Claude Skills
The open format is called Agent Skills and works in Claude Code, Codex, Cursor and other agents — most people know it as Claude Skills.
Every Agent Skill we could find on GitHub, deduplicated by content. 86 696 files from 2 113 authors, of which 68 413 are unique — the rest is the same skill repackaged into someone else's repository. For each one: what it weighs in tokens, whether it ships runnable scripts, and which MCP servers it needs.
Computes mass-spectral similarity and identifies compounds for metabolomics with matchms — comparing mass spectra, scoring similarity (cosine, modified cosine), and searching spectral libraries to annotate unknowns. Use when matching MS/MS spectra, identifying metabolites, or library searching; for full LC-MS/MS proteomics pipelines use pyopenms. Part of the AlterLab Academic Skills suite.
Queries the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biomedical relationships across genes, drugs, diseases, phenotypes, pathways, and biological processes. Use when exploring drug-disease or gene-disease links, building disease-centric knowledge subgraphs, or sourcing relations for drug repurposing and precision-medicine analyses. Part of the AlterLab Academic Skills suite.
Loads Therapeutics Data Commons (TDC, PyTDC) AI-ready drug-discovery datasets and benchmarks — ADME, toxicity, drug-target interaction (DTI), scaffold splits, and molecular oracles for therapeutic ML and pharmacological prediction. Use when fetching a standardized benchmark dataset, applying scaffold or cold-split evaluation, or sourcing labeled molecules for ADMET, toxicity, or DTI modeling. Sources data, splits, and oracles only — defer molecular featurization (ECFP/fingerprints), model training, and transformers to a molecular-ML skill (e.g. deepchem). Part of the AlterLab Academic Skills suite.
Featurizes molecules for machine learning with molfeat (100+ featurizers) — ECFP/MACCS/MAP4 fingerprints, RDKit and Mordred physicochemical descriptors, and pretrained embeddings (ChemBERTa, ChemGPT, GIN) exposed as scikit-learn transformers that convert SMILES into feature vectors. Use when turning molecules into ML-ready feature matrices for QSAR/QSPR or virtual screening, or benchmarking fingerprint against descriptor and embedding representations; for training models and MoleculeNet benchmarks on those features prefer alterlab-deepchem, and for low-level fingerprint or descriptor primitives prefer alterlab-rdkit. Part of the AlterLab Academic Skills suite.
Applies medicinal-chemistry filters with the medchem library — drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, and molecular complexity metrics for compound prioritization and library cleanup. Use when filtering or triaging a compound library, flagging PAINS or reactive groups, or assessing drug-likeness of candidate molecules. Part of the AlterLab Academic Skills suite.
Runs and analyzes molecular dynamics simulations with OpenMM and MDAnalysis — setting up protein and small-molecule systems, assigning force fields, running energy minimization and production MD, and analyzing trajectories (RMSD, RMSF, contact maps, free energy surfaces). Use when simulating protein or ligand dynamics, equilibrating a system, or computing trajectory metrics for structural biology, drug binding, or biophysics. Part of the AlterLab Academic Skills suite.
Provides the RDKit cheminformatics toolkit for low-level, fine-grained molecular primitives — SMILES/SDF parsing, descriptors (MW, LogP, TPSA), fingerprints, substructure/SMARTS search, 2D/3D coordinate generation, similarity, and reaction handling. Use when custom sanitization, specialized fingerprint or descriptor algorithms, reaction enumeration, or conformer generation demand direct API control; for a high-level pandas-friendly wrapper over RDKit prefer alterlab-datamol, and for turning molecules into ML feature vectors prefer alterlab-molfeat. Part of the AlterLab Academic Skills suite.
Drives the Rowan cloud quantum-chemistry platform via its Python API for computational chemistry — pKa prediction, geometry optimization, conformer searching, molecular property calculations, protein-ligand docking (AutoDock Vina), and AI protein cofolding (Chai-1, Boltz-1/2), with cloud compute and no local setup. Use when running DFT or semiempirical methods, neural network potentials (AIMNet2), molecular property or protein-ligand binding predictions, or automated computational chemistry pipelines. Part of the AlterLab Academic Skills suite.
