Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Comprehensive Pandas toolkit for construction data analysis. Filter, group, aggregate BIM elements, calculate quantities, merge datasets, and generate reports from structured construction data.
Use when you need to understand anything in an Oracle schema - finding objects, reading PL/SQL source, signatures, table columns, DDL, searching code, or asking who uses what. ALSO use when a .sql file, dump, export, migration script or repo copy of an object appears to answer the question already, including one the developer points you at - because those files drift from the database and are wrong in ways that read as correct. The database is the only source of truth.
Use when uploading, annotating, building, converting, analyzing, or debugging datasets in Ultralytics Platform or local YOLO — Platform dataset management and Smart Annotation, data.yaml, YOLO label .txt formats, COCO/DOTA/mask conversion, auto-labeling, splits, validation, and errors like "no labels found" or mAP near 0. Covers detect, segment, semantic, depth, classify, pose, and OBB data.
Pull and stream US stock market data from Alpaca — REST snapshots/bars/trades/quotes, historical bars with timeframes and feeds (IEX vs SIP), the assets master list, market clock & calendar, news, and the real-time WebSocket stream. Use when building charts, quotes, price feeds, or asset metadata on Alpaca in any language.
Use when the user wants to connect to, query, or explore a data source through a CData driver via the command line. This includes: creating or testing a connection to an enterprise system (Salesforce, NetSuite, Snowflake, SQL Server, Jira, etc.); running SQL queries against connected data; exploring schema (listing tables, columns, or stored procedures); executing source-specific stored procedures; and downloading, installing, or activating CData drivers. Acts as the prerequisite foundation for all driver-level cdata- skills (cdata-salesforce, cdata-airtable, etc.) that add source-specific guidance.
Clean and preprocess datasets by handling missing values, removing duplicates, correcting types, resolving outliers, and enforcing validation schemas. Use when the user requests data cleaning or provides relevant inputs for this workflow.
Navigate Etherscan website features, API endpoints, CLI commands, and MCP tools. Use when selecting an Etherscan interface, locating an explorer page or developer utility, constructing or troubleshooting API requests, automating terminal workflows, connecting or calling Etherscan MCP tools, handling authentication, confirming chain support, interpreting errors, or comparing capabilities, limits, and behavior across Etherscan interfaces.
Shared foundations for building reusable data models in PostHog, on either of two stacks: PostHog-native data-warehouse views / materialized views (HogQL, via the view-* MCP tools), or an external dbt project (sources.yml + staging/marts + schema tests) run against your own or PostHog's managed warehouse. Read before authoring any specific business model — covers the PostHog-vs-dbt decision, the view-create → view-materialize → sync_frequency workflow and the HogQL column-aliasing rule, the dbt project skeleton and the honest "no native dbt integration" picture, warehouse joins and star-schema dimensions, currency conversion with convertCurrency(), and checking/registering models in the data catalog for reuse. Companion to the domain skills modeling-revenue-metrics, modeling-conversion-metrics, modeling-activation-metrics, modeling-product-usage-metrics, and modeling-dimension-tables. Use when the user asks how to build a view, materialized view, or dbt model in PostHog, or which of the two stacks to use.
Agent-callable Notion tools for searching pages and databases, reading and creating pages, querying data sources, appending content, and managing schemas. Use when the user mentions Notion or wants to find, read, create, or edit Notion content, even if they don't name Notion explicitly.
Every Instagram Business metric across all your brand accounts Trigger phrases: `compare my instagram brands`, `instagram follower growth`, `best time to post on instagram`, `top instagram posts this month`, `instagram competitor analytics`, `use instagram-pp-cli`, `run instagram analytics`.
Use when defining or debugging Spring Boot 3 database transaction boundaries, rollback, propagation, optimistic-lock retries, or commit-bound events. For duplicate HTTP commands use idempotency-patterns; for reactive transactions use webflux-reactive-patterns
Use when training, fine-tuning, or validating Ultralytics YOLO models in Platform, cloud GPUs, or local code — model.train(), yolo train/val, remote metric streaming, epochs, batch, imgsz, devices, augmentation, multi-GPU, resumes, results, and fixing OOM, NaN loss, low mAP, or overfitting. For hyperparameter search and systematic improvement loops, see yolo-tuning.
