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. 80 149 files from 1 774 authors, of which 62 489 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.
Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.
Query STRING API for protein-protein interactions (59M proteins, 20B interactions). Network analysis, GO/KEGG enrichment, interaction discovery, 5000+ species, for systems biology.
Use when you need exact symbolic math in Python — algebra, calculus, equation solving, symbolic linear algebra, or code generation via lambdify/LaTeX. Prefer NumPy or SciPy when floating-point approximations are sufficient.
Access a collection of open-source molecular design and structural biology tools on the Tamarind Bio platform, via its REST API or MCP server — no local GPUs required. Tamarind bundles popular open-source models for structure prediction (AlphaFold, Boltz, Chai, ESMFold), protein, binder, and de novo design (RFdiffusion, ProteinMPNN, BoltzGen), antibody and nanobody design and developability, protein-ligand docking (DiffDock, Autodock Vina), binding-affinity prediction, MSA generation, and molecular dynamics. Use when the user mentions Tamarind or tamarind.bio, wants to run any of these open-source tools in the cloud, references app.tamarind.bio/api or the x-api-key header, or needs to submit batches of sequences for structural or biophysical characterization.
Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.
Zero-shot time series forecasting with Google's TimesFM foundation model. Use for any univariate time series (sales, sensors, energy, vitals, weather) without training a custom model. Supports CSV/DataFrame/array inputs with point forecasts and prediction intervals. Includes a preflight system checker script to verify RAM/GPU before first use.
PyTorch Geometric (PyG) for graph neural networks — node/link/graph classification, message passing (GCN, GAT, GraphSAGE, GIN), heterogeneous graphs, neighbor sampling, and custom datasets. Use when working with torch_geometric, not for general NetworkX analytics or non-graph PyTorch models.
Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.
PyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
Direct REST API access to UniProt. Protein searches, FASTA retrieval, ID mapping, Swiss-Prot/TrEMBL. For Python workflows with multiple databases, prefer bioservices (unified interface to 40+ services). Use this for direct HTTP/REST work or UniProt-specific control.
Query the U.S. Treasury Fiscal Data REST API for federal financial data. No API key required. Use for national debt (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates, foreign exchange rates, savings bonds, or U.S. government revenue and spending statistics.
Access USPTO APIs for patent/trademark searches, examination history (PEDS), assignments, citations, office actions, TSDR, for IP analysis and prior art searches.
Use this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.
Run structured What-If scenario analysis with 4–6 branch possibility exploration (best, likely, worst, wild card, contrarian, second-order). Use when the user asks speculative what-if questions about uncertain futures, strategic forks, contingency planning, or stress-testing a decision before committing.
Use this skill to create high-quality academic papers, literature reviews, grant proposals, clinical reports, and other research and scientific documents backed by comprehensive research and real, verifiable citations. Use this skill whenever the user asks for written output such as a report, paper...etc.
Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm) where the workbook file is the primary deliverable. Use for formulas, formatting, financial models, multi-sheet workbooks, and tabular cleanup exported to Excel. Also applies to .csv/.tsv when the user wants spreadsheet output. Do NOT use for Word documents, HTML reports, standalone Python scripts, database pipelines, or Google Sheets API work.
Chunked N-D arrays for cloud storage (Zarr-Python 3). Compressed arrays, parallel I/O, S3/GCS via fsspec, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
Optimize advertising budget allocation across campaigns using marginal returns analysis. Use this skill when the user needs to distribute budget across multiple campaigns, optimize spend pacing, or maximize overall ROAS under budget constraints — even if they say 'how to split my ad budget', 'campaign budget optimization', or 'diminishing returns on ad spend'.
Explain blockchain fundamentals including distributed ledger architecture, consensus mechanisms, and block structure. Use this skill when the user needs to understand blockchain concepts, evaluate whether blockchain fits a use case, or design a blockchain-based solution — even if they say 'how does blockchain work', 'do I need blockchain', or 'distributed ledger'.
Implement BM25 ranking function for e-commerce product search relevance scoring. Use this skill when the user needs to build a text-based product search engine, improve search result relevance, or replace basic TF-IDF with a more robust ranking function — even if they say 'product search ranking', 'search relevance', or 'BM25 implementation'.
Design and implement smart contracts as self-executing programmatic agreements on blockchain. Use this skill when the user needs to build automated on-chain logic, evaluate smart contract security, or design tokenized business rules — even if they say 'smart contract development', 'automated agreement', or 'on-chain logic'.