Generates professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings — biomarker-stratified patient cohort analyses with outcomes and evidence-based treatment recommendation reports with decision algorithms, supporting GRADE evidence grading, statistical analysis (hazard ratios, survival curves, waterfall plots), biomarker integration, and regulatory compliance, output as publication-ready LaTeX/PDF. Use when building a CDS document, cohort analysis, or treatment recommendation report for drug development, clinical research, or evidence synthesis, or when GRADE grading, hazard ratios, survival/waterfall plots, or biomarker stratification are requested. Part of the AlterLab Academic Skills suite.
Reads, writes, and manipulates DICOM (Digital Imaging and Communications in Medicine) medical imaging files with the pydicom Python library. Use when reading/writing/modifying DICOM data, extracting pixel data from CT, MRI, X-ray, or ultrasound images, anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM to other formats, handling compressed DICOM, or processing medical imaging datasets for PACS systems, radiology workflows, and healthcare imaging applications. Part of the AlterLab Academic Skills suite.
Builds PyTorch-native graph neural networks with TorchDrug for molecules and proteins, exposing custom GNN architectures, task/dataset abstractions, molecular generation, retrosynthesis planning, and knowledge-graph reasoning. Use when developing custom graph model layers, predicting protein properties from sequence or structure, or building retrosynthesis and drug-repurposing pipelines; for ready-made featurizers, MoleculeNet benchmarks, and pre-trained models with less code prefer alterlab-deepchem. Part of the AlterLab Academic Skills suite.
Prepares ISO 13485 certification documentation for medical device Quality Management Systems (QMS) — gap analysis of existing documentation, Quality Manuals, required procedures and work instructions, and Medical Device Files. Use for ISO 13485 QMS documentation, conducting a documentation gap analysis, drafting a Quality Manual or SOP/work instruction, assembling a Medical Device File, identifying missing documentation for medical device certification, or when medical device regulations, QMS certification, FDA QMSR, or EU MDR are mentioned. Part of the AlterLab Academic Skills suite.
Writes comprehensive clinical reports — case reports (CARE guidelines), diagnostic reports (radiology, pathology, lab), clinical trial reports (ICH-E3, SAE, CSR), and patient documentation (SOAP notes, H&P, discharge summaries) — with templates, regulatory compliance (HIPAA, FDA, ICH-GCP), and validation tools. Use when drafting a case report for journal publication, a radiology/pathology/lab diagnostic report, an ICH-E3 clinical study report (CSR) or SAE narrative, or SOAP/H&P/discharge patient records needing regulatory-compliant formatting. Part of the AlterLab Academic Skills suite.
Processes and analyzes physiological biosignals with the NeuroKit2 Python toolkit — ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements, or when computing heart rate variability (HRV), event-related potentials, complexity measures, autonomic nervous system assessment, or multi-modal physiological signal integration for psychophysiology research. Part of the AlterLab Academic Skills suite.
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, NDC, ATC), physiological signals (EEG, ECG), healthcare datasets (MIMIC-III/IV, eICU, OMOP), or implementing deep learning models for healthcare (RETAIN, SafeDrug, Transformer, GNN). Part of the AlterLab Academic Skills suite.
Generates concise (3-4 page), focused medical treatment plans in LaTeX/PDF format across all clinical specialties — general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management — using SMART goal frameworks, evidence-based interventions with minimal citations, HIPAA compliance, and professional formatting. Use when drafting a brief, actionable patient treatment or care plan with measurable SMART goals and structured follow-up for any specialty. Part of the AlterLab Academic Skills suite.
Audits and repairs Markdown link health across a skills repo via a four-tier pipeline (config hardening, intra-repo file-ref fixes, external URL substitutions, residual exclusions) and enforces a Tier 3 substitution guardrail that prevents regressions of previously-passing links; designed for lychee-based GitHub Actions link checkers but generalizes to markdown-link-check and similar tools. Use when the request mentions link audit, dead links, link health, lychee, broken links, link checker, markdown link audit, link-health audit, 404 audit, check-links failing, CI link-check, or 連結健檢, 死鏈, 失效連結, 斷鏈檢查. Part of the AlterLab Academic Skills suite.
