Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Google Ads math and forecasting calculations. Activate when the user asks about budget projections, ROAS calculations, CPA targets, conversion forecasts, impression share opportunity, or any PPC-related math. No API credentials needed — works with numbers the user provides.
Create and manage Google Forms programmatically via MCP. Use when: (1) creating survey forms, (2) adding questions/sections to forms, (3) getting form responses, (4) converting AskUserQuestion to shareable forms. Covers setup, all tools, item ordering, and API limitations.
High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.
Analyze intraday tick, order book, trade-by-trade and minute chart data plus pre-market data to assess intraday volatility, price-volume structure and liquidity risk. Use when the user asks about intraday charts, trade-by-trade data, order book, minute K bars, pre-market/intraday structure, or needs intraday diagnostics.
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies
Based on the Meixiang intelligent stock screening API, screen A-shares, Hong Kong stocks, US stocks, and others by user-provided indicators, industry, or sector conditions. Returns complete CSV data with column descriptions, avoiding the use of outdated financial information.
Comprehensive toolkit for creating, analyzing, and visualizing complex networks and graphs in Python. Use for network/graph data structures, analyzing relationships between entities, computing graph algorithms (shortest paths, centrality, clustering), detecting communities, generating synthetic networks, or visualizing network topologies. Applicable to social networks, biological networks, transportation systems, citation networks, and any domain involving pairwise relationships.
Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, perform motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, and AI-assisted visual analysis for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.
Diagnose a running OXYGEN workspace: trace one cell or record value and its provenance, explain stuck or failed runs, sweep and retry table failures, inspect approvals and provider state, and explain or reduce credit spend. Read-only investigation first; use the narrow playbook for the question.
Operator toolkit for nf-core/pacsomatic matched tumor-normal workflows from BAM inputs. Use this skill when the user needs to validate run inputs, generate pacsomatic-compliant samplesheets, prepare reproducible Nextflow launch artifacts, run locally or submit to schedulers (LSF/Slurm/PBS/SGE), and triage execution failures. Triggers on requests to run pacsomatic, prepare launch commands/scripts, perform dry-run checks, or troubleshoot pipeline startup and scheduler submission errors.
Full-featured computational pathology toolkit. Use for advanced WSI analysis including multiplexed immunofluorescence (CODEX, Vectra), nucleus segmentation, tissue graph construction, and ML model training on pathology data. Supports 160+ slide formats. For simple tile extraction from H&E slides, histolab may be simpler.
Hardware-agnostic quantum ML framework with automatic differentiation. Use when training quantum circuits via gradients, building hybrid quantum-classical models, or needing device portability across IBM/Google/Rigetti/IonQ. Best for variational algorithms (VQE, QAOA), quantum neural networks, and integration with PyTorch/JAX/TensorFlow. For hardware-specific optimizations use qiskit (IBM) or cirq (Google); for open quantum systems use qutip.
Lightning-fast DataFrame library written in Rust for high-performance data manipulation and analysis. Use when user wants blazing fast data transformations, working with large datasets, lazy evaluation pipelines, or needs better performance than pandas. Ideal for ETL, data wrangling, aggregations, joins, and reading/writing CSV, Parquet, JSON files.
Use this skill for reinforcement learning tasks, including high-performance RL training, custom environment development, vectorized parallel simulation, multi-agent systems, or integration with existing RL environments (Gymnasium, PettingZoo, Atari, Procgen, etc.). Use it to implement PPO training, create PufferEnv environments, optimize RL performance, or develop policies with CNNs/LSTMs.
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical imaging data in DICOM format, extracting pixel data from medical images (CT, MRI, X-ray, ultrasound), anonymizing DICOM files, working with DICOM metadata and tags, converting DICOM images to other formats, handling compressed DICOM data, or processing medical imaging datasets. Applies to tasks involving medical image analysis, PACS systems, radiology workflows, and healthcare imaging applications.
Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.
Guidance for SQL query optimization tasks. This skill should be used when optimizing slow SQL queries, improving database performance, or rewriting queries to be more efficient. Covers query plan analysis, benchmarking strategies, and database-specific optimization techniques.
Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.
Safely refactors dbt models with downstream impact analysis. Use when restructuring dbt models for: (1) Task mentions "refactor", "restructure", "extract", "split", "break into", or "reorganize" (2) Extracting CTEs to intermediate models or creating macros (3) Modifying model logic that has downstream consumers (4) Renaming columns, changing types, or reorganizing model dependencies Analyzes all downstream dependencies BEFORE making changes.
