Skills
96,770 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Design and audit revenue forecasts, capacity planning, funnel assumptions, pipeline, conversion, expansion, churn, retention cohorts, gross margin, infrastructure capacity, support capacity, sales capacity, inventory/supply, scenario planning, leading indicators, and forecast governance. Use when growth plans, pricing, launches, or operations need financially credible capacity-aware planning.
Implements the GDPR Article 17 right to erasure (right to be forgotten) workflow, covering all six grounds for erasure, five exceptions, technical deletion versus anonymization decisions, and third-party notification under Article 19. Activate for erasure request, deletion request, right to be forgotten, Art. 17 queries.
Extract full text from WeChat public account articles (mp.weixin.qq.com). Use when the user provides a WeChat article URL and asks for 提取文章, 抓取公众号, 读一下这篇, or when another skill needs to read a WeChat article before processing. Bypasses WeChat's access control via MicroMessenger UA spoofing. Stdlib only, no API key required.
This skill should be used when the user asks to "create a figure", "make a scientific figure", "create a paper figure", "make a figure for my paper", "make a figure for my manuscript", "compose figure panels", "assemble figure panels", "combine figure panels", "make a multi-panel figure", "figure composition", "export a figure", "generate a PDF figure", "create a Nature-style figure", "make a journal figure", "make a publication figure", or "create a figure for my grant". The skill composes multi-panel figures at exact journal dimensions (Nature 89/183 mm, Science 55/120 mm, Cell 85/174 mm, PNAS 87/180 mm) using svgutils, validates font sizes against journal minima before export, and writes PDF/PNG via Inkscape when available or cairosvg otherwise.
Comprehensive backend development skill for building scalable backend systems using NodeJS, Express, Go, Python, Postgres, GraphQL, REST APIs. Includes API scaffolding, database optimization, security implementation, and performance tuning. Use when designing APIs, optimizing database queries, implementing business logic, handling authentication/authorization, or reviewing backend code.
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.
Use when fixing the debris an audit found in a Shopify catalog: bulk-archive dead/discontinued/zero-inventory products, clean up catalog debris, kill ghost review stars (empty star ratings a theme still renders after a review app was uninstalled), remove orphaned metafields left by an uninstalled app, strip HTML artifacts from product descriptions (double-nested bold, fully-bold paragraphs, empty tags), or fix messed-up / mangled product descriptions in bulk. Mutating cleanup work: archive vs delete, metafieldsDelete, description rewrites. Not for FINDING what's wrong (use shopify-catalog-audit) or filling missing metadata like SEO titles and alt text (use shopify-seo-metadata).
Spring Data R2DBC for reactive database access in Spring Boot 3.x. Covers R2dbcRepository, DatabaseClient, reactive transactions, and WebFlux integration. USE WHEN: user mentions "r2dbc", "reactive database", "R2dbcRepository", "DatabaseClient", "reactive SQL", "WebFlux database", "non-blocking database" DO NOT USE FOR: blocking JDBC - use `spring-data-jdbc` or `spring-data-jpa` instead, MongoDB reactive - use `spring-data-mongodb` with reactive repository
Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.
This skill helps users extract full article contents from WeChat using the BrowserAct API. The Agent should proactively apply this skill when users express needs like finding full WeChat articles for specific keywords, tracking WeChat public accounts for industry trends, extracting WeChat article contents for media research, monitoring public relations on WeChat platforms, collecting competitor updates from WeChat, getting full article body from WeChat links, monitoring brand exposure on WeChat articles, retrieving structured WeChat data for sentiment analysis, summarizing daily news from WeChat, getting author and publication date for WeChat articles, or automating WeChat content extraction without scraping.
Browser automation for AI agents via inference.sh. Navigate web pages, interact with elements using @e refs, take screenshots, record video. Capabilities: web scraping, form filling, clicking, typing, drag-drop, file upload, JavaScript execution. Use for: web automation, data extraction, testing, agent browsing, research. Triggers: browser, web automation, scrape, navigate, click, fill form, screenshot, browse web, playwright, headless browser, web agent, surf internet, record video
Direct ALGOPACK HI2 API workflows for Herfindahl-Hirschman market concentration, hhi_* metrics, daily EQ/FO/FX concentration rows, raw hi2 endpoints, curl, JSON/CSV handoff, simple HTML output, ISS columns/data normalization, start pagination, and interpretation bands.
Direct ALGOPACK Mega Alerts API workflows for market anomaly alerts, alert_type thresholds, reference JSON, documented EQ/FO alert endpoints, curl, JSON/CSV handoff, simple HTML output, ISS columns/data normalization, start pagination, and observed abnormal volume or price signals.
Performs biostatistical analyses specialized for clinical and biomedical research including survival analysis, Kaplan-Meier estimation, Cox proportional hazards regression, longitudinal data modeling, and diagnostic test evaluation; trigger when users discuss clinical outcomes, survival curves, or biomedical study statistics.