Verifies that every entry in a bibliography ACTUALLY EXISTS by cross-checking it against four keyless public scholarly APIs (Crossref, OpenAlex, Semantic Scholar, arXiv) with a polite mailto identifier, resolving DOI/arXiv IDs, fuzzy-matching title and authors (difflib SequenceMatcher ratio >=0.70), flagging retractions marked in Crossref (update-to) or OpenAlex (is_retracted), and emitting per-entry JSON verdicts mapped to the AlterLab citation-hallucination taxonomy (TF/PAC/IH/PH/SH). Accepts BibTeX, a DOI/arXiv ID list, or free-form references; degrades gracefully offline by emitting 'unverified' verdicts and never silently passing. Use when the request mentions verify citations, check references, fabricated or hallucinated references, fake DOI, retraction check, bibliography audit, or reference existence check. Does NOT write or draft papers — for authoring a manuscript (whose citation-check mode inserts citations) prefer alterlab-paper-writer instead. Part of the AlterLab Academic Skills suite.
Simulates a full multi-reviewer journal review PANEL — 5 personas (Editor-in-Chief + 3 peer reviewers + a Devil's Advocate) debate a manuscript and produce a consensus Editorial Decision (accept/minor/major/reject) plus a prioritized Revision Roadmap. Modes: full, re-review (verify revisions addressed prior comments), quick, methodology-focus, Socratic guided. Use for: simulate peer review, mock review panel, editorial review before submission, multiple reviewer perspectives, re-review of a revised manuscript, or 'critique my paper hard'. For a single-reviewer referee report use alterlab-peer-review; for rubric/grade scoring use alterlab-scholar-eval; to write/revise the paper use alterlab-paper-writer. Part of the AlterLab Academic Skills suite.
Runs a 13-agent deep research pipeline for rigorous academic work on any topic across 7 modes (full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis), covering research-question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk-of-bias assessment, meta-analysis, APA 7.0 report compilation, editorial and devil's-advocate review, ethics review, and post-research literature monitoring. Use when the request mentions research, deep research, literature review, systematic review, meta-analysis, PRISMA, evidence synthesis, fact-check, guide my research, help me think through, or 研究, 深度研究, 文獻回顧, 文獻探討, 系統性回顧, 後設分析, 事實查核, 引導我的研究, 幫我釐清, 幫我想想, 我不確定要研究什麼, 研究方向, 研究主題. Part of the AlterLab Academic Skills suite.
Drafts and revises academic papers through a 12-agent pipeline with hardened LaTeX output (apa7 document class, justified text, table column-width formula, centered bilingual abstracts, standardized font stack, PDF compiled from LaTeX), supporting IMRaD, literature review, theoretical, case study, policy brief, and conference paper structures, APA 7.0 (default), Chicago, MLA, IEEE, and Vancouver citation formats, bilingual zh-TW plus EN abstracts, and multi-format output (LaTeX, DOCX, PDF, Markdown). Use when the request mentions write paper, academic paper, paper outline, write abstract, revise paper, check citations, convert to LaTeX, guide my paper, parse reviews, revision roadmap, or 寫論文, 學術論文, 論文大綱, 寫摘要, 修改論文, 檢查引用, 引導我寫論文, 帶我規劃論文, 逐章規劃, 論文架構, 審查意見, 修訂路線圖. Its citation-check mode formats and inserts citations while drafting; for a standalone anti-hallucination check that cited references actually exist prefer alterlab-citation-verifier instead. Part of the AlterLab Academic Skills suite.
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 the goal with a few questions, SELECTS the skills the task needs, and runs a dynamic multi-agent workflow composing them (via alterlab-workflow-orchestration, alterlab-research-pipeline, or alterlab-ssci-orchestrator). Triggers on 'use AlterLab skills', 'which AlterLab skill for X', 'is there an AlterLab skill for…', a multi-stage research goal, 'alterflow …', or any generic AlterLab request where the user does not know skill names. It always asks clarifying questions before executing a multi-step run. Use when someone references AlterLab generically, describes a multi-stage goal, or fires the alterflow keyword; when the user already names a specific skill, defer to that skill directly. Part of the AlterLab Academic Skills suite.