Extract structured data from any web page using Playwright — screenshots, design tokens, page text as Markdown, interactive element maps, and full page data packs. Use this skill whenever the user wants to capture a web page screenshot (full page or element), extract CSS design tokens or theme information, convert page content to Markdown, collect links and interactive elements from a page, or export a complete page analysis package. Triggers on any URL that isn't an Axure prototype — for Axure URLs use extract-axure-data instead.
Safe workflow for reading, mapping, and writing Google Sheets without shifting columns or overwriting the wrong ranges. Use when the user wants to read, update, or append spreadsheet data, fix sheet structure, or sync data into Google Sheets.
Workflow for optimizing ClickHouse and HogQL queries. Use when a HogQL query, query runner, insight, or report is too slow; when a hand-written ClickHouse query (via `sync_execute` or in a migration) is too slow; when ClickHouse times out or hits memory limits; when investigating a slow `system.query_log` row; or when reviewing a proposed HogQL printer change for performance. Covers extracting the ClickHouse SQL, common smells (`FROM ... FINAL`, `JSONExtract` over properties, missing skip indexes, self-joins, CTE blow-up), measuring against a real cluster, and applying the fix at the right layer (printer, query runner, or migration). Does NOT cover Postgres / Django ORM / app-database queries; for those use `profiling-slow-api-endpoints`.
Use when a PL/SQL change is ready to reach the database - applying a CREATE OR REPLACE, restoring from the journal, or any other write. Runs the six-step flow through pythia apply and enforces the gate - the developer sees the preview and approves in chat before anything is written, and a broken result is never reported as success.
Execute authoring T-SQL (DDL, DML, data ingestion, transactions, schema changes) against Microsoft Fabric Data Warehouse and SQL endpoints via the MCP `fabric-sqlendpoint-execute_query` tool. Use when the user wants to: (1) create/alter/drop tables, (2) insert/update/delete/merge data, (3) run COPY INTO or OPENROWSET ingestion, (4) manage transactions or stored procedures, (5) perform schema evolution, (6) use time travel or snapshots, (7) generate ETL/ELT workflows, (8) create views/functions/procedures on Lakehouse SQLEP. Triggers: "create table in warehouse", "insert data via T-SQL", "load from ADLS", "COPY INTO", "run ETL with T-SQL", "alter warehouse table", "upsert with T-SQL", "merge into warehouse", "create T-SQL procedure", "warehouse time travel", "recover deleted warehouse data", "create warehouse schema", "deploy warehouse", "transaction conflict", "snapshot isolation error".
Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use when you have results from MULTIPLE studies and need a single pooled estimate, or to synthesize evidence from a systematic review / multiple GWAS / replicated experiments. Handles the error-prone effect-size + standard-error preparation (converting OR/HR/CI, two-group means±SD, proportions, and correlations into the (effect, SE) the pooling step needs).
Given a PDB structure, produce a per-residue annotation table: which residues sit at a binding interface (vs a partner chain), which line a ligand pocket, which are buried (core) vs solvent-exposed (surface), and optionally secondary structure. This is the structural track drawn under a DMS heatmap and the structural prior SAE feature drops are read against. Use when you need to anchor a variant-interpretation or DMS analysis to the protein's actual physical context.
Quick BI-SmartQ skill with multiple data analysis capabilities: 1. **File Q&A**: Upload Excel/CSV files for intelligent analysis via Quick BI API 2. **Dataset Q&A**: Natural language queries on Quick BI platform datasets, with automatic intelligent table selection and matching 3. **Document Parsing**: Parse PDF/Word/Excel/CSV/images, extract text, and support extracting key fields to generate structured Excel 4. **Dashboard Skill Generation**: Auto-convert QuickBI dashboards into data query skills 5. **Data Insight**: Deep data insight analysis on Quick BI datasets 6. **Data Report**: Auto-generate professional data reports based on analysis results Use when users mention data analysis, smart Q&A, querying data, file analysis, document parsing, dashboard skills, data insight, or data reports.
This skill installs and configures the **Tablestore Mem0** plugin for OpenClaw. Tablestore Mem0 uses Alibaba Cloud Tablestore as the vector store backend for mem0, providing persistent long-term memory for AI agents. Use this skill when the user wants OpenClaw to persist or manage long-term memory using Alibaba Cloud Tablestore as the backend. Triggers: "set up tablestore memory", "install tablestore mem0 plugin", "configure long-term memory with tablestore", "remember this".