Implement VCG mechanism for incentive-compatible ad slot allocation with truthful bidding. Use this skill when the user needs to design a truthful auction mechanism, compute externality-based payments, or understand why platforms may prefer GSP over VCG — even if they say 'truthful auction design', 'VCG payments', or 'incentive-compatible mechanism'.
Implement and select ad bidding strategies from manual CPC to automated target-CPA and target-ROAS. Use this skill when the user needs to choose a bidding strategy, set up automated bidding, or optimize bid parameters — even if they say 'what bidding strategy should I use', 'target CPA setup', or 'smart bidding configuration'.
Build CTR prediction models for estimating ad click-through rates from features. Use this skill when the user needs to predict click probability, build an ad ranking model, or evaluate ad creative performance — even if they say 'predict click rate', 'ad relevance scoring', or 'which ad will get more clicks'.
Implement Generalized Second Price auction for ad slot allocation and pricing. Use this skill when the user needs to understand search ad auctions, compute ad positions and costs-per-click, or analyze bidding dynamics — even if they say 'how does Google Ads auction work', 'ad rank calculation', or 'second price auction for ads'.
Build ARIMA models for time series forecasting with trend and seasonality decomposition. Use this skill when the user needs to forecast future values from historical sequential data, test for stationarity, or select ARIMA parameters — even if they say 'time series forecast', 'predict next month sales', or 'ARIMA model'.
Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.
Apply exponential smoothing methods for time series forecasting with weighted moving averages. Use this skill when the user needs simple, robust forecasts, implement Holt-Winters for seasonal data, or build lightweight forecasting without complex models — even if they say 'simple forecast', 'moving average prediction', or 'smoothing method'.
Design multi-objective e-commerce product ranking combining relevance, conversion, and business metrics. Use this skill when the user needs to build a product ranking system beyond text relevance, balance relevance with commercial objectives, or implement learning-to-rank — even if they say 'product sorting', 'search result ranking', or 'how to rank products'.
Conduct compensation benchmarking analysis to position salaries against market data. Use this skill when the user needs to assess pay competitiveness, build salary bands, or analyze pay equity — even if they say 'are we paying market rate', 'salary benchmarking', or 'compensation analysis'.
Implement Gale-Shapley stable matching algorithm for two-sided matching problems. Use this skill when the user needs to match candidates to positions, assign students to schools, or solve any two-sided preference matching — even if they say 'optimal job matching', 'stable assignment', or 'candidate-position pairing'.
Combine multiple forecasting models into ensemble predictions for improved accuracy. Use this skill when the user needs to improve forecast reliability, combine ARIMA/Prophet/ETS outputs, or build a robust forecasting pipeline — even if they say 'combine forecasts', 'model averaging', or 'which forecast should I trust'.
Build forecasting models with Meta's Prophet for business time series with holidays and changepoints. Use this skill when the user needs user-friendly time series forecasting, handling of missing data and holidays, or automatic changepoint detection — even if they say 'forecast with Prophet', 'business forecast', or 'easy time series model'.
Build employee turnover prediction models to identify flight risk and retention drivers. Use this skill when the user needs to predict which employees are likely to leave, identify retention risk factors, or prioritize HR interventions — even if they say 'attrition prediction', 'who is going to quit', or 'employee retention model'.
Calculate Cpk process capability index to assess whether a process meets specification requirements. Use this skill when the user needs to evaluate process capability, compare processes, or determine if quality targets are achievable — even if they say 'can our process meet spec', 'process capability', or 'Cpk calculation'.
Conduct FMEA to systematically identify, prioritize, and mitigate potential failure modes. Use this skill when the user needs to assess product or process risks, prioritize corrective actions, or build a risk register — even if they say 'failure mode analysis', 'risk assessment', 'what could go wrong', or 'RPN calculation'.
Calculate network centrality metrics to identify important nodes in graphs. Use this skill when the user needs to find key influencers, critical infrastructure nodes, or central actors in a network — even if they say 'who is most important in this network', 'key nodes', or 'network influence measurement'.
Implement Louvain community detection to discover densely connected groups in networks. Use this skill when the user needs to find communities or clusters in social/organizational networks, segment customers by interaction patterns, or analyze network modular structure — even if they say 'find groups in this network', 'community detection', or 'network clustering'.
Design and analyze factorial experiments to identify significant process factors and optimize settings. Use this skill when the user needs to systematically test factor effects, optimize a manufacturing process, or determine which variables matter most — even if they say 'which factors affect quality', 'optimize process settings', or 'design an experiment'.
Solve the influence maximization problem to select optimal seed nodes for maximum information spread. Use this skill when the user needs to choose seed users for viral campaigns, maximize network reach under a budget constraint, or compare seeding strategies — even if they say 'who should we seed first', 'maximize viral reach', or 'optimal influencer selection'.