Orchestrates the full academic research pipeline (research, write, integrity check, review, revise, re-review, re-revise, final integrity check, finalize), coordinating alterlab-deep-research, alterlab-paper-writer, and alterlab-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Use when the request mentions academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, or complete paper workflow. Part of the AlterLab Academic Skills suite.
Supervises theses and dissertations end to end — structure guidance from proposal through defense, chapter-by-chapter writing support (introduction, literature review, methodology, results, discussion), supervision strategies, committee management, defense and viva voce preparation, timeline planning, feedback integration, examiner-expectation guidance, and formatting (APA 7, Chicago, university styles). Use when the request mentions thesis, dissertation, supervision, defense preparation, viva, proposal defense, thesis structure, thesis chapter, literature review chapter, methodology chapter, results chapter, discussion chapter, thesis timeline, committee, thesis formatting, or dissertation proposal. Part of the AlterLab Academic Skills suite.
Composes existing AlterLab skills into multi-agent agentic workflows using current Claude Code subagent and Claude Agent SDK orchestration patterns: parallel subagent fan-out, sequential pipelines, judge panels, adversarial verification, and loop-until-clean review cycles. Maps each pattern onto real skills (alterlab-research-pipeline, alterlab-deep-research, alterlab-citation-verifier, alterlab-paper-reviewer, alterlab-peer-review) with copyable delegation prompts, agent-definition frontmatter, and SDK query() snippets. Use when the request mentions multi-agent, subagents, agent team, parallel agents, orchestration, pipeline of skills, judge panel, adversarial verification, devil's advocate, loop until clean, chaining skills, dispatching agents, or composing skills into a workflow. Part of the AlterLab Academic Skills suite.
Designs courses and teaching materials using backward design (Wiggins & McTighe), constructive alignment (Biggs), and Bloom's taxonomy alignment, generating rubrics, formative and summative assessments, syllabi, lesson plans, inclusive-pedagogy guidance, and online/hybrid course architecture. Use when the request mentions course design, syllabus, learning outcomes, rubric, assessment design, lesson plan, backward design, constructive alignment, Bloom's taxonomy, curriculum mapping, course redesign, inclusive pedagogy, hybrid course, or online course design. Part of the AlterLab Academic Skills suite.
Scales pandas/NumPy workflows beyond memory with Dask distributed computing — parallel DataFrames, arrays, delayed task graphs, and cluster execution. Use when existing pandas/NumPy code must run on larger-than-RAM data or across clusters, for parallel file processing, distributed ML, or integration with existing pandas code. For out-of-core analytics on a single machine prefer vaex; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.
Creates, analyzes, and visualizes complex networks and graphs in Python with NetworkX. Use when working with network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing topologies — applicable to social, biological, transportation, citation, and any pairwise-relationship networks. This is classical graph analytics, not deep learning — for training graph neural networks (GCN/message passing, node/edge/graph classification on Cora-style data) use alterlab-torch-geometric instead. Part of the AlterLab Academic Skills suite.
Fast in-memory DataFrame analytics with Polars — lazy evaluation, parallel execution, and an Apache Arrow backend for datasets that fit in RAM. Use when pandas is too slow but data still fits in memory, for 1-100GB datasets, ETL pipelines, or a faster pandas replacement. For larger-than-RAM data prefer dask or vaex. Part of the AlterLab Academic Skills suite.
Scales reinforcement learning with PufferLib — high-throughput parallel training (PuffeRL), vectorized environments, and native multi-agent systems achieving 2-10x speedups over standard implementations. Use when scaling RL to millions of steps per second, running vectorized or multi-agent setups, building custom PufferEnv tasks, or integrating game environments (Atari, Procgen, NetHack, PettingZoo). For standard single-agent algorithm implementations (PPO/SAC/DQN) or quick prototyping prefer alterlab-stable-baselines3. Part of the AlterLab Academic Skills suite.
