Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Manage Databricks workspace connections: check current workspace, switch profiles, list available workspaces, or authenticate to a new workspace. Use when the user mentions "switch workspace", "which workspace", "current profile", "databrickscfg", "connect to workspace", or "databricks auth".
Build professional financial services data packs from various sources, including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs
Use when adding local data persistence to a Python project — caching, relational storage (simple, complex/OLTP, or analytical/OLAP), document storage, full-text search, vector/embedding search, background job queues, cron/scheduled tasks, pub/sub/event streaming, graph data, blob storage, or time-series data — and no explicit \"evaluate the best DB for this\" request was made. Gives the default technology per category, chosen for permissive licensing, pytest-local testability with no docker/cloud, and low-boilerplate LLM-agent-friendly APIs, so picks stay consistent across projects instead of drifting session to session.
Designing Data-Intensive Applications (DDIA) distilled reference guide by Martin Kleppmann. MUST be loaded when: designing database schemas, choosing storage engines, implementing replication or partitioning, handling distributed transactions, building batch/stream processing pipelines, choosing consistency models, implementing consensus, designing data flow architectures, evaluating trade-offs between availability and consistency, encoding/serialization decisions, data modeling (relational vs document vs graph), building fault-tolerant systems, or any system design and architecture discussion involving data-intensive applications. Trigger on: database design, replication, partitioning, sharding, transactions, isolation levels, consistency, consensus, CAP theorem, batch processing, stream processing, MapReduce, Kafka, event sourcing, CDC, OLTP, OLAP, B-tree, LSM-tree, data warehouse, schema evolution, encoding formats, distributed systems, fault tolerance, leader election, quorum.
Debug TensorFlow and Keras issues systematically. This skill helps diagnose and resolve machine learning problems including tensor shape mismatches, GPU/CUDA detection failures, out-of-memory errors, NaN/Inf values in loss functions, vanishing/exploding gradients, SavedModel loading errors, and data pipeline bottlenecks. Provides tf.debugging assertions, TensorBoard profiling, eager execution debugging, and version compatibility guidance.
Multiagent AI system for scientific research assistance that automates research workflows from data analysis to publication. This skill should be used when generating research ideas from datasets, developing research methodologies, executing computational experiments, performing literature searches, or generating publication-ready papers in LaTeX format. Supports end-to-end research pipelines with customizable agent orchestration.
Bulk RNA-seq DE with R/Bioconductor DESeq2. Negative binomial GLM, empirical Bayes shrinkage, Wald/LRT tests, multi-factor designs, Salmon tximeta import, apeglm LFC shrinkage, MA/volcano/heatmap viz. R gold standard. Use pydeseq2-differential-expression for Python; use edgeR for TMM normalization.
Access the DexPaprika API, CLI, and streaming service to query DEX data including networks, pools, tokens, and trading activity. Use this skill when making HTTP requests to api.dexpaprika.com or streaming.dexpaprika.com, or when using dexpaprika-cli for blockchain DEX information.
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage. Fast-turnaround format focusing on beat/miss analysis, key metrics, updated estimates, and revised thesis. Includes 1-3 summary tables and 8-12 charts. Use when user requests "earnings update", "quarterly update", "earnings analysis", "Q1/Q2/Q3/Q4 results", or post-earnings report.
This skill should be used when the user asks to "check ETH balance", "query ERC-20 balance", "get wallet balance", "check token holdings", "query Etherscan", or mentions Etherscan API, blockchain balance queries, or multi-chain balance lookups.
Turn messy logs, notebooks, run metadata, and partial conclusions into a clear research update with next actions. Use after intense experimentation, for lab notes, handoff, weekly updates, resuming interrupted work, or when run history needs to become decisions rather than scattered impressions.
Full lifecycle management of Feishu Bitable: building from scratch plus day-to-day CRUD operations. **Use this Skill when**: (1) the user says "build a Bitable", "make an XX table", "build an XX system" (2) the user describes business requirements and wants to implement them with Bitable (3) records in a Bitable need to be queried, added, modified, or deleted (4) fields, views, or data tables need to be managed (5) data needs to be imported or updated in bulk (6) the user mentions "多维表格", "bitable", "数据表", "记录", or "字段"
Extracts hidden or encoded text from GCODE files by analyzing toolpath geometry and coordinate data. This skill should be used when tasks involve decoding text from 3D printing files, recovering embossed or engraved text from GCODE, or CTF-style challenges involving GCODE analysis. Applies to any task requiring geometric reconstruction of text from CNC or 3D printer movement commands.
Analyzes the user's genetic data from a VCF file. Use when the user asks about their genetics, inherited traits, predispositions, substance metabolism (caffeine, alcohol, medications), athletic ability, disease risks, or gene-based nutrition.
NCBI GEO access via GEOparse and E-utilities. Search by keyword/organism/platform, download GSE series matrices, parse GPL annotations, extract GSM metadata, load expression matrices into pandas. For single-cell use cellxgene-census; for multi-DB access use gget-genomic-databases.
