Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Design and audit customer feedback intake across support tickets, sales calls, app reviews, surveys, NPS, communities, cancellation reasons, feature requests, bug reports, and product analytics. Use when teams need to turn noisy feedback into evidence, prioritization input, roadmap decisions, and customer follow-up without overreacting to anecdotes.
Read, build, and fix RevOS dashboards of metric cards through the RevOS MCP server. A card is one metric (a numeric measure), counted over a segment's rows or the card's own conditions, optionally broken down by a dimension. Use this whenever the user wants to see what's on a dashboard ("what's on our sales dashboard", "what does the pipeline card show"), put a metric or KPI on one ("add open pipeline to the board", "add revenue by stage as a donut"), change, reorder, move, or remove a card, create, rename, or delete a dashboard, or asks why a card shows "unavailable" or a number that looks wrong — even if they never say "dashboard" or "card". Read it before the first api_write or api_delete on `dashboards` or `dashboard-cards`.
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.
Design and audit data imports, customer migrations, bulk uploads, CSV/schema mapping, backfills, migration wizards, dry runs, validation, idempotency, reconciliation, rollback, progress visibility, and support handoffs. Use when moving product data between systems, onboarding customers from competitors, importing files, or migrating accounts without data loss.
Data visualization with chart selection, color theory, and annotation best practices. Covers chart types (bar, line, scatter, heatmap), axes rules, and storytelling with data. Use for: charts, graphs, dashboards, reports, presentations, infographics, data stories. Triggers: data visualization, chart, graph, data chart, bar chart, line chart, scatter plot, data viz, visualization, dashboard chart, infographic data, data presentation, chart design, plot, heatmap, pie chart alternative
Retrieve, version, and publish scientific datasets with DataLad and git-annex, and capture computational provenance with datalad run, rerun, and containers-run. Use when cloning or fetching data from OpenNeuro, DANDI, datasets.datalad.org, or any DataLad dataset; when a file in a dataset reads as a broken symlink or a small pointer instead of real data; when an analysis needs a machine-readable record of how each output was produced so it can be re-executed; or when publishing a dataset to siblings such as a GitHub repository plus a storage remote. Also use to decide between DataLad and plain Git for a data-carrying repository.
Query the Cancer Dependency Map (DepMap) for cancer cell line gene dependency scores (CRISPR Chronos), drug sensitivity data, and gene effect profiles. Use for identifying cancer-specific vulnerabilities, synthetic lethal interactions, and validating oncology drug targets.
Read market data for a known Kalshi prediction market on DFlow — orderbook, trades, top-of-book prices, candlesticks, forecast-percentile history, and Kalshi in-game live data — via one-shot REST snapshots, historical ranges, or live WebSocket streams. Use when the user asks "show me the orderbook for X", "get last hour of trades", "build a live price ticker", "stream orderbook depth", "pull 1-minute candles for the last day", "watch in-game scores for this sports market", or "alert me when the orderbook moves". Do NOT use to discover markets matching a criterion (use `dflow-kalshi-market-scanner`), to place orders (use `dflow-kalshi-trading`), or to read a user's own positions/P&L (use `dflow-kalshi-portfolio`).
Build, review, debug, configure, migrate, teach, or plan Drizzle ORM 1.0 RC (not 0.x) with kit, seed, validators, and Effect drivers. Use for drizzle-orm@rc, drizzle-kit@rc, drizzle-seed@rc, defineRelations RQBv2, pgTable/mysqlTable/sqliteTable, generate migrate push pull studio, drizzle-orm/zod|valibot|typebox|arktype|effect-schema, drizzle-orm/effect-postgres and other effect-* drivers on Effect 4, kit --output json/--hints/drizzle-kit/cli SDK, codecs, snakeCase, InferSelectModel, and 0.x-to-1.0 upgrades.
Search, retrieve and analyse data from the DUV Ultramarathon Statistics database (statistik.d-u-v.org, 10M+ results, 2.4M+ runners, 115k+ events). Use whenever the user asks about ultramarathon results, runners, races or rankings: national, continental and age-group records and their progression, personal bests, race results and course records, race history and winners, race calendars, IAU championship medallists, head-to-head comparisons, lifetime or yearly ultra mileage, oldest finishers, women who won outright, doping cases. Examples: 'Hungarian 100 km record', 'Spartathlon 2024 results', 'top 24 h women in 2024', 'course record at Ultrabalaton', 'who ran the most km in 2025', 'compare runner A and B'. Also Hungarian: 'országos csúcs', 'magyar rekord', 'pályacsúcs', 'ultrafutó eredmények', 'ki futott a legtöbbet', 'VB-érmesek'. Not for training data from Garmin/Strava or ITRA trail scores
Extract construction intelligence from AutoCAD DWG files by compiling libredwg from source, converting DWG to DXF, and parsing all entity types (survey points, utility structures, contours, property boundaries, construction keynotes, grading data). Use this skill whenever the user uploads or references a .dwg file, mentions AutoCAD drawings, Civil 3D files, grading plans, site plans in DWG format, or wants to extract data from CAD files. Also triggers when processing project documents that include .dwg files, or when the user says "read the DWG", "extract from CAD", "parse the drawing file", "process the civil plans", "what's in the DWG", "survey points from the drawing", "utility data from CAD". This skill handles the entire pipeline from binary DWG to structured JSON data.
