By AlterLab-IEU
Automate end-to-end academic research: write scientific papers with LaTeX/Markdown, search and cite literature, analyze data with Python libraries (pandas, PyTorch), run bioinformatics pipelines, generate publication-quality figures, create posters and presentations, and manage citations and references. Includes tools for grant writing, peer review, and clinical decision support.
Build, slice, concatenate, read, and write AnnData annotated data matrices (obs, var, X, layers, obsm, uns) for single-cell analysis in the scverse ecosystem. Use when handling .h5ad files, managing cell and gene annotations, or wrangling single-cell matrices — this is the data-format skill, for analysis workflows use scanpy, for probabilistic models use scvi-tools, for population-scale queries use cellxgene-census. Part of the AlterLab Academic Skills suite.
Infer gene regulatory networks (GRNs) from expression matrices using arboreto's scalable GRNBoost2 and GENIE3 tree-ensemble algorithms with Dask-distributed computation. Use when analyzing bulk or single-cell RNA-seq transcriptomics to map transcription-factor-to-target-gene regulatory interactions, build adjacency networks, or run the GRN-inference step of a SCENIC pipeline on large datasets. Part of the AlterLab Academic Skills suite.
Manipulate biological sequences, parse FASTA/GenBank/PDB files, run phylogenetics, and access NCBI/PubMed programmatically via Biopython (Bio.SeqIO, Bio.Entrez, Bio.PDB, Bio.Blast). Use when scripting custom bioinformatics pipelines, batch-processing sequence files, automating BLAST, or fetching records from Entrez — for quick one-off database lookups use gget, for unified multi-service integration use bioservices. Part of the AlterLab Academic Skills suite.
Query 40+ bioinformatics web services through one consistent Python API with bioservices (UniProt, KEGG, ChEMBL, Reactome, Ensembl, NCBI and more). Use when a workflow must hit multiple databases together, map identifiers across services, or run cross-database analyses — for quick single-database lookups use gget, for sequence and file manipulation use biopython. Part of the AlterLab Academic Skills suite.
Query the CZ CELLxGENE Census (61M+ cells) programmatically via cellxgene-census and TileDB-SOMA, slicing expression by tissue, disease, or cell type and returning AnnData. Use when pulling reference single-cell RNA-seq data from the largest curated public atlas, running population-scale queries, or benchmarking your data against a reference — for analyzing your own dataset use scanpy or scvi-tools. Part of the AlterLab Academic Skills suite.
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📢 Featured in awesome-claude-skills (5.7k ⭐)
Organized across 13 research domains — from bioinformatics to digital humanities
Research Pipeline · Scientific Databases · Bioinformatics · Data Science · Visualization · Clinical Research · and more
Explore Skills » · Quick Start · Domain Overview · Contributing · Report Bug
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Built by AlterLab Creative Technologies Laboratory
Not tied to any specific university — these skills work for any researcher, anywhere. |
🎯Plug & PlayDrop a |
🧠Domain ExpertEach skill transforms Claude |
🔬Real FrameworksBuilt on actual scientific |
🌐UniversalWorks for any researcher |
npx claudepluginhub alterlab-ieu/alterlab-academic-skills --plugin alterlab-visualizationAcademic paper writing skills for ML conferences (NeurIPS, ICML, ICLR, AAAI)
A growing collection of Claude-compatible academic workflow bundles. Covers scientific figures, manuscript writing and polishing, reviewer assessment, citation retrieval, data availability, paper reading, literature search, response letters, paper-to-PPTX conversion, and evidence-grounded Chinese invention patent drafting. Rules are organized as reusable skill folders with explicit workflows and quality checks.
Production-grade academic research pipeline for Claude Code: research → write → review → revise → finalize. 4 skills, 27 modes, 39-agent ensemble, v3.7.3 + v3.8 L3 claim-faithfulness gate, v3.9.0 cross-index triangulation, v3.10 triangulation policy layer, v3.11 deterministic citation verification gate (#182).
Research-team agents for Claude Code: supervisor, analysis-implementer, paper-writer, figure-descriptor, reviewer, literature-curator.
Academic research agents — hypothesis generation, experiment design, paper drafting, peer review simulation, and more.
Multi-agent orchestrator for academic writing: 12 specialist agents and 30 writing principles for review, research, drafting, polishing, bibliography auditing, and literature surveys.