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SkillsMP는 AlterLab-IEU/AlterLab-Academic-Skills에서 240개의 skill을 수집했습니다. skill을 열어 소스와 세부 정보를 확인하세요.
수집된 skill 240개 중 40개를 표시합니다.
The AlterLab front door and multi-agent launcher — routes a task to the right AlterLab skill(s) when the user invokes the suite without naming one, and for a multi-stage goal (or on the keyword 'alterflow', aliases 'alterresearch' / 'ultralab') it CLARIFIES…
원문 언어: 영어
Runs quantitative meta-analysis — computes effect sizes (Hedges' g / standardized mean difference, log odds/risk ratios) with their variances, pools them under fixed-effect and random-effects models, quantifies heterogeneity (I-squared, tau-squared, Cochran's…
원문 언어: 영어
Handles missing data with principled methods — forces an explicit MCAR / MAR / MNAR mechanism statement, then applies multiple imputation by chained equations (MICE) with Rubin's-rules pooling of estimates and standard errors, or full-information maximum…
원문 언어: 영어
Fits and reports mixed-effects / multilevel / hierarchical models for clustered, nested, longitudinal, and repeated-measures data — random intercepts and slopes, variance components and the ICC, cross-level interactions, and GLMMs (logistic/Poisson) — using…
원문 언어: 영어
Analyzes qualitative data as a dispatched pipeline module — codebook development, thematic / framework / content analysis, and inter-coder reliability computed correctly (Krippendorff's alpha as primary via the krippendorff package or a bundled stdlib nominal…
원문 언어: 영어
Coordinates a social-science study through the stage-gated methods pipeline — question then design-gate then measurement-gate then sampling-gate then the right analysis module then inference-gate then reporting — holding a single YAML Design Passport that…
원문 언어: 영어
Gates trustworthiness before qualitative or interpretivist claims are written — the qualitative analog of the measurement gate. Checks that researcher positionality and role are stated, that the four Lincoln & Guba trustworthiness criteria are addressed…
원문 언어: 영어
Analyzes complex-sample survey data with design-based inference — declares a survey design (weights, strata, PSUs/clusters, FPC) before estimating means, totals, proportions, ratios, and quantiles, computes design-adjusted standard errors via Taylor…
원문 언어: 영어
Develops, tests, and deploys clinical machine learning models with the PyHealth healthcare AI toolkit. Use when working with electronic health records (EHR), clinical prediction tasks (mortality, readmission, drug recommendation), medical coding systems (ICD,…
원문 언어: 영어
Query and download public cancer imaging data from the NCI Imaging Data Commons (IDC) using the idc-index Python package, filtering by metadata, visualizing in-browser, and checking licenses, with no authentication required. Use when obtaining large-scale…
원문 언어: 영어
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server, returning 25+ fields per paper (methods, results, sample sizes, quality scores, conclusions). Use when running a literature review or…
원문 언어: 영어
Builds agent-based models of social systems with Mesa 3 — the current AgentSet API (model.agents.shuffle_do('step'), auto-assigned unique_id, mandatory super().__init__(seed=...)), cell spaces (mesa.discrete_space OrthogonalMooreGrid / classic mesa.space…
원문 언어: 영어
Estimates causal effects from observational and quasi-experimental data — difference-in-differences, instrumental variables, regression discontinuity, panel fixed effects, propensity-score / doubly-robust methods, and heterogeneous treatment effects (CATE) —…
원문 언어: 영어
Runs Qualitative Comparative Analysis — crisp-set (csQCA), multi-value (mvQCA), and fuzzy-set (fsQCA) — for small-to-medium-N configurational research: calibrating raw data into set membership, building and refining a truth table, and Boolean minimization…
원문 언어: 영어
Fits and evaluates measurement models — confirmatory factor analysis, full structural equation models, exploratory factor analysis, item response theory, and multi-group measurement invariance — using the verified Python stack: semopy (model syntax =~ / ~ /…
원문 언어: 영어
Applies social-network-analysis method discipline to relational data — degree/betweenness/closeness/eigenvector centrality and PageRank, community detection (Louvain and greedy-modularity native in networkx, Leiden via igraph), and inferential network models…
원문 언어: 영어
Routes a social-science study to its research design — true experiment, quasi-experiment (difference-in-differences, instrumental variables, regression discontinuity, interrupted time series, fixed effects), observational/correlational, qualitative, or mixed…
원문 언어: 영어
Audits final inferential claims against the design, sample, and uncertainty before they are written or published — refuses causal language unless the design's identifying assumption is defended (else downgrades to associational), corrects p-value and…
원문 언어: 영어
Gates measurement quality before a scale or instrument is trusted — checks that each construct is defined and operationalized, that reliability is evidenced with McDonald omega (not Cronbach alpha alone, which assumes tau-equivalence and is only a lower…
원문 언어: 영어
Gates who is sampled, how, and how many before data collection — checks that the sampling FRAME matches the target population (coverage error), that the METHOD is named (probability vs non-probability: simple random, stratified, cluster, systematic, quota,…