Exploratory data analysis (EDA) on a scientific data file — auto-detects the format, runs structure/quality/statistics checks, and writes a markdown EDA report with downstream recommendations. Use when asked to "explore", "analyze", "summarize", "profile", or "QC" a data file, or to understand its structure/content/quality before deciding what analysis to run. Covers tabular (.csv .tsv .xlsx .parquet), arrays (.npy .npz .hdf5 .h5 .mat .fits), sequence/genomics (.fasta .fastq .sam .bam .vcf .bed .gff .gtf .h5ad), microscopy (.tif .nd2 .czi .lif .ims .dcm .nii), spectroscopy/MS (.mzML .mzXML .mgf .fid .jdx), chemistry (.pdb .cif .mol .sdf .xyz .gro), and proteomics/metabolomics (.pepXML .mzid .mzTab). For zero-shot forecasting of a series use alterlab-timesfm; to create/configure a chunked cloud array store use alterlab-zarr. Part of the AlterLab Academic Skills suite.
Multi-objective optimization with pymoo — NSGA-II, NSGA-III, MOEA/D, Pareto-front computation, constraint handling, and standard benchmarks (ZDT, DTLZ). Use when solving multi-objective or constrained optimization problems, computing Pareto-optimal trade-offs, or tackling engineering design problems with competing objectives. Part of the AlterLab Academic Skills suite.
Scalable deep-learning training with PyTorch Lightning — organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, build data pipelines and callbacks, log to W&B or TensorBoard, and run distributed training (DDP, FSDP, DeepSpeed). Use when structuring PyTorch training loops, scaling neural-network training across GPUs/TPUs, or adding checkpointing, logging, and distributed strategies. Part of the AlterLab Academic Skills suite.
Bayesian modeling and probabilistic programming with PyMC — hierarchical models, MCMC (NUTS) sampling, variational inference, LOO/WAIC model comparison, and posterior predictive checks. Use when fitting Bayesian or hierarchical models, estimating posteriors and credible intervals, running probabilistic inference, or comparing models with LOO/WAIC. Part of the AlterLab Academic Skills suite.
Survival analysis and time-to-event modeling in Python with scikit-survival. Use when working with censored survival data, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating predictions with concordance index or Brier score, handling competing risks, or implementing any time-to-event workflow. Part of the AlterLab Academic Skills suite.
Classical machine learning in Python with scikit-learn — algorithms, preprocessing, pipelines, and best-practice reference documentation. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, feature preprocessing, or building ML pipelines. Part of the AlterLab Academic Skills suite.
Process-based discrete-event simulation in Python with SimPy — processes, queues, shared resources, and time-based events. Use when simulating systems where entities contend for shared resources over time, such as manufacturing systems, service operations, network traffic, or logistics. Part of the AlterLab Academic Skills suite.
Model interpretability and explainability with SHAP (SHapley Additive exPlanations) — feature importance and plots (waterfall, beeswarm, bar, scatter, force, heatmap). Use when explaining ML model predictions, computing feature importance, debugging models, analyzing bias or fairness, comparing models, or implementing explainable AI across tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model. Part of the AlterLab Academic Skills suite.
Trains single-agent reinforcement learning agents with Stable-Baselines3 — PPO, SAC, DQN, TD3, DDPG, and A2C behind a scikit-learn-like API. Use for standard single-agent RL experiments, quick prototyping, well-documented algorithm implementations on Gymnasium environments, or adding callbacks and evaluation. For high-throughput parallel training, multi-agent systems, or custom vectorized environments prefer alterlab-pufferlib. Part of the AlterLab Academic Skills suite.
Statistical modeling in Python with statsmodels — OLS, GLM, mixed models, and ARIMA with detailed diagnostics, residuals, and inference. Use when fitting specific model classes for econometrics, time series, or rigorous inference with coefficient tables and confidence intervals. For guided statistical test selection with APA reporting prefer statistical-analysis. Part of the AlterLab Academic Skills suite.
Guided statistical analysis with hypothesis-test selection, assumption checking, power analysis, and APA-formatted reporting. Use when choosing the appropriate statistical test for data, verifying test assumptions, computing power/sample size, or producing APA-style results for academic research. For implementing specific models programmatically prefer statsmodels. Part of the AlterLab Academic Skills suite.