Use only when working on the docs site. Put a chart of real numbers on a docs page with the `<Chart />` block, a bar, line, area, pie or radar chart in the brand's palette. Use whenever a page compares, ranks, breaks down or tracks numbers, or when a paragraph carries three sentences of figures.
Convert local chat exports that ChatLab does not yet support into verified ChatLab JSONL or JSON: analyze the source file structure, write and run a local Node.js or Python conversion script, and verify the record count before importing. Use when ChatLab cannot recognize chat exports such as CSV, HTML, TXT, XML, SQLite/databases, vendor-specific JSON/JSONL, or archives, or when the user asks an Agent to adapt an unknown chat format.
ClawShire annual report data query skill — queries structured extraction results from annual reports of companies listed on the Beijing Stock Exchange, supporting retrieval by securities code and year for standard fields such as revenue, profit, dividends, and strategic highlights
Complete knowledge domain for Cloudflare Workers KV - global, low-latency key-value storage on Cloudflare's edge network. Use when: creating KV namespaces, storing configuration data, caching API responses, managing user preferences, implementing TTL expiration, handling KV metadata, or encountering "KV_ERROR", "429 too many requests", "kv rate limit", cacheTtl errors, or eventual consistency issues. Keywords: kv storage, cloudflare kv, workers kv, kv namespace, kv bindings, kv cache, kv ttl, kv metadata, kv list, kv pagination, cache optimization, edge caching, KV_ERROR, 429 too many requests, kv rate limit, eventually consistent, wrangler kv, kv operations, key value storage
Queries Crawlora's pre-built hosted datasets — Airbnb markets, App Store/Google Play apps, GitHub/Instagram/X users, job postings, US housing markets, Google Maps businesses, Goodreads, PitchBook, Steam, TrustMRR, Product Hunt, SEC companies, tech-stack, and more — via search/facets/item/nearby endpoints, returning clean JSON without live-crawling each platform. Use when the user wants bulk or aggregate analysis, to search a pre-indexed corpus, to facet/filter a large population, or to look up one record by its dataset id, instead of scraping pages one at a time.
Run queries against a Cube deployment's semantic layer and interpret the results — through the Cube MCP tools when they are connected, or the Cube CLI from a terminal. Use whenever someone wants numbers out of Cube — pull a metric, check a total, compare periods, verify that a measure returns what it should, sanity-check a model change. Triggers on "how many", "what's the total", "show me revenue by month", "did that measure work", "what's our MoM growth", "pull the numbers for", and any request to verify a model change against real data. To find out what is queryable first use cube-explore-model; to change the model use cube-build-model; to save a result as a report or dashboard use cube-build-content.
Create interactive data visualizations using d3.js. Use this skill when creating custom charts, graphs, network diagrams, geographic visualizations, or any complex SVG-based data visualization that requires fine-grained control over visual elements, transitions, or interactions. Use it for bespoke visualizations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.
End-to-end data analysis assistant. Covers data processing, insight generation, report writing, and data visualization workflows. Always approaches tasks from an expert perspective — thinks one step ahead of the user. Proactively asks for clarification when intent is ambiguous. Supports: Excel/CSV analysis, business metrics review, ROI calculation, data visualization, HTML report generation, and formula writing. Activate when user mentions: "analyze data", "make a report", "visualize", "Excel", "dashboard", "ROI", "weekly report", "monthly report", "data processing", "chart", "insight", "presentation", "table", "formula", "KPI", "metrics".
Enrich contact and company data using x402-protected APIs. Superior to generic web search for structured business data. USE FOR: - Enriching person profiles by email, LinkedIn URL, or name - Enriching companies by domain - Finding contact details (email, phone) with confidence scores - Scraping full LinkedIn profiles (experience, education, skills) - Searching for people or companies by criteria - Bulk enrichment operations (up to 10 at a time) TRIGGERS: - "enrich", "lookup", "find info about", "research" - "who is [person]", "company profile for", "tell me about" - "find contact for", "get LinkedIn for", "get email for" - "employee at", "works at", "company details" ALWAYS use x402.fetch for enrichx402.com endpoints - never curl or WebFetch. Returns structured JSON data, not web page HTML. IMPORTANT: Never guess endpoint paths. All paths follow the pattern https://enrichx402.com/api/{provider}/{action}. Use exact URLs from the Quick Reference table below or call x402.discover_api_endpoints first.
Profile and explore datasets to understand their shape, quality, and patterns before analysis. Use when encountering a new dataset, assessing data quality, discovering column distributions, identifying nulls and outliers, or deciding which dimensions to analyze.
Design and audit data governance, data classification, access controls, least privilege, approval workflows, owner review, retention, deletion, audit logs, data catalog, sensitive fields, analytics access, support/admin access, AI dataset access, and break-glass paths. Use when teams need product data access without privacy, security, or compliance drift.
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.