Skills
97,501 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Graph database implementation for relationship-heavy data models. Use when building social networks, recommendation engines, knowledge graphs, or fraud detection. Covers Neo4j (primary), ArangoDB, Amazon Neptune, Cypher query patterns, and graph data modeling.
Toolkit for comprehensive Spreadsheet reading, creation, editing, and analysis with visual quality control. Use to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing tabular data, (3) Modifying existing spreadsheets while preserving formulas, (4) Building financial models with proper formatting, (5) Data visualization with in-sheet charts, or any other spreadsheet tasks.
Investigate an onchain entity, protocol, market, or wallet question with Allium. Use when the task needs a defensible analytical answer rather than a one-off lookup: check Terminal for existing coverage, select the right source, build the method from a small test to production, validate the result, make calibrated claims, and save reproducible Explorer queries (with an optional dashboard hand-off prompt for Allium Assistant). Chains after `data-matching` (source selection) rather than repeating it
Download sales, trends, financial, and analytics reports from App Store Connect using the `asc` CLI tool. Use this skill when: (1) Downloading sales reports: "asc sales-reports download --vendor-number ... --report-type SALES ..." (2) Downloading finance reports: "asc finance-reports download --vendor-number ... --report-type FINANCIAL ..." (3) Analytics reports: "asc analytics-reports request/list/reports/instances/segments" (4) Checking app sales, revenue, downloads, subscriptions, proceeds, or analytics (5) User says "download my sales report", "show sales data", "get financial report", "check my app revenue", "how many downloads", "subscription metrics", "app analytics", "app usage data", "engagement metrics"
This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.
This skill should be used when analyzing market breadth charts, specifically the S&P 500 Breadth Index (200-Day MA based) and the US Stock Market Uptrend Stock Ratio charts. Use this skill when the user provides breadth chart images for analysis, requests market breadth assessment, positioning strategy recommendations, or wants to understand medium-term strategic and short-term tactical market outlook based on breadth indicators. All analysis and output are conducted in English.
Methodology playbook for analyzing cross-chain bridge and messaging-protocol data on Allium—CCTP, LayerZero, Wormhole, Across, canonical/application bridges, and general lock-and-mint flows. Use whenever a question touches bridge transfer volume, net flows between chains, tracing a specific cross-chain transaction, or attributing bridge activity to a protocol/integrator—even if the user just names a bridge or asks to "trace this transfer across chains." Covers direction and double-counting mechanics and non-EVM tracing gaps that commonly arise before writing SQL. Distinct from a formal third-party coverage benchmark (see bridge-protocol-coverage-assessment); this is the lighter-weight reference for getting an individual bridge query right
Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.
Use this skill first when the user wants to design or plan a dashboard, especially Vizro dashboards. Enforces a 3-step workflow (requirements, layout, visualization) before implementation. Activate when the user asks to create, design, or plan a dashboard. For implementation, use the dashboard-build skill after completing Steps 1-3.
Methodology playbook for analyzing DEX/AMM trading, liquidity, and pricing data on Allium—Uniswap, PancakeSwap, PumpSwap/Pump.fun, Orca, Pendle, aggregators (0x, 1inch), and perp DEXs like Hyperliquid. Use whenever a question touches swap volume, trade counts, liquidity positions, impermanent loss, aggregator fee/routing analysis, or DEX-derived token prices—even if the user just names a protocol or asks why a number "looks off" compared with a known reference. Covers per-hop double counting, protocol-vs-project grouping, and fee-leg reconstruction pitfalls that commonly arise before writing SQL
Douyin short-video growth assistant for content creators, operators, brands, and e-commerce, used when the user provides Douyin content links; it helps generate sentiment and public-opinion assessments, user profiles and intent signals, operating suggestions, and reply suggestions based on the input material.
Create a visual Jira sprint dashboard from Jira project, space, sprint, board, filter, JQL, work item keys, or Jira URL data. Use when the user asks for a Jira sprint dashboard, standup dashboard, sprint review, delivery review, engineering manager dashboard, WIP review, planning view, closeout view, or a visual snapshot of Jira work that is more useful than a flat report. Use the richest dashboard format supported by the current agent, such as Cursor Canvas, an interactive artifact, HTML, or Markdown.