Browser automation with persistent named pages via the dev-browser CLI. Use when users ask to navigate websites, fill forms, take screenshots, extract web data, test web apps, log into sites, or automate browser workflows. Trigger phrases include "go to [url]", "click on", "fill out the form", "take a screenshot", "scrape", "automate", "test the website", "log into", "open the browser", or any browser interaction request.
Use the local Kaggle CLI skill for command guidance, workflows, and troubleshooting across competitions, datasets, kernels/notebooks, models, model variations and versions, inbox file uploads, forums/discussions, benchmarks, configuration, OAuth/API-token authentication, and accelerator quota. Activate this skill when the user asks about kaggle CLI commands, examples, flags, metadata files, download/upload flows, submissions, benchmark tasks, or Kaggle CLI behavior.
Mercado Libre product selection data lookup and analysis. Uses the LinkFox gateway to call Blue Whale’s 24 tools for products, official links, keywords, categories, trends, stores, reviews, exchange rates, and plan usage, covering the Mexico, Brazil, Argentina, Chile, and Colombia sites. Trigger this skill when the user mentions Mercado Libre, 美客多, 蓝鲸选品, Lanjing, 美客多选品, product search, category trends, trending keywords, traffic keyword reverse lookup, store lookup, review lookup, exchange rates, or plan usage.
Multi-step Excel data analysis orchestrator. Covers: (1) reading multi-sheet Excel files and counting rows, (2) large-file detection (automatic Parquet optimization at ≥10k rows), (3) data cleaning (missing values, text normalization, invalid characters), (4) conditional filtering and category extraction, (5) cross-sheet statistical aggregation, (6) exporting Excel/CSV with download links. Covers the full flow from data reading to report generation, orchestrating capability sub-skills step by step. **Proactively use this skill in any of the following cases instead of answering with a few lines of pandas**: ① the user uses trigger words: Excel 分析 / 表格分析 / 数据分析 / 数据清洗 / 数据统计 / 数据筛选 / 数据可视化 / 数据导出 / 汇总统计 / 透视表 / 分组统计 / 交叉分析 / 趋势分析 / 对比分析 / 异常值检测 / 去重 / 缺失值处理 / Excel 报告 / 生成报表 / analyze Excel / data analysis / data cleaning / pivot table; ② the user uploads or specifies a .xlsx / .xls / .csv file and asks for analysis, cleaning, statistics, or visualization; ③ the task involves any of multi-sheet reading, conditional filtering, category aggregation, or chart generation; ④ the user asks to export a formatted Excel report or a download link. Not for: pure text processing that does not involve tabular data, image analysis (use sn-da-image-caption), or simple Q&A with a single formula calculation.
An image understanding and data extraction skill. Use when image files (.png/.jpg/.jpeg/.gif/.webp/.bmp) are the primary input and the user needs to understand, extract data from, or analyze their contents. Provides a preconfigured caption script (scripts/caption.py) that converts images into text descriptions using a vision model, with no additional API Key configuration required. Covers: (1) captioning charts/tables/screenshots/flowcharts with scripts/caption.py; (2) parsing caption text into a structured DataFrame; (3) recreating visualizations based on extracted data; (4) exporting to Excel/CSV. Proactively use this skill whenever any of the following apply; do not guess image contents: ① The user uses trigger terms: image analysis / chart extraction / table recognition / OCR / image description / screenshot analysis / chart data / extract data from an image / image to table / image recognition / image caption / extract data from image / chart analysis / table OCR; ② The user uploads or specifies an image file (.png / .jpg / .jpeg / .gif / .webp / .bmp) and asks to understand it, extract data, or analyze its contents; ③ The task requires extracting structured information from chart screenshots, table screenshots, UI screenshots, or flowcharts; ④ The user asks to convert image data to Excel/CSV or recreate visualizations. Do not use for image editing (cropping, filters, resizing), image generation, or descriptions of scenery/people photos without data.