CLI/Python toolkit for rapid bioinformatics queries. Preferred for quick BLAST searches. Access to 20+ databases: gene info (Ensembl/UniProt), AlphaFold, ARCHS4, Enrichr, OpenTargets, COSMIC, genome downloads. For advanced BLAST/batch processing, use biopython. For multi-database integration, use bioservices.
Use for scientific AI/ML discovery and implementation across biology, chemistry, physics, astronomy, climate, genomics, materials science, medicine, ecology, engineering, mathematics, drug discovery, protein design, weather modeling, single-cell analysis, PDE solving, and related research domains. Helps find and use curated Hugging Science and Hugging Face resources, including datasets via `datasets`, models via `transformers` or the HF Inference API, Spaces via `gradio_client`, and blog posts for methodology. Trigger when the user asks for scientific datasets/models, fine-tuning data, scientific ML benchmarks, domain ML tools, or AI tools for research.
Performs discounted cash flow valuation using the appropriate model variant (DDM, FCFE, or FCFF) with configurable growth stages. Produces year-by-year cash flow projections, terminal value, equity bridge (subtract debt, add cash, subtract option value), per-share intrinsic value, and sensitivity analysis. Use when valuing a company intrinsically, building a DCF model, estimating fair value, or when user mentions DCF, discounted cash flow, intrinsic value, terminal value, or free cash flow valuation.
Configure Litestar stores and the store registry for caching, server-side sessions, rate limiting, and other key-value state with explicit backend selection, bytes-safe data handling, TTL and renewal policy, namespacing, registry wiring, and lifecycle cleanup. Use when a Litestar app depends on `MemoryStore`, `FileStore`, `RedisStore`, `ValkeyStore`, or `StoreRegistry`. Do not use for relational persistence, domain repositories, or response-caching policy details that belong in database or caching-focused skills.
Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.
Register, update, or re-sync data sources for MFS so they become searchable — postgres / mysql / mongo / snowflake / bigquery, github / jira / linear / notion / hubspot / zendesk, slack / discord / gmail / feishu, s3 / gdrive / web / file. Use whenever the user wants to ADD a new data source to MFS, change an existing connector's config, re-ingest / re-index a source, list registered connectors, or troubleshoot a sync that's not picking up data. Trigger phrases include "add X to MFS", "ingest my [postgres/slack/github/etc]", "register this repo / database / workspace", "make X searchable", "re-sync Y", "update the slack token", "what connectors do I have". Do NOT use for: searching / finding / reading content (use `mfs-find`); raw mutation of the source itself (MFS only reads).
MiniMax spreadsheet production system. Engage for any task that involves tabular data, numeric analysis, or spreadsheet generation. Supports XLSX/XLSM/CSV through Python 3 (openpyxl + pandas) for workbook construction, formula recalculation via recalc.py (LibreOffice headless), and the MiniMaxXlsx CLI (C#/.NET) for structural validation, formula auditing, and pivot table synthesis.
Integrates Neon serverless Postgres and Vercel Postgres (built on Neon) into web and edge applications: the @neondatabase/serverless and @vercel/postgres HTTP and WebSocket drivers, pooled versus non-pooled connection strings, sslmode requirements, git-like database branching with neonctl, Drizzle ORM and Prisma integration, migrations, and point-in-time restore. Use when setting up serverless Postgres for Cloudflare Workers, Vercel Edge Functions, Next.js, or Node.js serverless functions, configuring connection pooling for edge runtimes, creating database branches, migrating from D1/SQLite or traditional Postgres to serverless Postgres, or debugging errors like connection pool exhausted, TCP connections not supported in serverless, transaction timeouts, and sslmode required.
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linking related objects, enforcing constraints, planning multi-step actions as graph transformations, or when skills need to share state. Trigger on "remember", "what do I know about", "link X to Y", "show dependencies", entity CRUD, or cross-skill data access.
Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.
Summarize the matter portfolio from _log.yaml — risk distribution, upcoming deadlines, stale matters, materiality roll-up, stage distribution and flagged anomalies. Use when the user asks "how's the portfolio looking", "how many open matters are there", or needs a portfolio summary.
Execute read-only SQL queries against multiple PostgreSQL databases. Use when: (1) querying PostgreSQL databases, (2) exploring database schemas/tables, (3) running SELECT queries for data analysis, (4) checking database contents. Supports multiple database connections with descriptions for intelligent auto-selection. Blocks all write operations (INSERT, UPDATE, DELETE, DROP, etc.) for safety.