Analyze local scientific CSV, TXT, XLS, or XLSX data; recommend publication-informed charts and Chinese scientific palettes; freeze a reproducible plan; and automate editable figures through a callable local Origin/OriginPro installation on physical Windows 10/11 x64. Use for beginner “drop in a file and draw it” requests; XPS, XRD, XAS, PL/TRPL, DSC, NMR, FTIR/IR, UV-Vis, electrochemistry, medical/AI evidence, distribution, relationship, error-bar, bar, stacked, pie, Sankey, radar, heatmap, or verified 3D workflows; project-local Python setup; palette selection; and OPJU/PNG/PDF/TIF verification. Do not use on macOS, Linux, WSL, Wine/CrossOver, Parallels, or other VMs; to install or modify Origin; to redistribute reference images; or to claim an unverified Origin route.
Experiment executor and monitor for academic research. 2-agent system covering code experiments (ML training, statistical analysis, ETL, simulation) and human studies (surveys, field studies, interviews). 4 modes: run (execute + monitor code), manage (track human studies), validate (statistical interpretation + reproducibility verification), plan (Socratic experiment design). Use when a researcher wants to run or monitor a research experiment, manage or resume a human-subject study, check the statistics or reproducibility of research results, or design an experiment or study, in English or Chinese (e.g., 跑實驗、管理研究、驗證結果、規劃實驗).
Query Feishu Bitable (多维表格) with server-side filter, sort, field selection, and multiple output formats. Activate when querying bitable records with conditions, filtering multidimensional table data, or when data volume is large and client-side filtering is inefficient. Preferred over feishu_bitable_list_records tool when filter expressions or full pagination is needed.
Evaluate model predictions against ground truth using COCO, Open Images, or custom protocols. Use when computing mAP, precision, recall, confusion matrices, or analyzing TP/FP/FN examples for detection, classification, segmentation, or regression tasks.
Fine-tune and serve Physical Intelligence OpenPI models (pi0, pi0-fast, pi0.5) using JAX or PyTorch backends for robot policy inference across ALOHA, DROID, and LIBERO environments. Use when adapting pi0 models to custom datasets, converting JAX checkpoints to PyTorch, running policy inference servers, or debugging norm stats and GPU memory issues
Flyway database migrations for Spring Boot applications. Covers migration scripts, versioning, callbacks, and rollback strategies. Based on production patterns from castellino and gestionale-presenze projects. USE WHEN: user mentions "flyway", "database versioning", "Java migrations", "Spring Boot migrations", "V1__", "migration checksum" DO NOT USE FOR: general migration strategies - use `migrations` instead, Liquibase - use specific tool skill, Prisma/TypeORM - use respective skills
Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. **Perfect for:** - Public company valuation (M&A, investment analysis) - Benchmarking performance vs. industry peers - Pricing IPOs or funding rounds - Identifying valuation outliers (over/under-valued) - Supporting investment committee presentations - Creating sector overview reports **Not ideal for:** - Private companies without comparable public peers - Highly diversified conglomerates - Distressed/bankrupt companies - Pre-revenue startups - Companies with unique business models
Convert genomic intervals between coordinate conventions, normalise and compare variant representations, and detect assembly or contig-naming mismatches before they corrupt an analysis. Use whenever coordinates cross a format, tool, or assembly boundary - converting between BED, GFF/GTF, VCF, SAM/BAM, WIG, PSL, genePred, Picard interval_list, or region strings; reconciling 0-based half-open with 1-based inclusive; left-aligning or trimming indels; checking whether two variant records describe the same change; mapping genomic to transcript, CDS, or protein positions; auditing a BED/GTF/VCF for convention violations; or diagnosing GRCh37 vs hg19 vs GRCh38 vs T2T, chr-prefix, and liftover problems. Triggers include "off by one", "0-based", "1-based", "half-open", "coordinate system", "left-align", "normalize variant", "bcftools norm", "chr prefix", "wrong genome build", "liftover", "REF mismatch", and "HGVS".