Audit your biggest closed-won deals to find your PROVEN ideal customer profile, then find more accounts like them. Use whenever someone wants to analyze won deals, audit their best customers, see which companies generated the most revenue, find their real ICP, build a look-alike target list, segment customers by what actually pays, or learn which acquisition channel produced their best revenue. Triggers on: 'audit my biggest deals', 'which customers made us the most money', 'analyze my closed-won', 'what's my proven ICP', 'find more customers like my best ones', 'look-alike accounts', 'HubSpot deal analysis', 'revenue by account', 'which channel generated my best deals', 'acquisition source analysis'. For RevOps, Heads of Sales/Marketing, founders and growth leads doing ICP refinement, account-based targeting or pipeline/QBR review. Reads HubSpot via its MCP or a CSV export, then hands the profile to sales-nav-search-builder to generate the prospecting search. Maintained by La Growth Machine.
Visualize live Wuji Hand 2 or Wuji Glove data with the official Rerun Web Viewer using `wuji viz`. Use when a user wants to inspect hand pose, per-taxel forces, per-finger resultant force, force history, live visualization faults, interactive device selection, or Hand 2 runtime tactile zeroing.
Industry data query and analysis tool. Connects to multiple compliant public data sources, supporting exact matching, fuzzy search, aggregate statistics, grouped sorting and other query methods. It currently covers most scenarios such as basic business registration information, regional indicators for supply chain nodes, supply chain enterprise information, A-share related information and more. The complete list of available data sources and their field structures must be obtained dynamically through the describe_datasource interface. Trigger this Skill whenever the user expresses any intent to query data about enterprises, companies, industries or supply chains. Typical scenarios include but are not limited to: - Querying a company's business registration information (name, legal representative, registered capital, date of establishment, business scope, shareholders, financing history, etc.) - Querying fundamentals, securities information, F10 data, IPO information, dividends, executive information of A-share listed companies - Querying the structure of a supply chain, including node enterprises, regional distribution, statistics of enterprises with various labels (leading, high-tech, specialized and innovative, unicorn, little giant, etc.) - Querying regional supply chain indicators (revenue, assets, profit margin, ROE, intellectual property, bidding data, etc.) - Aggregate statistics or group analysis by region, industry, enterprise label and other dimensions - Any public data query intent such as "look up such and such company", "help me look at this enterprise", "which leaders are in the xx industry" Even if it cannot be determined whether the required data is covered, the data source list interface should be called first to probe, without refusing or guessing directly. Data comes from compliant public channels and is for reference only, not a basis for decision-making.
Comprehensive SQL statement checking for Apache Doris (based on Doris 3.1.4 Nereids ANTLR4 grammar), supporting two check modes: 1. Syntax Check - Keyword validation, statement structure verification, clause completeness, Doris-specific syntax compatibility (DISTRIBUTED BY, PARTITION BY, ENGINE, DUPLICATE/AGGREGATE/UNIQUE KEY, INSERT OVERWRITE, LOAD, EXPORT, MTMV, BACKUP/RESTORE etc.) 2. Specification Check - Object design standards, data operation standards, naming conventions based on Apache Doris development best practices. Built-in custom Doris SQL tokenizer (504 keywords from DorisLexer.g4) and recursive descent parser supporting 100+ Doris statement types. Applicable when users need SQL quality review, syntax validation, or specification compliance checking for Apache Doris SQL (versions 2.1.x / 3.0.x / 3.1.x / 4.x). 触发词："Doris SQL检查"、"Doris SQL规范"、"Doris SQL审计"、"Doris SQL语法"、"检查Doris SQL"、"Doris SQL review"
Pragmatic qualitative analysis for interview data in sociology research. Guides you through systematic coding, interpretation, and synthesis with quality checkpoints. Supports theory-informed (Track A) or data-first (Track B) approaches.
Meilisearch — fast, open-source search engine with typo tolerance, faceted search, and AI-powered hybrid search. Use when building with Meilisearch or asking about its index configuration, search parameters, filters, facets, API keys, geosearch, ranking rules, or integration with JavaScript/TypeScript clients. Fetch live documentation for up-to-date details.
Use MistTrack OpenAPI for cryptocurrency address risk analysis, AML compliance checks, and transaction tracking. MistTrack is an anti-money laundering tracking tool developed by SlowMist. It supports risk scores, label lookups, and transaction investigations for addresses and transactions on major chains such as BTC, ETH, TRX, and BNB.
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.