Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Operate OmniGraph graphs and deployments. Use for `.pg` schemas, `.gq` queries, OmniGraph CLI commands, `file://`/`s3://`/`az://` graph URIs, `cluster.yaml`, operator config, bearer-authenticated servers, graph-backed knowledge or memory, Blob values, embeddings, branches, commits, and change feeds. Apply especially before schema changes, bulk loads, and retries after uncertain remote outcomes.
Provides comprehensive guidance for Oracle database including SQL, PL/SQL, functions, performance tuning (AWR/ASH), backup (RMAN), Data Guard, and RAC. Use when the user asks about Oracle, needs to write Oracle SQL, work with PL/SQL, or manage Oracle databases.
Screen post-earnings gap-up stocks for PEAD (Post-Earnings Announcement Drift) patterns. Analyzes weekly candle formation to detect red candle pullbacks and breakout signals. Supports two input modes - FMP earnings calendar (Mode A) or earnings-trade-analyzer JSON output (Mode B). Use when user asks about PEAD screening, post-earnings drift, earnings gap follow-through, red candle breakout patterns, or weekly earnings momentum setups.
Analyze product or business data to support a decision or recommendation. Use when a decision depends on metric-backed evidence, such as choosing a direction, prioritizing an opportunity, evaluating a change, segmenting users, sizing tradeoffs, or deciding what to do next.
3D visualization and mesh analysis for geoscience data using PyVista/VTK. Use when the agent needs to: (1) Create 3D visualizations of geological models, (2) Render seismic volumes or voxel data, (3) Visualize point clouds or well paths, (4) Plot surfaces and meshes in 3D, (5) Read/write VTK, STL, OBJ files, (6) Create cross-sections through 3D models, (7) Export publication-quality figures or interactive HTML.
Ahrefs API analyst (extension). Reads referring domains, backlinks, organic keywords, and content explorer data via the tested @ahrefs/[email protected] server. Pairs with seo-backlinks for multi-source confidence weighting.
Query and summarize site activity logs for a Webflow enterprise site. Surfaces recent changes, identifies who made them, and generates human-readable activity reports. Use for site monitoring, change tracking, publish preparation, or weekly activity summaries. Enterprise plans only.
Advanced statistical modeling and hypothesis testing. Complementary to SciPy's stats module, it provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and statistical data exploration. Use for linear regression, GLM, time series analysis, ANOVA, survival analysis, causal inference, and statistical hypothesis testing. Load when working with OLS, WLS, logistic regression, Poisson regression, ARIMA, SARIMAX, statistical diagnostics, p-values, confidence intervals, or R-style statistical analysis.
Screen US stocks for high-quality dividend opportunities combining value characteristics (P/E ratio under 20, P/B ratio under 2), attractive yields (3% or higher), and consistent growth (dividend/revenue/EPS trending up over 3 years). Supports two-stage screening using FINVIZ Elite API for efficient pre-filtering followed by FMP API for detailed analysis. Use when user requests dividend stock screening, income portfolio ideas, or quality value stocks with strong fundamentals.
Manage Volcengine's AI-native BaaS platform (Supabase edition / AIDAP / 火山引擎 AI 原生 BaaS 平台 Supabase 版) — a Volcengine-operated distribution that differs from official Supabase. Use when the user asks to create, inspect, or manage Volcengine Supabase or AIDAP resources — workspaces, branches, computes, SQL queries, schema changes, Auth, Realtime, Edge Functions, Storage buckets, frontend static-site hosting (Pages), API keys, connection info, or TypeScript type generation — or when a Volcengine deployment selects AIDAP as its database provider. Also trigger on Chinese prompts mentioning "火山引擎 Supabase" or "火山 Supabase". Operations run through the byted-supabase-cli command-line tool (installed via `npm i -g @byted-supabase/cli`; this is NOT the official `supabase` CLI). Do NOT use it for general database discussions, non-Supabase services (RDS MySQL, Redis), or pure client-side coding unrelated to Supabase backend management.
N-dimensional labeled arrays for geoscience data. Read/write NetCDF, work with climate and oceanographic datasets, perform multi-dimensional analysis with labeled coordinates. Use when the agent needs to: (1) Read/write NetCDF or Zarr files, (2) Work with multidimensional arrays with labeled dimensions, (3) Analyze climate, ocean, or atmosphere data, (4) Compute temporal aggregations (daily/monthly/annual means), (5) Perform area-weighted statistics, (6) Process large datasets with Dask, (7) Apply CF conventions to scientific data.