Methodology playbook for analyzing DeFi lending protocol data on Allium—Aave, Morpho, Fluid, Euler, Maple, Compound-style forks, and per-chain lending markets. Use whenever a question touches deposits, borrows, liquidations, collateral swaps, utilization, TVL, or leverage on a lending protocol—even if the user just names a protocol/asset or asks to compare two lending metrics tables. Covers table-per-event-type join gotchas, column-naming inconsistencies, and liquidation-fill double counting that commonly arise before writing SQL
Guides structured security log analysis across authentication, network, endpoint, and cloud audit log sources. Auto-invoked when the user shares log data, asks about suspicious events, needs help interpreting Windows Event IDs or Linux auth logs, or is establishing baselines for anomaly detection. Produces log source taxonomy, anomaly identification, baseline recommendations, and correlation findings mapped to MITRE ATT&CK v16 techniques.
Decomposes high-level North Star metrics into actionable sub-metrics and leading indicators, maps causal relationships between metric levels, and identifies high-impact experiments to move key metrics. Use when setting product North Star metrics, decomposing business metrics into drivers, mapping strategy to measurable outcomes, identifying which metrics to move through experimentation, understanding leading vs lagging indicators, prioritizing metric improvement opportunities, or when user mentions metric tree, metric decomposition, North Star metric, KPI breakdown, metric drivers, or how metrics connect.
Create, inspect, retrieve, author, revise, enrich, audit, repair, research, analyze change impact, migrate, validate, and maintain knowledge for AI agents as conformant Open Knowledge Format (OKF) bundles. Use for /okf commands; work on an existing OKF bundle; documentation of tables, datasets, views, metrics, runbooks, playbooks, APIs, schemas, or join paths; exports from catalogs, metadata, websites, URLs, or source code; and any knowledge an agent will read later. Apply implicitly when editing an OKF bundle so conformance, provenance and sources, generated and verified trust signals, freshness, log.md, indexes, and cross-links remain current. Tracks OKF v0.2, including attested computations and migration from v0.1.
Parse OpenClaw, Claude Code, or Hermes agent session logs and upload structured telemetry via the Cobo Agentic Wallet TelemetryAPI. Zero external dependencies — only requires Python 3 and a valid caw installation (API key auto-detected). Use when: user wants to analyze a session from any supported agent platform, upload session telemetry, view session traces, or debug agent behavior.
Statistical arbitrage tool for identifying and analyzing pair trading opportunities. Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations for market-neutral strategies. Use when the user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction. Supports correlation analysis, cointegration testing, and spread backtesting.
Write OKRs, KPIs, or a North Star Metric derived from product strategy — or review an existing goal set. Enforces outcome-based key results with baselines and targets, one focus objective per team per cycle, leading+lagging balance, and guardrails against gameable targets. Trigger when asked to write OKRs, set quarterly goals, define KPIs or success metrics, choose a North Star Metric, or run goal-setting/planning for a team or product.
Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube, Google Images), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", "image SERP", "google images", "image rankings", or "real search data".
Google SEO APIs: Search Console (Search Analytics, URL Inspection, Sitemaps), PageSpeed Insights v5, CrUX field data with 25-week history, Indexing API v3, and GA4 organic traffic. Provides real Google field data for Core Web Vitals, indexation status, search performance, and organic traffic trends. Use when user says "search console", "GSC", "PageSpeed", "CrUX", "field data", "indexing API", "GA4 organic", "URL inspection", "google api setup", "real CWV data", "impressions", "clicks", "CTR", "position data", "LCP", "INP", "CLS", "FCP", "TTFB", or "Lighthouse scores".
Guided statistical analysis: test choice, assumption checks, effect sizes, power, APA reporting. Pick tests, verify assumptions, or format results for publication. Covers frequentist (t-test, ANOVA, chi-square, regression, correlation, survival, count, reliability) and Bayesian. Use statsmodels or pymc-bayesian-modeling to fit.
Supabase (open source Firebase alternative, BaaS) reference. PostgreSQL database, Auth (Email / OAuth / Magic Link / Phone / SSO), Storage, Edge Functions (Deno), Realtime (subscriptions / presence / broadcast), Vector embeddings, supabase-js, supabase CLI, RLS (Row Level Security), migrations. Based on PostgreSQL. SQL Server / node-mssql is a separate skill, mssql.
