PKU-YuanGroup/OpenAI4S

volcengine-datapro

Discover and call the configured Volcengine DataPro dataPro_search(query:string) MCP tool for professional-dataset queries.

原文の言語: 英語

admet-genetic

ADMET-guided genetic molecule optimization workflow from seed SMILES; use when the agent needs to build or run an RDKit/SA-Score/ADMET-AI GA pipeline for molecule optimization, enforce molecule lineage logs, render optimization-history HTML dashboards, and…

原文の言語: 英語

alphafold2

Predict protein structure for monomers and multimers with AlphaFold2 via the ColabFold runner (Mirdita et al. 2022, github.com/sokrypton/ColabFold; AlphaFold2 Jumper et al. 2021). Reach for this skill to fold a sequence or complex with the AF2/AF2-Multimer…

原文の言語: 英語

audit-dataset

Audit tabular datasets before analysis or training for schema drift, missing values, duplicate rows or IDs, target imbalance, and entity or group leakage across splits using pure-stdlib helpers.

原文の言語: 英語

bioprobench

Score an LLM's biological-protocol reasoning on the BioProBench benchmark: protocol QA, step ordering, error detection, protocol generation, and LLM-judged error reasoning; or generate the responses.

原文の言語: 英語

boltz

Structure prediction for protein, nucleic-acid, and small-molecule complexes with Boltz-2 (Passaro & Wohlwend et al. 2025, github.com/jwohlwend/boltz). Reach for this skill to validate designed binders against a target, to co-fold a protein with a SMILES or…

原文の言語: 英語

borzoi

Predict genome-wide functional tracks (RNA-seq, CAGE, DNase, ChIP) from DNA sequence with Borzoi. Use this skill when: (1) Scoring the regulatory effect of a variant on expression/accessibility, (2) Generating predicted coverage tracks for a locus, (3)…

原文の言語: 英語

catalyst-sar-screening

HARD-LOCKED Catalyst-Design-Agent FAIRChem UMA (uma-s-1p1, oc20) SAC SAR screening for dissolution potential / adsorption / overpotential. Always call run_pipeline with the user's metals/metrics into a fresh workdir and present ONLY that run's…

原文の言語: 英語

chai1

Structure prediction for protein, nucleic-acid, and small-molecule complexes with the Chai-1 foundation model (Chai Discovery 2024, github.com/chaidiscovery/chai-lab). Reach for this skill to predict an antibody-antigen or protein-ligand complex from a single…

原文の言語: 英語

diffdock

Predict small-molecule binding poses with DiffDock-L (Corso et al. 2023/2024, github.com/gcorso/DiffDock) — blind diffusion docking that places a ligand into a protein pocket without a predefined search box and ranks the samples with a learned confidence…

原文の言語: 英語

esmfold2

Biohub ESMFold2 / ESMFold2-Fast all-atom co-folding (Candido et al. 2026, github.com/Biohub/esm). Single-sequence and MSA modes; protein, DNA, RNA, ligand (CCD/SMILES), modified residues. FoldBench Ab-Ag 50-55%, PPI 70-77% DockQ-pass. Also covers the…

原文の言語: 英語

evaluate-model

Evaluate binary classification or regression models with confusion-matrix metrics, tie-aware ROC AUC, regression errors, and deterministic bootstrap confidence intervals; emphasizes held-out data, uncertainty, baselines, and subgroup checks.

原文の言語: 英語

evidence-walkthrough

Run the reference end-to-end research pass — fixed database query, local analysis, versioned artifacts with lineage, then an exported evidence package that verifies in a clean environment. Use as the first-run demonstration, as a benchmark case, or when a…

原文の言語: 英語

evo2

Score, embed, and generate DNA sequences with Evo 2, a long-context genomic foundation model. Use this skill when: (1) Computing per-nucleotide or per-sequence likelihoods for variant effect scoring, (2) Embedding genomic windows for downstream…

原文の言語: 英語

example-stats

descriptive-statistics helpers — summary (mean/std/median), quantile, zscore normalization, and Pearson correlation on plain Python number lists (no pandas/numpy).

原文の言語: 英語

fair-esm2

Embed proteins with Meta AI's ESM-2 (`fair-esm` package). Use this skill when: (1) Extracting per-residue or per-sequence embeddings for downstream ML, (2) Masked-LM likelihood / mutation effect scoring, (3) Contact prediction from a sequence.

原文の言語: 英語

figure-composer

Compose one publication-grade multi-panel figure. Entry from a one-line claim + data refs, OR from an existing figure via `derive_outline(png)`. Runs a per-figure loop: outline (12-col grid, per-panel ask + label_budget) → fan-out one sub-agent per panel…

原文の言語: 英語

figure-style

Publication-grade figure correctness and legibility rules. Load before drawing any plot and call `apply_figure_style()` — sets a role-mapped font-size ladder, outward ticks, frameless legends, and 300-dpi output. The skill is a checklist, not a house look:…

原文の言語: 英語

indication-dossier

Generate a therapeutic indication dossier. Covers the patient population, epidemiology, disease biology, standard of care, regulatory precedent, and landmark clinical trials.