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Comprehensive qualitative research methods assistant supporting thematic analysis (Braun & Clarke), grounded theory (Strauss & Corbin; Charmaz), interpretative phenomenological analysis (IPA), content analysis, narrative inquiry, ethnography, case study methodology (Yin), coding techniques (open/axial/selective), NVivo-style workflows with Python alternatives, trustworthiness criteria (Lincoln & Guba), reflexivity, and member checking. Use when designing or analyzing qualitative research — thematic analysis, grounded theory, coding data, phenomenology, IPA, ethnography, case study, narrative inquiry, content analysis, qualitative coding, NVivo, trustworthiness, member checking, reflexivity, interview analysis, or focus group analysis. Part of the AlterLab Academic Skills suite.
Molecular biology toolkit: sequence manipulation, FASTA/GenBank/PDB I/O, NCBI Entrez, BLAST automation, pairwise/MSA alignment, Bio.PDB, phylogenetic trees. Use for batch processing, custom pipelines, format conversion, PubMed/GenBank queries. For quick gene lookups use gget; for multi-service REST APIs use bioservices.
Always invoke before answering any request to create, compare, design, or review a user-facing map, even if the request is terse or underspecified. Covers publication maps, choropleths, map series and small multiples, comparable multi-date panels, proportional/bivariate/flow maps, raster rendering, and interactive web maps. Includes classification, color, legends, projections, accessibility, and large-data aggregation. Do not trigger for a temporary diagnostic plot inside another analysis.
Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse/client). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically — including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy/TLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like "my inserts keep failing" or "connection drops randomly" in a Node.js context are strong signals to use this skill. Do NOT use for browser/Web client issues.
Parse and extract clinical data from HL7v2 messages and FHIR R4 resources using Python. Use when the user mentions HL7v2, HL7, FHIR, PID segment, OBX segment, MSH segment, Patient resource, Observation resource, Condition resource, MedicationRequest, EHR data extraction, clinical message parsing, FHIR bundle, HL7 to FHIR conversion, ADT message, ORU message, lab result extraction, or clinical data quality checks. Triggers include "parse HL7", "extract FHIR", "HL7v2 message", "FHIR resource", "PID segment", "OBX segment", "Patient resource", "Observation resource", "EHR parsing", "clinical data extraction", "HL7 to CSV", "FHIR to DataFrame".
Designs neural network architectures that respect validated symmetry groups, recommending architecture families (G-CNN, steerable CNN, e3nn), layer patterns, and implementation libraries. Use when you have validated symmetry groups and need equivariant architecture design, or when user mentions equivariant layers, G-CNN, e3nn, steerable networks, or building symmetry into a model.
Decomposes complex unknowns into estimable components to produce rapid order-of-magnitude answers with bounded uncertainty. Use when making quick estimates (market sizing, resource planning, feasibility checks), bounding unknowns with upper/lower limits, sanity-checking strategic assumptions, or when user mentions Fermi estimation, back-of-envelope calculation, order of magnitude, ballpark estimate, or triangulation.
Batch content grabber — bulk fetch bookmarks, user tweets, search results, author notes, wiki pages, subreddit posts, and more from X/Twitter, Xiaohongshu, Reddit, WeChat, YouTube, Feishu, Zhihu. Use when user wants to batch/bulk fetch, search keywords, or grab all posts from an account.
Headless 360 REST API deployment step for creating sObject records. Handles describe-based field discovery, required-field derivation, entity-relationship ordering, and composite graph transactions. Use this skill when a designer skill (or a user directly) needs to create sObject records after design confirmation, including non-setup sObject creation.
Reasoning skill for medical imaging study design and biomarker selection. Use when the user asks to plan an imaging study, choose a preprocessing strategy, select an imaging biomarker, design a radiomics pipeline, handle DICOM de-identification, plan longitudinal imaging analysis, or pick a registration target. Triggers include "imaging study", "preprocessing strategy", "DICOM de-identification", "imaging biomarker", "radiomics", "longitudinal imaging", "registration target", "MNI vs native space", "scanner harmonization", "ComBat", "IBSI", "multi-site imaging", "burned-in PHI", "test-retest reliability", "volumetric biomarker", "diffusion MRI", "perfusion imaging", "fMRI study design", "spectroscopy biomarker".
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials; prefer the bundled templates and run the helper script `new_notebook.py` to generate a clean starting notebook.