Guidance for SAM-based cell segmentation and mask conversion tasks involving MobileSAM, mask-to-polygon conversion, CSV processing, and command-line interface design. This skill applies when working with Segment Anything Model (SAM) for biological image segmentation, converting binary masks to polygon coordinates, processing microscopy data, or building CLI tools that interface with deep learning models. (project)
Comprehensive toolkit for survival analysis and time-to-event modeling in Python using scikit-survival. Use when working with censored survival data, performing time-to-event analysis, fitting Cox models, Random Survival Forests, Gradient Boosting models, or Survival SVMs, evaluating survival predictions with concordance index or Brier score, handling competing risks, or implementing any survival analysis workflow with the scikit-survival library.
Compose semantic vectors in Scry -- embed concepts as @handles, search by cosine distance, debias with vector algebra, and diagnose signal loss. Use when the task involves: semantic search, embedding, vector, cosine distance, <=>, "X but not Y", debias, embed this concept, @handle, vibe algebra, concept vector. NOT for: word2vec training, fine-tuning embeddings, local vector databases (FAISS, Pinecone, Chroma), or plain keyword/SQL search (use scry).
Comprehensive PET selection guide covering differential privacy, homomorphic encryption, secure multi-party computation, federated learning, zero-knowledge proofs, and trusted execution environments. Includes use-case matching matrix, performance comparison, and GDPR alignment assessment for each technology.
Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.
Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.
Conduct Exploratory Data Analysis (EDA) using descriptive statistics, visualizations, and data quality checks. Use this skill when the user has a dataset and needs to understand its structure, find patterns, detect anomalies, or prepare data for further analysis — even if they say 'what does this data look like', 'find interesting patterns', 'clean this data', or 'summarize this dataset'.
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.
Profile a new tabular dataset before modeling. Find target leakage, missing data patterns, high-cardinality categoricals, near-constant features, redundant pairs, and non-linear relationships that Pearson correlation misses. Use whenever the user hands you a CSV or parquet and asks "what should I do with this?" Always run this skill before training any model on data you haven't seen before.
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.
Query the U.S. Treasury Fiscal Data API for federal financial data including national debt, government spending, revenue, interest rates, exchange rates, and savings bonds. Access 54 datasets and 182 data tables with no API key required. Use when working with U.S. federal fiscal data, national debt tracking (Debt to the Penny), Daily Treasury Statements, Monthly Treasury Statements, Treasury securities auctions, interest rates on Treasury securities, foreign exchange rates, savings bonds, or any U.S. government financial statistics.
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 don't fit in memory.
Research-driven authoring of Lytics webhook templates -- fetches destination docs, drafts and tests the transform, and emits a ready-to-go webhook job blueprint. Use when the user wants to send Lytics audience triggers or enrichment requests to a custom webhook destination (e.g., Qualtrics, Slack, custom CRM).
Weekly analytics report combining GA4, Google Search Console, and Microsoft Clarity into a premium HTML email. Use for automated Sunday cron jobs that generate weekly product analytics reports. Tracks users, sessions, pageviews, SEO performance (queries, impressions, CTR, position), UX friction (dead clicks, rage clicks, quickbacks), and week-over-week trends. Includes accountability tracking (did we do what we said?), prioritized recommendations with impact/effort ratings, and actionable checklists. Persona: $15K/month SEO consultant. Outputs: HTML email + chat summary.
Use when the user wants to integrate with the X (Twitter) API via Xquik to search tweets, look up user profiles, extract followers, run giveaway draws, monitor accounts, or access trending topics. Also use when the user mentions 'Xquik,' 'Twitter API,' 'X API,' 'tweet scraper,' 'follower extraction,' or 'Twitter monitoring.' Covers REST API, webhooks, and MCP server setup.
Turn the business data the user already has (Airtable, Notion, Google Sheets, Lark Base, Excel, CSV exports, or a database someone once set up for them) into three things in one sitting: a plain-language account of what their database actually records, a findings list of data problems and blind spots, and a one-page dashboard answering the single most urgent money question, with alerts ranked by money at risk. Use when the user wants a dashboard or a read of their data ("build me a dashboard", "turn my spreadsheet into a dashboard", "what is my data telling me", "帮我做仪表板", "分析我的数据/表格"), pastes a link or drops files from one of those products and asks what the data says, or returns with a fresh export to refresh a page built earlier. NOT for designing or restructuring the database itself (that is breakthrough-base-schema-design), NOT for setting up a second brain or vault, and NOT a live auto-updating dashboard: every page is a snapshot of one export.
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
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.