Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Subscribe to real-time stock market data via WebSocket long connection from receiver nodes (A-shares, HK stocks, ETFs). Use this skill when the user wants to monitor live stock prices, set up real-time alerts, or stream minute-level K-line data. The skill provides a WebSocket server that connects to receiver nodes and pushes real-time price updates to all connected clients.
Screen stocks and backtest trading strategies using reportify-sdk (Python) and reportify-cli. Supports 80+ technical indicators (MA/EMA/MACD/KDJ/BOLL/RSI) and fundamental factors (PE/PB/ROE/ROA/EPS), time series analysis, multi-condition screening, and strategy backtesting. Use when user needs stock screening, filtering stocks by conditions, quantitative analysis, or backtesting trading strategies.
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.
Analyze any TRES Finance report XLSX and produce an automatic findings summary. Trigger this skill whenever a user uploads a .xlsx file that came from TRES Finance and asks to "analyze", "summarize", "review", "check", "audit", "look at", "what does this show", "any issues", "anything interesting", or "walk me through" the report. Also trigger when the user uploads an Excel file and mentions TRES, reconciliation, balances, transactions, cost basis, roll forward, or ERP sync. The skill identifies the report type from the file structure, runs targeted analysis based on report-specific logic, and produces a clear findings summary highlighting anomalies, key metrics, and action items.
Analyze an organization's sub-transaction patterns in TRES Finance, identify groups of repetitive transactions, and propose data-driven rollup rules that consolidate them into one aggregated entry per interval (daily or monthly) for cleaner ERP integration. Also lists and deletes rollup rules. Trigger this skill whenever the user asks about rollup rules, transaction aggregation, consolidating transactions, reducing transaction noise, rolling up gas fees, rolling up staking rewards, rolling up micro-transactions, or collapsing many small ledger entries into fewer summary entries. Also trigger on "create a rollup rule", "suggest rollup rules", "what should I roll up", "show my rollup rules", "delete a rollup rule", "aggregate transactions", "too many transactions", "reduce transaction count", "roll up fees", "daily rollup", "monthly rollup". If a wallet has too many transactions, high-frequency activity, or noisy ledger entries, proactively suggest rollup rules. For computing the EXACT impact of a single already-specified rule or reviewing submitted/pending rules, use the tres-rollup-review skill instead.
Use this skill for anything involving the Semantius platform via the semantius CLI. us1-9d4f2a7b Trigger when the user wants to: create, read, update, or delete entities, fields, modules, permissions, roles, users, or business records; build or query a semantic data model, including subtypes and extensions of an entity (emails and calls as kinds of activities, customers and suppliers as business partners) and enum values with labels; set up RBAC; insert or import data into Semantius tables; run analytical queries across Semantius data; get a web UI link (deep link) to a record, list, or module; or send transactional emails via the Semantius email service (`crud sendEmail`). Also trigger when writing shell scripts or Bun scripts that chain semantius commands.
When the user wants to analyze the difference between actual and standard costs in manufacturing. Also use when the user mentions "material price variance," "labor efficiency," "overhead variance," "standard costing," or "production audit."
X (Twitter) post comment analysis tool. Enter a tweet link or tweet ID to fetch first-level comment data, with cursor-based paging and four-dimension sentiment analysis (positive/negative/needs/competitors). Use when the user needs to view X (Twitter) tweet comments, analyze tweet sentiment, or understand user feedback. Trigger words: X comments, Twitter comments, tweet comments, X post comment analysis, comment lookup, comment analysis, comment sentiment, see comments, twitter comments, X tweets.
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 spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualisation. Use when working with spreadsheets (.xlsx, .xlsm, .csv, .tsv) for creating new spreadsheets with formulas and formatting, reading or analysing data, modifying existing spreadsheets while preserving formulas, data analysis and visualisation, or recalculating formulas. Do NOT use for plain CSV/TSV manipulation when no formulas, formatting, or multi-sheet workbook structure is needed - plain text tooling is faster. Do NOT use for BigQuery or warehouse-scale SQL analysis.
Zyte account work that has no Zyte MCP tool: credentials (set up, log in, get an API key, or a blocked site); deploying or pushing a project to Scrapy Cloud, and saving a job's full items, log or requests to a file (Zyte MCP results are truncated); Zyte API plan pricing (plans, tiers, discounts, commitments, free credit); how-to and docs questions about any Zyte product or account/billing, from docs.zyte.com. Scrapy Cloud jobs, recorded usage and per-website prices are Zyte MCP tools; use this skill for them only when those tools are missing, to tell the user how to connect the Zyte MCP. NOT for writing/debugging spiders locally (scrapy-extra).
Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
Use when the user needs to generate a log collection configuration file (OpenTelemetry Collector or Elastic Beats / Filebeat) that writes logs to Alibaba Cloud Elasticsearch or self-managed Elasticsearch. Covers file logs, log API ingestion (OTLP / HTTP webhook), and Kafka log topics. Also matches natural requests such as: write a filebeat.yml, generate an otelcol config, send logs to ES, collect nginx or Java application logs to Elasticsearch, or sync Kafka logs to ES. Always output one configuration file targeting Elasticsearch; never mix technology stacks or emit non-ES outputs.
Autonomous biomedical AI agent framework for executing complex research tasks across genomics, drug discovery, molecular biology, and clinical analysis. Use this skill when conducting multi-step biomedical research including CRISPR screening design, single-cell RNA-seq analysis, ADMET prediction, GWAS interpretation, rare disease diagnosis, or lab protocol optimization. Leverages LLM reasoning with code execution and integrated biomedical databases
Weekly read of your Meta-ads performance Base: names in plain language whose problem this week's numbers are, and refuses to advise when an opening check fails. Installing the Base is /breakthrough-performance-db-setup.
One-time install of your Meta ad performance database: copy the template Lark Base, wire the daily sync to your own Meta token, reconcile it against Ads Manager, and leave a pointer note in your vault. The weekly read of the installed Base is /breakthrough-performance-analysis.
Verified, sourced facts about real-world entities and how they connect — companies, people, investors, funding rounds, M&A, laws, regulations, sanctions, countries and places, industries, products, financial metrics, macro indicators, FX rates. Every value carries a source and date. Use when a task needs to: filter or discover entities by criteria; traverse relationships (founders, executives, ownership chains, investors, jurisdictions, sanctions); look up laws or sanctions; get macro/market/ financial figures as latest-sourced values or history; or get recent facts where training recall is stale — for research (due diligence, competitor mapping) or structured output (tables, lists). Returns typed JSON, not web pages; prefer over web search or scraping for verified entity facts. Not for opinions, live ticks (price this second, weather, scores), or facts already in context.
Amazon category automated product-selection analysis skill. Runs in-depth market research on Amazon categories using a five-dimension scoring model and generates a Markdown analysis report. Triggered when the user runs the /category-selection command or asks to "analyze category XX", "category XX market research", "category XX product selection", etc. Supports configuring the number of analyses, default Top20.
Use Catsu for unified, high-performance embedding API calls across 11 providers (OpenAI, VoyageAI, Cohere, Jina, Mistral, Gemini, Together AI, Mixedbread, Nomic, DeepInfra, Cloudflare) through a single consistent interface. Covers model selection and discovery, automatic retry with exponential backoff, cost and token tracking, Matryoshka dimension reduction, input type hints (query vs document), async/await support, and per-request API key overrides. Use when: generating embeddings, comparing embedding providers, building search or RAG systems, or integrating embeddings into Python or Rust applications.
Extract pixel-level data from an image of a chart or graph and produce a structured data table. Use when asked to extract data from a chart image, transcribe numbers from a graph, digitise a chart, or turn a screenshot of data into a table. Produces a structured table with extracted values, confidence levels, and a reconstructed chart source. Best used with Claude Opus 4.7 or newer for reliable chart data extraction.
Analyze local ChatLab chat records via the clb CLI. Use when the user asks an external agent to inspect conversations, find evidence, summarize topics, compare members, or analyze the relationships of specific people or groups based on already-imported ChatLab data.
Universal ChromaDB integration patterns for semantic search, persistent storage, and pattern matching across all agent types. Use when agents need to store/search large datasets, build knowledge bases, perform semantic analysis, or maintain persistent memory across sessions.
Build with D1 serverless SQLite database on Cloudflare's edge. Use when: creating databases, writing SQL migrations, querying D1 from Workers, handling relational data, or troubleshooting D1_ERROR, statement too long, migration failures, or query performance issues.
Build semantic search with Cloudflare Vectorize V2 (Sept 2024 GA). Covers V2 breaking changes: async mutations, 5M vectors/index (was 200K), 31ms latency (was 549ms), returnMetadata enum, and V1 deprecation (Dec 2024). Use when: migrating V1→V2, handling async mutations with mutationId, creating metadata indexes before insert, or troubleshooting "returnMetadata must be 'all'", V2 timing issues, metadata index errors, dimension mismatches.