Zero-shot univariate time-series forecasting with Google's TimesFM foundation model, producing point forecasts and prediction intervals from CSV/DataFrame/array inputs, with a preflight system checker for RAM/GPU. Use to forecast any univariate series (sales, sensors, energy, vitals, weather) without training a custom model. Part of the AlterLab Academic Skills suite.
Symbolic mathematics in Python with SymPy — solve equations algebraically, perform calculus (derivatives, integrals, limits), manipulate algebraic expressions, work with symbolic matrices, and generate executable code from formulas. Use when exact symbolic results are needed rather than numerical approximations, or for physics, number-theory, and geometry computations involving variables and parameters. Part of the AlterLab Academic Skills suite.
Graph Neural Networks with PyTorch Geometric (PyG) — node and graph classification, link prediction, GCN, GAT, and GraphSAGE layers, heterogeneous graphs, and molecular property prediction. Use when building or training GNNs for geometric deep learning on graph-structured data. Part of the AlterLab Academic Skills suite.
Pre-trained transformer models with Hugging Face Transformers for NLP, computer vision, audio, and multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, or fine-tuning transformer models on custom datasets. Part of the AlterLab Academic Skills suite.
Nonlinear dimensionality reduction with UMAP — fast manifold learning for 2D/3D visualization, clustering preprocessing (e.g., HDBSCAN), and supervised or parametric UMAP. Use when projecting high-dimensional data to low dimensions for visualization, embedding generation, or as a preprocessing step before clustering. Part of the AlterLab Academic Skills suite.
Out-of-core tabular analytics with Vaex for billion-row datasets that exceed RAM — lazy evaluation, fast aggregations, big-data visualization, and ML on a single machine. Use when working with large CSV/HDF5/Arrow/Parquet files, computing fast statistics on massive datasets, visualizing big data, or building ML pipelines that do not fit in memory. For distributed clusters prefer dask; for in-memory speed prefer polars. Part of the AlterLab Academic Skills suite.
Chunked, compressed N-dimensional arrays for cloud storage with Zarr — parallel I/O, S3/GCS integration, and NumPy/Dask/Xarray compatibility. Use when storing or reading large N-D scientific arrays, streaming chunked data to/from cloud object stores, or building large-scale scientific computing pipelines. Part of the AlterLab Academic Skills suite.
Search the bioRxiv preprint server and retrieve paper metadata or download PDFs via its API. Use when finding life sciences preprints by keywords, authors, DOI, date ranges, or categories, or when conducting a biology literature review of not-yet-peer-reviewed work. Part of the AlterLab Academic Skills suite.
Access the AlphaFold DB of 200M+ AI-PREDICTED protein structures — retrieve models by UniProt accession, download PDB/mmCIF files, and analyze prediction confidence metrics (pLDDT, PAE). Use when a UniProt ID needs a computationally predicted 3D structure or when no experimental structure exists, for homology modeling, protein engineering, or structure-based drug discovery; for EXPERIMENTALLY determined structures (X-ray, cryo-EM, NMR) prefer alterlab-pdb, and for protein sequences, annotations, or accession ID mapping prefer alterlab-uniprot instead. Part of the AlterLab Academic Skills suite.
Search and retrieve preprints from arXiv via the Atom API by keywords, authors, arXiv IDs, date ranges, or subject categories. Use when finding or fetching papers in physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, or economics, or resolving an arXiv ID to its metadata and PDF. Part of the AlterLab Academic Skills suite.
Query BindingDB for measured protein-ligand binding affinities (Ki, Kd, IC50, EC50) via its keyless REST API or the full TSV download, searching by target (UniProt ID), compound (SMILES), or pathogen. Use when looking up experimental binding constants, profiling inhibitors of a protein target, doing lead optimization, polypharmacology analysis, or structure-activity relationship (SAR) studies; for curated bioactivity mining or drug-like compound library screening at scale prefer alterlab-chembl instead. Part of the AlterLab Academic Skills suite.