원문 언어: 영어
Analyzes text as social-science data — topic modeling (BERTopic with embeddings + class-based TF-IDF, LDA/NMF via scikit-learn or gensim), document embeddings (sentence-transformers), dictionary/lexicon methods, and supervised text classification — choosing…
원문 언어: 영어
Predict genome-wide functional genomics tracks from DNA sequence with Borzoi (Linder 2025) — a sequence-to-function model outputting RNA-seq, CAGE, ATAC, and ChIP coverage across long context, used to score non-coding and regulatory variant effects. Use when…
원문 언어: 영어
Apply the scGPT single-cell foundation model (Cui 2024) to annotate and embed cells — zero-shot and fine-tuned cell-type annotation, gene/cell embeddings, batch integration, and gene-regulatory / perturbation inference from AnnData. Use when annotating cell…
원문 언어: 영어
Explore a single PDF in depth — parse it once, then answer questions across its sections, figures, tables, and appendices — comparing methods across sections, extracting every instance of a pattern within the document, and reading values off its charts and…
원문 언어: 영어
Verify publication figures with a render-then-check QA pass — data-fidelity against every underlying row, axis/label floor-and-ceiling legibility, bounding-box collision detection for overlapping text/markers, and 300-dpi print-readiness. Use when proofing or…
원문 언어: 영어
Design protein sequences around bound ligands, metals, and nucleic acids with LigandMPNN (Dauparas 2023) — inverse folding that conditions on non-protein context, so binding-pocket and metal-site residues are chosen to fit the actual ligand. Use when…
원문 언어: 영어
Design protein sequences for a fixed backbone with ProteinMPNN (Dauparas 2022) — message-passing inverse folding that outputs sequences predicted to fold to a given structure, with fixed positions, tied/symmetric chains, amino-acid bias, and a soluble-model…
원문 언어: 영어
Generate de-novo protein backbones with RFdiffusion (Watson 2023) — a diffusion model for unconditional monomer generation, motif scaffolding, binder design against a target, and symmetric oligomers. Use when generating a new protein backbone from scratch,…
원문 언어: 영어
Predict protein 3D structures with AlphaFold2 via ColabFold — MMseqs2-accelerated MSAs, monomer and AlphaFold2-Multimer complex folding, and confidence-based validation (pLDDT, pTM/ipTM, PAE). Use when folding a protein sequence or complex from FASTA,…
원문 언어: 영어
Co-fold biomolecular complexes with Boltz-2, an open AlphaFold3-style model — predict protein + ligand (SMILES/CCD), protein + nucleic-acid, and multi-chain structures in one pass, with binding-affinity prediction. Use when folding a protein together with a…
원문 언어: 영어
Predict biomolecular complexes with Chai-1, an open AlphaFold3-style model that folds multi-entity assemblies (proteins, ligands, nucleic acids) from a single typed FASTA — strong on antibody–antigen and protein–ligand complexes, with optional MSA and…
원문 언어: 영어
Dispatch long-running GPU/CPU jobs to remote compute with a provider-agnostic submit → poll → harvest pattern across SLURM/HPC (sbatch, squeue, sacct) and managed APIs (Modal, RunPod, GCP Batch / Vertex AI). Use when submitting a batch job to a cluster,…
원문 언어: 영어
Extract and preprocess tiles from whole-slide images (WSI) with histolab — OpenSlide-backed slide loading, tissue detection and masks, Random/Grid/Score tile extraction, and image/morphological filters for H&E preprocessing. Use when the user needs…
원문 언어: 영어
Simulates open quantum systems with QuTiP, the Quantum Toolbox in Python, solving Lindblad master equations (mesolve), Monte Carlo trajectories (mcsolve), and unitary dynamics (sesolve). Use when studying master-equation or Lindblad dynamics, decoherence,…
원문 언어: 영어
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) — the scverse data STRUCTURE, not an analysis pipeline. Use when creating or wrangling .h5ad/zarr files, managing cell and gene annotations,…
원문 언어: 영어
Run full computational-pathology workflows with PathML — whole-slide-image (WSI) analysis across 160+ slide formats, multiplexed immunofluorescence (CODEX, Vectra, MERFISH), nucleus segmentation/classification (HoVer-Net, HACTNet), tissue- and cell-graph…
원문 언어: 영어
Run the standard single-cell RNA-seq analysis pipeline with Scanpy on AnnData — QC filtering, normalization, dimensionality reduction (PCA, UMAP, t-SNE), Leiden/Louvain clustering, marker/differential expression, PAGA trajectories, and plotting. Use when…
원문 언어: 영어
Run RNA velocity analysis with scVelo on single-cell RNA-seq data — estimate cell-state transitions from spliced/unspliced mRNA dynamics, infer trajectory direction, compute latent time, and identify driver genes. Use when adding directionality to…
원문 언어: 영어
Wraps RDKit in a high-level, pandas-friendly datamol interface with sensible defaults for everyday drug discovery — SMILES/SDF loading into DataFrames, molecule standardization, descriptors, fingerprints, Butina clustering, 3D conformer generation, scaffold…
원문 언어: 영어
Runs molecular machine learning with DeepChem — diverse featurizers, pre-built MoleculeNet benchmark datasets, and pre-trained models (ChemBERTa, GROVER) for property prediction (ADMET, toxicity, solubility) via traditional ML or graph neural networks. Use…
원문 언어: 영어