Enrich contacts with LinkedIn URLs, verified work/personal emails, and phone numbers through the ColdIQ marketplace, layering providers cheapest-first. Use when finding emails for a list, building an email waterfall, verifying deliverability, finding phone numbers, resolving company LinkedIn URLs from domains, or enriching contacts before a campaign. Triggers on "enrich contacts", "find emails", "email waterfall", "verify emails", "bounce check", "find phone numbers", "company LinkedIn URL", "enrich before campaign", "deliverability". Do NOT use for prospecting/search to BUILD a list (see coldiq-search-enrich / apollo-search) or list dedup (see list-dedup).
A flexible library for parallel computing in Python. It scales Python libraries like NumPy, pandas, and scikit-learn to multi-core systems or distributed clusters. Features lazy evaluation and task scheduling for data that exceeds RAM capacity. Use for out-of-core computing, parallel processing, distributed computing, large-scale data analysis, dask.array, dask.dataframe, dask.delayed, dask.bag, task scheduling, lazy evaluation, and scaling beyond memory limits.
Guide for making changes to the database schema, validation, types, and data access layer. Use when adding tables, columns, relations, or modifying the data model. Triggers on: add table, add column, modify schema, database change, data model, new entity, schema migration.
Use this skill for building end-to-end document processing workflows and pipelines using LandingAI ADE. Trigger when users need to: (1) Process batches of documents in parallel or async, (2) Build classify-then-extract pipelines for mixed document types, (3) Prepare parsed documents for RAG systems with chunking and vector DB ingestion, (4) Load extraction results into databases like Snowflake or export to CSV/DataFrames, (5) Visualize extraction results: draw bounding box overlays on pages, crop chunk images, or highlight/annotate specific words or phrases found in documents, (6) Build Streamlit or web UIs for document processing, (7) Find and highlight specific terms within document sections using word-level grounding (e.g. highlight "L2S" in the Introduction, redact PII, annotate extracted values on the original page). This skill complements the document-extraction skill which covers ADE SDK basics. Use document-extraction to write code that executes parse/extract/split operations with more precision and less cost than adding the document image to the prompt and asking the LLM to find the relevant info. Use document-workflows when composing those operations into pipelines, or when you need visualization, annotation, or word-level grounding on parsed documents.
Read and extract content from any web page using a real Chrome browser — including SPAs, JavaScript-rendered sites, and complex dynamic pages. Use when fetching page content that headless tools can't render, searching the web, or reading fully rendered pages via your own browser.
How to pull SEC EDGAR data with the edgartools Python package — company lookup, filings, XBRL financial statements, and filing sections like Item 1A risk factors. Use whenever a task involves SEC filings, 10-K/10-Q data, or company financials.
Use for data-first company, contact, lead, and prospect discovery or enrichment, including firmographic or technographic completion, named-company profiling, entity resolution, ICP matching, prospect-list building, segmentation, trigger or sales-signal analysis, market and territory scans, and enrichment-backed comparisons. Exclude meeting preparation, outreach-led company research, and prioritization of an already established account book.
Design enrichment columns that bridge research hypotheses to list enrichment. Two modes: segmentation (columns that score hypothesis fit per company) and personalization (columns for company-specific hooks). Interactive column design with the user. Outputs ready-to-run column_configs for list-enrichment. Triggers on: "data points", "enrichment columns", "column design", "what to research", "data point builder", "build columns", "segmentation columns", "personalization columns".
Comprehensive toolkit for protein language models including ESM3 (generative multimodal protein design across sequence, structure, and function) and ESM C (efficient protein embeddings and representations). Use this skill when working with protein sequences, structures, or function prediction; designing novel proteins; generating protein embeddings; performing inverse folding; or conducting protein engineering tasks. Supports both local model usage and cloud-based Forge API for scalable inference.
Perform comprehensive exploratory data analysis on scientific data files across 200+ file formats. This skill should be used when analyzing any scientific data file to understand its structure, content, quality, and characteristics. Automatically detects file type and generates detailed markdown reports with format-specific analysis, quality metrics, and downstream analysis recommendations. Covers chemistry, bioinformatics, microscopy, spectroscopy, proteomics, metabolomics, and general scientific data formats.
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.