Implement Statistical Process Control charts to monitor production process stability. Use this skill when the user needs to detect process shifts, set control limits, or distinguish common cause from special cause variation — even if they say 'process monitoring', 'control chart', or 'is our process in control'.
Implement Named Entity Recognition to identify and classify entities in text. Use this skill when the user needs to extract people, organizations, locations, dates, or custom entities from documents — even if they say 'extract names from text', 'find companies mentioned', or 'entity extraction'.
Design bundle pricing strategies using pure bundling, mixed bundling, and consumer surplus analysis. Use this skill when the user needs to set prices for product bundles, determine whether bundling increases profit, or analyze unbundling opportunities — even if they say 'should we bundle these products', 'bundle pricing', or 'package deal pricing'.
Calculate price elasticity of demand to quantify how price changes affect sales volume. Use this skill when the user needs to estimate demand sensitivity, set optimal prices, or evaluate the revenue impact of price changes — even if they say 'how sensitive are customers to price', 'will a price increase hurt sales', or 'elasticity calculation'.
Run conjoint analysis to measure how product attributes drive consumer preferences and willingness to pay. Use this skill when the user needs to quantify feature value trade-offs, estimate willingness to pay for specific features, or optimize product configuration — even if they say 'which features do customers value most', 'willingness to pay for feature X', or 'product attribute trade-offs'.
Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.
Implement text summarization using extractive and abstractive approaches. Use this skill when the user needs to condense long documents, build an automatic summarization pipeline, or compare summarization strategies — even if they say 'summarize this document', 'TLDR', or 'key points extraction'.
Implement dynamic pricing strategies that adjust prices in real-time based on demand, time, and competition. Use this skill when the user needs to build a dynamic pricing system, implement surge pricing, or optimize prices for perishable inventory — even if they say 'real-time pricing', 'surge pricing', or 'demand-based price adjustment'.
Calculate text similarity using lexical and semantic methods for matching and deduplication. Use this skill when the user needs to find similar documents, detect near-duplicates, or measure semantic closeness between texts — even if they say 'how similar are these texts', 'find duplicates', or 'semantic matching'.
Apply Bayesian averaging to rank items by combining observed ratings with prior expectations. Use this skill when the user needs to rank items with varying review counts, build a 'top rated' list that handles low-sample items fairly, or implement IMDB-style weighted rating — even if they say 'weighted average rating', 'IMDB formula', or 'ranking with prior'.
Conduct Van Westendorp Price Sensitivity Meter analysis to identify acceptable price ranges. Use this skill when the user needs to determine price boundaries for a new product, find the optimal and indifference price points, or survey-based pricing research — even if they say 'what should we charge', 'price sensitivity survey', or 'acceptable price range'.
Implement Elo rating system to rank items or players from pairwise comparison outcomes. Use this skill when the user needs to rank items from head-to-head matchups, build a competitive rating system, or evaluate relative quality from comparison data — even if they say 'player rating', 'ranking from comparisons', or 'competitive scoring system'.
Calculate Wilson Score confidence intervals for ranking items by positive proportion with sample size correction. Use this skill when the user needs to rank products by ratings, sort content by approval rate, or build a 'best rated' list that accounts for sample size — even if they say 'rank by star rating', 'best rated with few reviews', or 'confidence-adjusted rating'.
Implement TrueSkill rating system for multiplayer and team-based competitive ranking. Use this skill when the user needs to rate players in team games, handle multiplayer (non-1v1) matchups, or build a matchmaking system with uncertainty tracking — even if they say 'team rating system', 'multiplayer ranking', or 'matchmaking rating'.
Implement collaborative filtering for recommendations based on user behavior patterns. Use this skill when the user needs to build a recommendation engine from user-item interaction data, find similar users or items, or predict ratings — even if they say 'users who bought this also bought', 'similar users', or 'recommend based on behavior'.
Implement content-based recommendation by matching item features to user preference profiles. Use this skill when the user needs to recommend items based on attributes, solve the cold start problem for new items, or build recommendations without collaborative data — even if they say 'recommend similar products', 'items like this', or 'feature-based matching'.
Design hybrid recommendation systems combining multiple strategies for improved accuracy. Use this skill when the user needs to overcome single-method limitations, combine collaborative and content-based filtering, or build a production recommendation pipeline — even if they say 'combine recommendation approaches', 'best recommendation architecture', or 'cold start plus personalization'.
Claude Skills — questions
Answers built from the skills we actually parsed.