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
This skill is designed to help users automatically extract business data from Google Maps search results. The Agent should proactively apply this skill when the user makes the following requests: searching for coffee shops in a specific city, finding dentists or medical clinics nearby, tracking competitors' locations in a certain area, extracting business leads from Google Maps lists, gathering restaurant data for market research, finding hotels or accommodation options in a region, locating specific services like coworking spaces or gyms, monitoring new business openings in a neighborhood, collecting contact information and addresses for sales prospecting, analyzing price ranges and cuisines of local eateries, getting ratings and review counts for a list of businesses, exporting local business data into a CRM or database.
Apply Structural Equation Modeling (SEM) to test hypothesized causal structures by combining measurement models (CFA) and structural models (path analysis). Use this skill when the user needs to validate latent constructs, test mediation or moderation paths, assess model fit with CFI/TLI/RMSEA/SRMR, or when they ask 'do these variables form a causal chain', 'how do I test my theoretical model', or 'is my measurement model valid'.
Generates data visualization charts (bar, line, area, pie, doughnut, scatter, radar, treemap) as PNG using Apache ECharts v6. 1080×1080px default, 5 style presets, highlight annotations. Trigger when user says "create a chart", "visualize data", "make a bar chart", "line graph", "pie chart", "data visualization", "chart this data", "plot", "graph", or "visualize these numbers".
Plans and audits use of ChicagoHAI HypoGeniC/HypoRefine for LLM-assisted hypothesis generation from labeled text datasets. Use for the `hypogenic` package, its task configs, hypothesis banks, or HypoBench datasets—not for manual hypothesis formulation or scientific validation.
Query and download public cancer imaging data from NCI Imaging Data Commons. Invoke for any question about IDC collections, cancer imaging datasets, DICOM data access, radiology (CT, MR, PET) or pathology AI training sets, metadata queries, visualization, or license checks — even when the user doesn't explicitly mention "IDC". No authentication required.
Use when user asks to create/edit/query Online reports, SQL reports, data reports, or says "创建报表", "生成报表", "新建报表", "查询报表", "online报表", "SQL报表", "数据报表", "统计报表", "create report", "generate report", "data report". Also triggers when user describes report requirements like "做一个销售统计报表", mentions JeecgBoot cgreport/online report, or says "查看现有报表" / "列出所有报表". This skill handles Online 报表 (SQL-driven data display/reports), not Online forms (cgform) or designer forms (desform).
Scaffold a production-ready Python Kafka producer and consumer using `confluent-kafka-python`, with Schema Registry, graceful shutdown, idempotent producer, tests and a complete project layout. Discovers the target topic, partition count and registered JSON Schema directly from the live cluster via any attached Kafka MCP server (Lenses MCP, Confluent's MCP, Aiven's, custom) before asking the user. Use when user says "build a Python Kafka consumer", "scaffold a Kafka client in Python", "add Kafka to my Python service", "consume from `<topic>` in Python" or "write a Kafka producer". Do NOT use for Kafka Connect connectors, schema-evolution reviews or non-Python clients.
Use compatible KOLSprite MCP tools for a straightforward current TikTok creator, product, video, or shop lookup. Use for searches, filters, profiles, comparisons, and returned statistics. Do not use for multi-step opportunity evaluation, cold-start planning, content strategy, creator-program design, competitor diagnosis, or growth strategy.
Use this skill when the user provides MySQL DDL, CREATE TABLE statements, table structure screenshots, or asks you to fill in Kysely database types, DB interfaces, Insertable / Updateable types, bulk inserts, bulk updates, upsert, or query/modify methods. The focus is accurately converting DDL into this repository's Kysely types and corresponding service methods, avoiding mistakes in ToGenerated, null/default checks, and camelCase/snake_case mapping.
Use when working with LaminDB, the open-source lineage-native lakehouse for biological datasets and models. Covers setup, artifact registration, query/search, lineage tracking, validation, ontology-backed annotation with Bionty, collections, branches, storage, and workflow integrations.
Parse, search, analyze, and ingest LinkedIn GDPR data exports into structured JSON or RLAMA for semantic search. Covers messages, connections, profile data, and Markdown export. Requires a LinkedIn GDPR ZIP file. Triggers on 'LinkedIn data', 'search messages', 'analyze connections', 'LinkedIn export', 'GDPR download'.
Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Q&A, chatbots, knowledge retrieval, or building RAG pipelines. Best for data-centric LLM applications.
Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language chatbots or image understanding tasks. Best for conversational image analysis.
Import PyTorch, ONNX, or Keras 3 / TensorFlow 2.16+ deep learning models into MATLAB as dlnetwork objects. Use when importing .pt2 exported programs, traced .pt files, .onnx models, or Keras 3 models via matlabsaver. Covers importNetworkFromPyTorch, importNetworkFromONNX, importNetworkFromKeras, importNetworkFromTensorFlow, torch.export.export, PyTorchInputSizes, InputDataFormats, matlabsaver, tf_keras downgrade, numeric validation against PyTorch or ONNX Runtime, and placeholder/custom layer implementation. Applies when user mentions any of these functions, file formats, or encounters import errors, unsupported operator warnings, 0 learnables, or uninitialized networks.
Creates MATLAB interfaces to Python image processing and computer vision models from GitHub repositories or pip-installable packages using MPyReq. Use when asked to interface MATLAB with a Python CV/image model (segmentation, depth estimation, object detection, image generation, super-resolution, etc.), given a GitHub repo URL for an image/vision model, or asked to create an MPyReq demo for a deep-learning vision pipeline. Do NOT use for general-purpose Python-MATLAB interfacing, non-vision models (NLP, tabular, audio), model deployment/serving, or MATLAB-only image processing workflows.
Build OCR pipelines in MATLAB using the ocr() function. Use this skill when the user wants to read text from images, documents, signs, meters, displays, license plates, gauges, receipts, or seven-segment displays. Covers image preprocessing, text detection (CRAFT, MSER), ROI-based recognition, multi-language OCR, and custom model training. Use when: OCR, text recognition, extract text from image, character recognition, document scanning, meter reading, gauge reading, receipt scanning, digitize text from photo.
Find and explore RevOS metrics — the measures of the org's semantic model, as the app's Metrics page lists them — through the RevOS MCP server: which metrics exist, what each one aggregates and how it's formatted, which tables, segments, and dashboard cards use it, and what its value is right now, counted over a segment or a few conditions and optionally broken down by a dimension. Use this whenever the user asks "what metrics do we have", "is there a metric for win rate / ARR / open pipeline", "where is this metric used", "what's open pipeline for the EMEA segment", "deal amount by stage", or wants a metric's value the same way a dashboard card would show it — even if they never say "metric". For putting a metric on a dashboard, use the dashboards skill; it builds on this one.
Deep-dive analysis of a single MLB player (hitter or pitcher) for the Yahoo Fantasy Baseball 2K25 league. Web-searches FanGraphs (ATC projections), Baseball Savant (xwOBA/xBA/xERA), MLB.com (lineups, probables), RotoWire (weather, injuries), and RotoBaller (closer depth) to produce the full set of structured player signals defined in the signal framework. Emits form_score, matchup_score, opportunity_score, daily_quality, regression_index, obp_contribution, sb_opportunity, role_certainty for hitters and qs_probability, k_ceiling, era_whip_risk, streamability_score, two_start_bonus, save_role_certainty for pitchers. Use when you need to analyze player, compute daily_quality, compute regression index, produce player signals, run a hitter analysis, run a pitcher analysis, or prep start/sit inputs for the lineup optimizer.
Tracks machine learning experiments and manages model lifecycles with MLflow, covering mlflow.log_param, log_metric and log_artifact, autologging for scikit-learn, PyTorch Lightning, XGBoost and HuggingFace Transformers, the Model Registry with versions and stage transitions, run searching, and local or cloud model serving. Use when logging parameters, metrics and artifacts for training runs, comparing runs across experiments, registering and promoting model versions from Staging to Production, serving a logged model for inference, or reproducing an experiment from an MLflow project. Not for hyperparameter-sweep dashboards or general data versioning; use a dedicated tool for those.
Optimize MongoDB client connection configuration (pools, timeouts, patterns) for any supported driver language. Use this skill when working/updating/reviewing on functions that instantiate or configure a MongoDB client (eg, when calling `connect()`), configuring connection pools, troubleshooting connection errors (ECONNREFUSED, timeouts, pool exhaustion), optimizing performance issues related to connections. This includes scenarios like building serverless functions with MongoDB, creating API endpoints that use MongoDB, optimizing high-traffic MongoDB applications, creating long-running tasks and concurrency, or debugging connection-related failures.
Network flow extractor that analyzes pcap/pcapng files to identify outbound connections with automatic DNS hostname resolution. Use when you need to enumerate network destinations, identify what hosts a device communicates with, or map IP addresses to hostnames from packet captures.
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.