Skills
97,222 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. CoPaper.AI completes a reproducible, rigorous empirical paper in 20 minutes and supports user-uploaded Skills. Maintained by CoPaper.AI from Stanford REAP.
Guide for annotating statistical significance (p-value asterisks) on comparison plots. Covers standard notation (ns, *, **, ***, ****), matplotlib bracket+asterisk implementation, and use with seaborn box/violin/bar plots. Use when preparing publication-ready figures with significance markers.
credit_transactions audit trail invariants and daily reconciliation patterns. PROACTIVELY activate for: (1) Designing a credit / balance / entitlement ledger, (2) credit_transactions row shape (delta, balanceAfter, reason, referenceType, referenceId, idempotencyKey), (3) Canonical idempotency key formats (refund:/debit:/stripe_checkout:/stripe_invoice:/stripe_refund:/stripe_signup_bonus:/dispute_hold:/dispute_restore:), (4) Daily reconciliation cron design (snapshot comparison, not full-sum), (5) Alert-only policy (never auto-correct drift), (6) Canonical refund helper pattern (single source of truth across pg-raw and Drizzle call sites), (7) Past_due block + credit balance shared preflight, (8) creditsDeducted boolean return pattern for pre- vs post-deduction error differentiation, (9) Money-safe email description based on resolvedVia flag, (10) Skip-users-without-baseline-snapshot reconciliation logic. Provides: table schema, canonical helper skeleton, reconciliation SQL, email gating pattern.
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.
This skill should be used when working with pre-trained transformer models for natural language processing, computer vision, audio, or multimodal tasks. Use for text generation, classification, question answering, translation, summarization, image classification, object detection, speech recognition, and fine-tuning models on custom datasets.
Read Twitter/X for financial research using the twitter-cli tool (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my bookmarks", "who follows", "look up @user", "what's trending about", "market sentiment on Twitter", "what are people saying about AAPL", "fintwit", any mention of Twitter/X in context of reading financial news or market research. This skill is READ-ONLY — it does NOT support posting, liking, retweeting, or any write operations.
Guides uCharts usage for cross-platform chart creation in uni-app, including line, bar, pie, radar, and mixed charts with data formatting, configuration, and responsive design. Use when the user needs to create charts with uCharts, configure chart options, or build data visualizations in uni-app applications.
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
ABP Framework domain layer patterns including entities, aggregates, repositories, domain services, and data seeding. Use when: (1) creating entities with proper base classes, (2) implementing custom repositories, (3) writing domain services, (4) seeding data.
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Search Algolia indices via the Algolia MCP server, retrieve analytics (top searches, no-result rates, click positions, user counts), and get product recommendations (bought-together, related, trending). Triggers on search, indexing, analytics, Algolia, recommendations, MCP.
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.
Processes astronomy and astrophysics data with the Astropy Python library — celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, and world coordinate systems (WCS). Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or general astronomical data analysis. Part of the AlterLab Academic Skills suite.
Applies computational methods to humanities research — text mining and NLP (LDA/BERTopic topic modeling, sentiment, named entity recognition with spaCy/NLTK), corpus linguistics (concordance, collocation, keyness), digital archives (Dublin Core, TEI XML), GIS for history, network analysis, stylometry and authorship attribution, OCR (Tesseract, Kraken, Transkribus), and data visualization (Gephi, Palladio). Use when distant-reading a literary corpus, mapping historical events or trade networks, attributing disputed authorship, digitizing historical documents, or building digital scholarly editions. Part of the AlterLab Academic Skills suite.
Query and analyze scholarly literature using the OpenAlex API across 300M+ works, retrieving papers, authors, institutions, citations, and open access status. Use when searching academic papers, tracking citations, finding works by author or institution, analyzing research trends, discovering open access publications, or running bibliometric analysis. Part of the AlterLab Academic Skills suite.
Provide direct REST API access to PubMed via the NCBI E-utilities API, supporting advanced Boolean/MeSH queries, batch processing, and citation management. Use when searching biomedical literature by MeSH terms, retrieving abstracts or PMIDs in bulk, or scripting custom PubMed queries over raw HTTP/REST — for Python workflows prefer biopython (Bio.Entrez) instead, use this for direct REST work or custom API implementations. Part of the AlterLab Academic Skills suite.
Run differential gene expression analysis on bulk RNA-seq count matrices with PyDESeq2, the Python port of DESeq2 — size-factor normalization, dispersion estimation, Wald tests, FDR (Benjamini-Hochberg) correction, and volcano/MA plots. Use when identifying differentially expressed genes between conditions from raw bulk RNA-seq counts. Part of the AlterLab Academic Skills suite.