Read-only retrieval of entity information from the FIWARE Orion Context Broker (NGSIv2). Proactively use when you need GET queries to data-integration platforms, city OS, smart city platforms, sensor data or IoT platforms, to check entity lists and counts, or to investigate the NGSIv2 API. Triggers on requests involving Orion / NGSIv2 / Context Broker / entity retrieval / q queries / geospatial queries / platform access with the Fiware-Service header.
Use when you need a runnable PyTorch training repo for a medical-imaging task (segmentation, classification, detection, synthesis, self-supervised, or fine-tuning a pretrained backbone). Emits a patient-level seed-locked split, train/evaluate scripts, config and a Methods stub.
Use this skill to assess and plan a migration from AWS MSK (Managed Streaming for Apache Kafka) to Confluent Cloud. Triggers on user intent like "migrate MSK to Confluent Cloud", "move off MSK", "MSK to CC cutover", "Zero-Cut migration from MSK", "kcp scan my MSK", or any discussion of MSK-to-CC assessment, planning, cluster sizing, Cluster Linking setup, Gateway-based switchover, or post-cutover validation. Do NOT trigger for non-MSK Kafka sources (open-source Kafka, Aiven, Confluent Platform, Redpanda) — this skill is MSK-only. Do NOT trigger for greenfield Confluent Cloud projects with no existing Kafka source. Do NOT trigger for general Kafka programming questions (producer/consumer code, Kafka Streams) unrelated to migration.
Analyze Android, Linux, and Chromium Perfetto traces with local trace_processor_shell evidence. Use for startup, scrolling or jank, input latency, ANR, CPU scheduling, memory or GC, Binder or IO, GPU or SurfaceFlinger, power or thermal, rendering-pipeline identification, trace capture guidance, scene reconstruction, and single- or multi-trace performance comparison.
Reason about pharmacoepidemiologic study design for causal inference from real-world data — choosing active comparator new-user designs, emulating target trials, avoiding immortal time bias, handling time-varying confounding with marginal structural models, and selecting propensity score methods. Use when the user asks to design a drug safety or effectiveness study, choose between propensity score matching vs weighting vs stratification, emulate a target trial, handle immortal time bias, apply marginal structural models, assess unmeasured confounding with E-values, select an active comparator, define a new-user cohort, or evaluate a pharmacoepidemiology study design. Triggers include "active comparator", "new-user design", "target trial emulation", "immortal time bias", "propensity score matching", "IPTW", "marginal structural model", "confounding by indication", "E-value", "negative control outcomes", "quantitative bias analysis", "time-varying confounding", "prevalent user bias", "washout period", "landmark analysis", "stabilized weights".
Structural MRI/CT preprocessing pipeline for radiology workflows covering skull stripping, bias field correction, registration, and intensity normalization. Triggers on skull stripping, bias correction, registration, ANTs, FSL, HD-BET, N4, brain extraction, normalization, FLIRT, FNIRT, SyN, fslreorient2std, MNI registration, T1 preprocessing.
Use when building or auditing a radiomics or tabular clinical-ML prediction model with a classical learner (LASSO, SVM, random forest, XGBoost and similar). Enforces nested CV, dimensionality control, in-fold feature selection, feature stability, calibration and external validation.
Guide for choosing and creating scientific visualizations for publications and talks. Covers chart-type selection by data structure, color theory for accessibility/print, figure composition, journal formatting (Nature, Cell, ACS), and common pitfalls. Consult when visualizing data or preparing submission figures.
Provides comprehensive guidance for Spring Data JPA including repositories, entity management, query methods, and database operations. Use when the user asks about Spring Data JPA, needs to work with JPA repositories, implement data access layers, or configure JPA in Spring
Implements Syncfusion Windows Forms GridGroupingControl for advanced data management with grouping, sorting, filtering, and hierarchical display. Use this when working with multi-level grouping, master-detail grids, nested table relationships, or data summaries with aggregates. The skill covers group-by operations, Excel-like filtering, dynamic record filters, hierarchical data structures, and enterprise-level grid capabilities.
This skill should be used to generate realistic, persona-consistent synthetic coaching and therapy session transcripts for evals, demos, and training data. It produces fictional but believable coach/client (or therapist/client) dialogue grounded in a chosen modality (ICF/GROW coaching, CBT, IFS parts work, ACT/motivational interviewing) and exports to Fathom/Granola transcript style, plain dialogue, structured JSON, or Obsidian markdown. Triggers on requests like "generate a synthetic coaching session", "make fake therapy transcripts for evals", "create demo session transcripts", "synthetic CBT dialogue", "persona-consistent coaching transcript", "test data for my session summarizer", or "mock coaching call".
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.