原文の言語: 英語

ligandmpnn

Inverse-fold a backbone with ligand, nucleic-acid, and metal context using LigandMPNN (Dauparas et al. 2023, github.com/dauparas/LigandMPNN). Reach for this skill to redesign the residues lining a binding pocket around a bound small molecule or cofactor, to…

原文の言語: 英語

literature-review

Find, verify, and synthesize scientific literature — from "what's the seminal paper for X" through full multi-source reviews. Covers grounding claims in real retrieved sources, avoiding fabricated citations, handling retractions, and calibrating confidence to…

原文の言語: 英語

mineral-spectra-analysis

Raman mineral mixture spectra analysis pipeline for unknown mixed-mineral spectra; preprocess noisy spectra once, iteratively match residual peaks against a reference spectral library, unmix components with NNLS, diagnose reliability, write reports, and…

原文の言語: 英語

openfold3

Structure prediction using OpenFold3, an open-weights PyTorch reproduction of AlphaFold3 from the AlQuraishi Lab. Use this skill when predicting protein/nucleic-acid/ligand complex structures with an Apache-2.0-licensed AF3 reimplementation.

原文の言語: 英語

paper-narrative

Judge and reshape the STORY a paper's figures tell. Input is the work itself — manuscript (or abstract) + figure deck — no hand-written brief. `derive_paper_brief(abstract, captions)` extracts pitch/vision/per-figure-claims; a handling-editor reviewer on the…

原文の言語: 英語

pdf-explore

Use this skill when the user has attached a PDF, paper, report, or other document and the answer needs content from more than one place in it: summarize the methods or any other section, compare sections, find where a topic is discussed, read a value or label…

原文の言語: 英語

plan-ml-experiment

Plan reproducible machine-learning experiments with leakage-safe random, grouped, or chronological splits; deterministic configuration fingerprints; dataset checksums; seeds, baselines, ablations, and artifact manifests.

原文の言語: 英語

protein-design-mcp

Compose auditable protein-design operations through the configured OpenAI4S protein-design MCP connector: target-conditioned RFdiffusion backbone generation, constrained ProteinMPNN sequence design, monomer or complex structure prediction, Rosetta scoring and…

原文の言語: 英語

protein-mutation-enhancement

Deterministic protein gain-of-function mutation workflow: build single, double, and higher-order mutant libraries; merge ESM sequence-effect scores, structure metrics from ESMFold-class models, property/function scores; rank candidates; and decide whether to…

原文の言語: 英語

proteinmpnn

Inverse-fold a protein backbone (PDB structure) into amino-acid sequence with ProteinMPNN (Dauparas et al. 2022, github.com/dauparas/ProteinMPNN). Reach for this skill to run sequence design on RFdiffusion backbones, to redesign one chain of a PDB while…

原文の言語: 英語

reaction-atom-mapping

Map atoms and changed bonds for a complete reaction with RXNMapper. Use for reactant/product correspondence and reaction-centre audits, not target-only retrosynthesis or feasibility.

原文の言語: 英語

reaction-condition-recommendation

Recommend ranked catalyst, reagent, and solvent labels for a fully specified reaction using the reviewed Parrot USPTO checkpoint. Not for unknown reactions or lab procedures.

原文の言語: 英語

reaction-forward-prediction

Predict ranked products from reactants and reagents with ReactionT5v2-forward; use for outcome prediction or round-trip recovery. Product rank is not reaction feasibility.

原文の言語: 英語

reaction-yield-estimation

Estimate yield for a fully specified reactant/reagent/product record with ReactionT5v2-yield. Use for in-domain screening, not route success; flag domain shift and uncalibrated uncertainty.

原文の言語: 英語

remote-compute-nvidia

Run GPU jobs on NVIDIA NIM microservices via host.compute.create('byoc:nvidia', ...). Covers both forms — self_hosted (an nvcr.io NIM container on a local GPU with --gpus all) and hosted (the fully-managed integrate.api.nvidia.com gateway, no local GPU) —…

原文の言語: 英語

remote-compute-ssh

Submit→poll .result()→harvest workflow for the user's SSH/SLURM hosts. Load once you've decided to dispatch remote.

原文の言語: 英語

retrosynthesis-planning

Search multi-step retrosynthesis routes from a target to stock with AiZynthFinder, then audit and rank route trees. Use for recursive planning, not mapping, forward prediction, conditions, or yield.

原文の言語: 英語

rfdiffusion

Generate de novo protein backbones with RFdiffusion for protein-target binders, hotspot-conditioned interfaces, motif scaffolding, partial diffusion, or symmetric assemblies. Use this skill when a design workflow needs reproducible RFdiffusion contigs,…

原文の言語: 英語

scgpt

Embed and annotate single-cell expression data with scGPT, a foundation model for single-cell biology. Use this skill when: (1) Producing cell embeddings from an AnnData for clustering/integration, (2) Zero-shot or fine-tuned cell-type annotation, (3)…

原文の言語: 英語

scvi-tools

Probabilistic single-cell RNA-seq with scvi-tools — scVI for a batch-corrected latent space, scANVI for semi-supervised label transfer, and Bayesian differential expression. Reach for this skill to integrate scRNA-seq batches, embed cells for clustering,…

原文の言語: 英語

single-cell-rna-analysis

Reproducible Scanpy workflow for human or mouse 10x scRNA-seq and snRNA-seq count matrices: single-sample descriptive QC, clustering and annotation, or comparative donor-aware pseudobulk DE and Milo DA; plus preflight validation, optional explicitly requested…

原文の言語: 英語