0.1 \
--fixed_residues "A45 A46 A47 A48"
Residue selections are space-separated {chain}{resnum} tokens inside one
quoted string ("A45 A46 B10"; insertion codes append directly, "B82A").
That is the format for --fixed_residues and --redesigned_residues;
--bias_AA_per_residue and --omit_AA_per_residue instead take a path to a
JSON file whose keys use the same {chain}{resnum} form, and
--chains_to_design is comma-separated ("A,B"). If you want to redesign only the pocket, naming the pocket residues
in --redesigned_residues is usually shorter than fixing everything else.
Under --out_folder you get seqs/<stem>.fa (headers carry
overall_confidence and ligand_confidence), backbones/<stem>_{1..N}.pdb
with the designed sequence threaded onto the input coordinates, and — with
--pack_side_chains 1 — full-atom packed models in packed/. The threaded
PDBs are the reason to prefer this runner even for protein-only jobs.
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 · 124 lines · 100 tokens per session scan A 57ec2d14fafd
ligandmpnn is a skill published in the GitHub repository aipoch/open-science (5,357 stars, last pushed today), licensed Apache-2.0. It adds 100 tokens to every session and 1,682 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.
Other skills, from other repositories
journal-club-ppt
Instructions for turning a scientific paper PDF into a journal-club presentation or literature report. They require reconstructing the paper's scientific logic before creating the slides.
figure-style
Correctness and legibility checklist for publication figures, plus a matplotlib sidecar. Load before plotting anything and call applyfigurestyle() (role-mapped font ladder, outward ticks, frameless legends, 300-dpi saves, CJK-safe fonts). Covers data fidelity, label budgets, axis/colour/type rules, chart choice by…
research-integrity-audit
A research-integrity review of a scientific manuscript's figures and reported numbers. It looks for possible image reuse or alteration and numerical patterns that may indicate data inconsistencies.
analysis-workflow
Organize multi-step scientific analyses into reproducible, self-contained modules. Use for workflows such as QC→PCA→DEG→GSEA that produce scripts, inputs, figures, tables, and methods. Creates a stable module layout, records exact inputs/parameters/package and database versions in each module README, keeps large data…
audit-biomedical-paper-evidence
A reviewer for life-science and biomedical papers, PDFs, figures, captions, and screenshots. It examines the scientific question, experiments, evidence, causality, and limitations.
literature-review
Retrieve, verify, and synthesize scientific literature. Use for seminal-paper lookups, evidence summaries, method comparisons, and gap analyses. Every citation must come from a live lookup, never from memory; retractions are checked; the deliverable is argued prose with resolvable DOI links.