Builds statistical plots with the seaborn Python library and pandas DataFrame integration, on attractive matplotlib-based defaults. Use for quick exploration of distributions, relationships, and categorical comparisons — box plots, violin plots, swarm/strip plots, KDE/histograms, pair plots, joint plots, regression plots, correlation heatmaps, and faceted small multiples (relplot/displot/catplot/lmplot). For interactive/hover/zoom charts defer to alterlab-plotly; for exact journal/manuscript styling (column widths, point fonts, CMYK, vector export) defer to alterlab-scientific-viz; for low-level custom matplotlib figures defer to alterlab-matplotlib (seaborn integrates with it for fine-tuning). Part of the AlterLab Academic Skills suite.
Guided statistical analysis with hypothesis-test selection, assumption checking, effect sizes, power analysis, and APA-formatted reporting using scipy.stats, statsmodels, and pingouin (Bayesian alternatives with PyMC). Use when choosing and running 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 alterlab-statsmodels. Part of the AlterLab Academic Skills suite.
Data structure for annotated matrices in single-cell analysis. Use when working with .h5ad files or integrating with the scverse ecosystem. This is the data format skill—for analysis workflows use scanpy; for probabilistic models use scvi-tools; for population-scale queries use cellxgene-census.
GRN inference from expression via GRNBoost2 (gradient boosting) or GENIE3 (Random Forest). Load matrix, filter by TFs, infer TF-target-importance links, save network. Dask-parallelized to single-cell scale. Core SCENIC component.
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Query BindingDB for measured drug-target binding affinities (Ki, Kd, IC50, EC50). Search by target (UniProt ID), compound (SMILES/name), or pathogen. Essential for drug discovery, lead optimization, polypharmacology analysis, and structure-activity relationship (SAR) studies.
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access (Bio.Entrez). Best for batch processing, custom bioinformatics pipelines, BLAST automation. For quick lookups use gget; for multi-service integration use bioservices.
Complete Birdeye API integration for real-time DeFi data across Solana and 15 other chains. Use for token prices, OHLCV charts, market discovery, on-chain trader intelligence, holder analysis, wallet portfolio & P&L, and WebSocket streams for live prices and whale alerts.
Query or write generic CawPlan business metrics (subscriptions, CawPlan tickets, QA, workflow executions, tokens/credit, API usage, ...) via the metrics store, optionally charting the result. Use when the user asks to query, chart, or plot a business metric like subscription upgrades, ticket creation/status-change counts, QA test point/report counts, workflow success/failure rates, token consumption, or API usage over time; or asks to record/write/log a business metric event. NOT for: Support Ops sheet exports/backfills/reconciliation (use `cawplan-support-metrics`), a combined product health overview mixing metrics + feedback + critical issues (use `cawplan-product-insights`), device/app install metrics (that public endpoint has been retired), AI feedback categorization (`cawplan analytics get`), ticket creation, or critical issue lists.
Use when writing application code in Node.js / JavaScript / TypeScript — or any non-JVM language (Go, PHP, Ruby, Rust, C/C++) — that connects to a data source through a CData driver, after the connection and SQL have been validated with the cdata-cli discovery skill. Covers the CData ODBC Driver edition: confirming the system-installed driver, configuring a DSN or a DSN-less connection string, and connecting via the language's ODBC bindings (e.g. the Node `odbc` package). This is the cross-language build-phase companion to cdata-cli — the fallback for any environment that speaks ODBC.
Query the CELLxGENE Census (61M+ cells) programmatically. Use when you need expression data across tissues, diseases, or cell types from the largest curated single-cell atlas. Best for population-scale queries, reference atlas comparisons. For analyzing your own data use scanpy or scvi-tools.
Leveraged buyout model for A-share and China-market LBO scenarios. Adapts the standard LBO-model methodology for Chinese debt markets, CAS accounting, and AkShare data. Use instead of the original lbo-model skill when analyzing leveraged buyouts involving Chinese targets or China-based sponsors.
Generic PowerPoint authoring skill for A-share investment analysis and pitch decks. Creates professional 路演PPT / 投资分析PPT for any A-share company using live data from iFind MCP (Tier-1) and AkShare MCP (Tier-2 fallback) tools. All company-specific values are parameterized — never hardcoded. Triggers on "A股PPT制作", "投资PPT", "制作PPT", "路演PPT", "pitch deck", "PowerPoint [company/ticker]", or any request to create slides for a Chinese stock. When invoked, use the `generate_a_share_ppt` script with --company and --ticker args.