Use this skill for JavaScript apps needing Excel-like UI using the Syncfusion Spreadsheet Component. Trigger for creating, viewing, editing Excel (.xlsx, .xls, .xlsb) and CSV files; embedding spreadsheet editors; data binding from APIs/JSON; using formulas, charts, validation, filtering, or conditional formatting. Also trigger when users reference spreadsheet files ("open xlsx", "load Excel file", "add Syncfusion spreadsheet", "bind data to spreadsheet"). Do NOT trigger for standalone file processing without UI components.
Integrates uCharts data visualization into UniApp projects including easycom configuration, pages.json and manifest.json setup, platform-specific handling (H5, mini-program, App, nvue), and chart lifecycle management. Use when the user needs to add charts to a UniApp project, configure uCharts components, or handle cross-platform chart rendering.
Comprehensive US stock analysis including fundamental analysis (financial metrics, business quality, valuation), technical analysis (indicators, chart patterns, support/resistance), stock comparisons, and investment report generation. Use when user requests analysis of US stock tickers (e.g., "analyze AAPL", "compare TSLA vs NVDA", "give me a report on Microsoft"), evaluation of financial metrics, technical chart analysis, or investment recommendations for American stocks.
When the user wants to analyze a YouTube video's performance, review retention data, diagnose low CTR, or understand why a video underperformed or overperformed. Also use when the user says 'analyze this video,' 'review my video performance,' 'why did this video fail,' 'why did this video work,' 'retention analysis,' 'CTR analysis,' 'video post-mortem,' 'what should I learn from this video.' For full channel health check, see channel-audit. For improving future ideas based on learnings, see idea-generation.
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.
Invoke SLS DataAgent to autonomously perform data acquisition, processing, analysis, and visualization for Alibaba Cloud SLS (Simple Log Service). Acts as a fully automated data analyst — ask a question in natural language, get structured conclusions and charts. Use when the user asks about: 数据分析, 取数, 数据查询, 日志分析, SLS, 可视化, 图表, 数据洞察, data analysis, DataAgent, 全自动数据分析师.
Guide for using the Marketplace Ad Pros MCP server to manage and analyze Amazon Advertising accounts. Use when working with Amazon Ads campaigns, performance reports, bid/budget recommendations, keyword research, or product data through the MCP tools.
Write efficient data queries in B2C Commerce for products, customers, and orders. Use this skill whenever the user needs to search products on a storefront page, look up customer profiles, query orders, paginate search results, or fix slow category pages. Also use when they ask about performance problems with data access, replacing database-intensive APIs with index-backed alternatives, or choosing between search vs query methods — even if they just say "my category page is slow" or "how do I find customers by email".
Build a new dashboard, or update an existing one, from a set of insights — the same job the in-app assistant does with its upsert-dashboard tool, but over MCP. Use when a user asks to create a dashboard, put several metrics/charts together on one page, assemble a dashboard for a topic (product analytics, retention, revenue, activation, etc.), or add/remove/replace insights on a dashboard they already have. Covers deciding create vs update, reusing existing insights vs creating new ones, and using PostHog's vetted dashboard templates as reference for what a strong dashboard on a topic looks like.
Plan and run one mechanical edit across a whole population of records. Use when reformatting, renaming, retagging or stripping a field across dozens or thousands of issues, rows, files or board items, and whenever a mass edit needs a dry run, a rollback or proof that it did not touch anything else.
Post-earnings quarterly update reports for A-share companies under coverage. Analyzes 业绩快报/正式财报, generates variance tables (actual vs 一致预期 vs prior), flags key drivers, and drafts structured earnings notes in Chinese sell-side format. Use instead of the original earnings-analysis skill for Chinese equities. Triggers on "A股财报分析", "季度业绩点评", "年报/中报点评", "earnings review", or "[company] earnings".
Use Chonkie for fast, lightweight text chunking in RAG pipelines. Covers all 11 chunker types (Token, Fast, Sentence, Recursive, Semantic, Late, Code, Neural, Slumber, Table, TeraflopAI), the Pipeline API for chaining fetch→process→chunk→refine→export→store workflows, embeddings refineries, vector DB handshakes (Chroma, Qdrant, Pinecone, etc.), tokenizer selection, recipe system, REST API server, and async/batch processing. Use when: splitting documents for embeddings, building ingestion pipelines, chunking code or markdown, setting up vector DB ingestion, or any text segmentation task for retrieval-augmented generation.
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.