Access the BRENDA enzyme database via its SOAP API to retrieve kinetic parameters (Km, kcat, Ki), reaction equations, organism data, and substrate-specific enzyme information indexed by EC number. Use when looking up enzyme kinetics, turnover numbers, or substrate specificity for biochemical research and metabolic pathway analysis. Part of the AlterLab Academic Skills suite.
Query cBioPortal via its keyless REST API for cancer genomics across TCGA, GENIE, MSK-IMPACT and hundreds of studies — somatic mutations, copy-number alterations (GISTIC), mRNA/protein expression, structural variants, and patient-level clinical/survival data. Use when asked how often a gene is mutated/amplified/deleted in a tumor type, to profile oncogenes or tumor suppressors across cancers (pan-cancer alteration frequency), to pull patient-level mutations joined to OS/clinical outcomes, or to validate a cancer target from cohort genomics. For germline variant pathogenicity use alterlab-clinvar; for mutational-signature (SBS) decomposition use alterlab-cosmic; for CRISPR/RNAi gene-dependency use alterlab-depmap; for aggregated target-disease evidence use alterlab-opentargets. Part of the AlterLab Academic Skills suite.
Query ChEMBL via the chembl_webresource_client Python client for curated bioactive molecules and drug-like compound libraries at scale — search compounds by structure or physicochemical properties, retrieve bioactivity measurements (IC50, Ki, EC50), and find inhibitors of a target. Use when screening chemical libraries, mining curated bioactivity for a protein, running SAR studies, or sourcing medicinal-chemistry data; for measured protein-ligand binding affinities (Ki/Kd/IC50) prefer alterlab-bindingdb instead. Part of the AlterLab Academic Skills suite.
Query NCBI ClinVar via the E-utilities API or FTP for the clinical significance (pathogenicity) of human germline genetic variants, searching by gene, variant, condition, or genomic position and interpreting ACMG/AMP classifications and review-status star ratings. Use when assessing whether a variant is pathogenic, likely pathogenic, VUS, likely benign, or benign, resolving conflicting interpretations, or annotating a VCF with ClinVar clinical significance. For population allele frequencies by ancestry use alterlab-gnomad; for somatic cancer mutation frequencies use alterlab-cosmic. Part of the AlterLab Academic Skills suite.
Query ClinicalTrials.gov via its API v2 to search trials by condition, drug, location, recruitment status, or phase and retrieve trial details by NCT ID. Use when finding interventional or observational studies, checking trial status and eligibility for patient matching, or exporting clinical trial records for research. Part of the AlterLab Academic Skills suite.
Access ClinPGx pharmacogenomics data (the successor to PharmGKB) to query gene-drug interactions, CPIC/DPWG dosing guidelines, drug labels, and pharmacogene records. Use when interpreting pharmacogenes (CYP2D6, CYP2C19, TPMT, DPYD, SLCO1B1), looking up genotype-guided drug dosing, checking PGx drug-safety associations (e.g. HLA-B*57:01 and abacavir), or supporting precision medicine and clinical pharmacogenomics decisions. For star-allele definitions/frequencies see PharmVar; for germline/somatic variant pathogenicity see alterlab-clinvar. Part of the AlterLab Academic Skills suite.
Access the COSMIC catalogue of somatic mutations in cancer to query somatic mutations, the Cancer Gene Census, mutational signatures, and gene fusions (authentication required). Use when curating known cancer driver genes, looking up recurrent somatic mutations in a gene, or interpreting mutational signatures for cancer research and precision oncology. Not for germline pathogenicity calls (use alterlab-clinvar) or interactive cohort visualization like OncoPrints and survival from study data (use alterlab-cbioportal). Part of the AlterLab Academic Skills suite.
Query Google Data Commons for public statistical data aggregated from global sources, resolving geographic entities and pulling time-series statistics. Use when working with demographic data, economic indicators, health statistics, or environmental data — population counts, GDP figures, unemployment rates, disease prevalence — or when resolving places to DCIDs and exploring relationships between statistical entities. Part of the AlterLab Academic Skills suite.
Claude Skills — questions
Answers built from the skills we actually parsed.