Build and operate predictive models for logistics networks—demand forecasting at SKU/location/lane granularity; inventory positioning and safety stock optimization interfaces; ETA and lead-time prediction; capacity and congestion signals; route and network flow forecasting at model-integration level; cold chain and perishables; promotion and seasonality; model monitoring, drift, and backtesting against operational KPIs (fill rate, OTIF, WMAPE/MAPE). Use for predictive logistics, demand forecasting logistics, ETA prediction, inventory positioning, safety stock optimization, OTIF forecast, lane demand, WMAPE, logistics ML, capacity forecasting logistics, or cold chain forecast—not pure OR/MIP without logistics domain (operations-research-algorithm-developer), supply chain strategy only (supply-chain-manager), WMS feature dev (wms-developer), fleet telematics ingestion (geospatial-telematics-developer), generic ML without logistics (data-scientist), or EDI document mapping (edi-engineer).
Design and audit product analytics instrumentation for events, properties, identity, sessions, funnels, cohorts, experiments, attribution, billing/support joins, privacy, consent, data quality, mobile/web/server tracking, dashboards, QA, release gates, and metric ownership. Use when teams need trustworthy behavior data before product decisions or launches.
TOC router for PyGraphistry Python SDK tasks. Use when asked to "plot a graph", "visualize my edges", "load a dataframe into graphistry", "import graphistry", "run UMAP on my graph", "query my graph with GFQL or Cypher", "pick an execution engine", "make my GFQL query faster", "connect graphistry to Neo4j/Splunk/Kusto", or any Python SDK graph workflow. Also triggers on "graphistry.register", "g.plot()", ".gfql()", "chain-list", "hypergraph", "engine='polars'", "polars-gpu", "cudf", or "pygraphistry". Dispatches to pygraphistry-core (auth/ETL/plot), pygraphistry-gfql (queries, engines, indexes), pygraphistry-visualization (styling/sharing), pygraphistry-ai (UMAP/DBSCAN/embeddings), or pygraphistry-connectors (external DBs). Proactively suggest when the user shares an edges/nodes DataFrame and asks about graph analysis or visualization.
PyGraphistry graph ML/AI: UMAP, DBSCAN, embeddings, and anomaly detection workflows. Use when asked to "run UMAP on my graph", "cluster nodes", "find anomalies in my network data", "embed nodes", "fit-transform pipeline", "semantic search over graph nodes", or "graph AI". Also triggers on "graphistry umap", "dbscan clusters", "node embeddings", "featurize", or "anomaly triage". Proactively suggest when the user has node feature columns and asks about outliers, clusters, or similarity without yet using UMAP or DBSCAN.
Construct and run GFQL graph queries in PyGraphistry using chain-list syntax or Cypher strings. Use when asked to "query my graph with GFQL", "MATCH pattern in graphistry", "find paths between nodes", "hop constraints", "let bindings", "GRAPH constructor", or "run Cypher on my graph". Also triggers on "g.gfql()", "n() e_forward() n()", "chain-list query", "subgraph extraction", "remote graph query", or "pattern matching in graphistry". Proactively suggest when the user wants multi-hop traversal or pattern matching on a graph already loaded in PyGraphistry.
Comprehensive healthcare AI toolkit for developing, testing, and deploying machine learning models with clinical data. This skill should be used 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 applications (RETAIN, SafeDrug, Transformer, GNN).
Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets
Python data engineering implementation and review skill. Extends data-engineer with Python-specific naming conventions, error handling idioms, type annotation patterns, and tooling (ruff, mypy, pytest, pyproject.toml). Use when implementing or reviewing Python data pipelines or libraries.
Design probes and diagnostics to inspect what a model representation has learned. Use for embeddings, intermediate features, collapse checks, retrieval behavior, clustering structure, mechanistic interpretation-lite, or when aggregate accuracy does not explain internal behavior.
Clay platform help — Waterfall Enrichment (150+ data providers), Claygent (AI Research Agents), Sculptor (workflow builder), Signals (job changes, intent), Audiences, Ad Sync (LinkedIn/Meta/Google), Email Sequencer, CRM Sync (Salesforce/HubSpot/Dynamics), HTTP API actions, webhooks. Use when enrichment data has gaps or low match rates, Claygent not returning useful research, Sculptor workflows failing or hard to build, signals missing job changes you care about, audiences not syncing to ad platforms, CRM data out of sync with Clay, or webhooks not firing. Do NOT use for enrichment strategy across tools (use /sales-enrich), cadence/sequence strategy across tools (use /sales-cadence), intent signal strategy across tools (use /sales-intent), B2B advertising strategy across tools (use /sales-b2b-advertising), or CRM data hygiene strategy across tools (use /sales-data-hygiene).
Enrich.so platform help — real-time B2B data enrichment API with 50+ data points per lookup and success-based billing. Key capabilities: reverse email lookup, LinkedIn profile enrichment, email/phone finders, company data, similar company search, employee search, IP to company, and bulk enrichment. Use when you have an email but need the LinkedIn profile behind it, a LinkedIn URL but need contact details, you need to find someone's email from just name and company, company data is missing for your leads, or the Enrich.so API isn't returning expected results. Do NOT use for cross-platform enrichment strategy (use /sales-enrich), email deliverability strategy (use /sales-deliverability), or prospect list strategy (use /sales-prospect-list).
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.