Roll forward A-share financial models for new periods. Adapts the original roll-forward skill for Chinese financial statements, CAS conventions, and A-share reporting calendars. Triggers on "A股模型滚动", "模型更新", "roll forward model China", "roll forward", "更新财务模型", or "project [company] [period]".
Create one-page company profile slides (strip profiles) for A-share targets in pitch books and deal materials. Adapted from the original strip-profile skill for Chinese companies, A-share market data, and Chinese business terminology. Triggers on "A股公司简介", "公司概况", "strip profile China", "one-pager A-share", or "target profile for [company]".
Unit economics analysis for China-focused portfolio companies. Adapted from the original unit-economics skill for Chinese business models, pricing, and market dynamics. Triggers on "A股单位经济模型", "单元经济模型", "unit economics China", "unit economics [company]", "LTV/CAC China", or "Cohort analysis [company]".
Write variance commentary for A-share financial results. Adapts the original variance-commentary skill for Chinese financial statements, CAS conventions, and Chinese business terminology. Triggers on "A股业绩点评", "财报点评", "variance commentary China", "业绩分析", "variances [company]", or "commentary on [company] results".
Reviews your last 30 posts to find what is worth doubling down on, what is dead weight, and the gaps to fill. Use when the user says "audit my content", "what is working on my account", "what should I stop posting", "review my last 30 posts".
Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.
Parallel/distributed computing for larger-than-RAM data. Components: DataFrames (parallel pandas), Arrays (parallel NumPy), Bags, Futures, Schedulers. Scales laptop to HPC cluster. For single-machine speed use polars; for out-of-core without cluster use vaex.
Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.
Pythonic wrapper around RDKit with simplified interface and sensible defaults. Preferred for standard drug discovery including SMILES parsing, standardization, descriptors, fingerprints, clustering, 3D conformers, parallel processing. Returns native rdkit.Chem.Mol objects. For advanced control or custom parameters, use rdkit directly.
Audit concurrency correctness — transaction isolation level, lost-update / read-modify-write races, queue-worker contention (SKIP LOCKED), and idempotency for key-value / document / wide-column writes. Module M14. Feeds the Performance & Scale score.
Audit data-integrity constraints — missing NOT NULL on required columns, absent CHECK constraints for domain rules, missing UNIQUE on natural keys, and the over-nullable UNIQUE trap where NULLs silently permit duplicates. Module M5. Feeds the Design & Integrity score (Constraints category).
Audit column defaults and generated columns — application-side timestamps that should be DB defaults, non-deterministic or wrong defaults, derived values that should be GENERATED ALWAYS / computed columns instead of drift-prone duplicated data, and identity/sequence defaults. Module M6. Feeds the Design & Integrity score (Tipos category, shared with M4).
Engine selection (M0) — at design/start time, recommends a database paradigm and engine for a described workload (access patterns, consistency needs, scale, team, platform), and names the runner-up with the trade-off you're accepting. M0 is a RECOMMENDATION, not a scored audit module — it emits no findings and never contributes to the Design or Performance score. Walks the decision tree in references/engine-selection-tree.md and is honest about lock-in and operability instead of fabricating benchmarks.
Audit primary-key strategy — missing PK (severity 5), surrogate vs natural keys, UUIDv4 index fragmentation vs time-ordered UUIDv7/ULID/bigint, and int4/serial exhaustion on high-volume tables. Module M2. Feeds both the Design & Integrity (Llaves) and Performance & Scale (Escala) scores.
Migration-safety lint (M22) — the static contract the migration-safety auditor (MSA) and db-migration-writer enforce on a candidate migration BEFORE it runs. Checks reversibility (down migration present / expand-contract), lock level (does it take ACCESS EXCLUSIVE on a hot table), table rewrite (full-table rewrites on type/default changes), destructive ops (DROP COLUMN/TABLE, TRUNCATE), unsafe enum mutation, NOT NULL without default on a large table, and schema drift between the declarative artifact and migration history. Feeds the Performance & Scale score.
Audit naming conventions — inconsistent table/column casing and pluralization, ambiguous or reserved-word identifiers, untyped/opaque columns, inconsistent FK and boolean naming, and identifiers that fight the engine's case-folding rules. Module M7. Feeds the Design & Integrity score (Naming category, low weight).
Orchestrates a full database audit — detects the stack(s), dispatches read-only auditor subagents in parallel by paradigm, merges and dedupes their findings, computes the two never-blended scores (Design & Integrity + Performance & Scale) via score.mjs, and renders the report. Invoked by the audit command; not usually called directly.
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An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.