Skills
96,981 packaged capabilities and playbooks for agents. Install any of them with one command.
Data & databases
Pulls Bigdata.com (RavenPack) financial and news data via the official bigdata-client SDK and /v1/* REST endpoints — structured financials, prices, analyst estimates, entity-sentiment series, annotated chunk search, screener. Use when the Bigdata MCP's tearsheets/search feel thin and you need the machine-readable substrate: mentions of Bigdata.com, RavenPack, a bd_v2_ key, rp_entity_id, or chunk/query_unit cost.
Buffett-style single-stock moat diagnostic — "Would Buffett buy this stock?" Five dimensions: business & moat / financial health / management & capital allocation / valuation & margin of safety / long-term visibility. Data from Longbridge CLI first, MCP fallback, WebSearch only for gaps. Runs cross-statement reconciliation (勾稽校验) BEFORE scoring; data-source appendix closes with a one-line reconciliation summary. Output: star-rated radar card, dimension detail, Buffett-voice narrative, mandatory holding-period education block. Triggers: "巴菲特", "护城河", "巴菲特会买吗", "价值投资", "好生意", "宽护城河", "定价权", "诊股", "巴菲特诊股", "巴菲特视角", "长期持有", "護城河", "巴菲特會買嗎", "價值投資", "寬護城河", "定價權", "診股", "巴菲特診股", "巴菲特視角", "長期持有", "Buffett", "Warren Buffett", "moat", "economic moat", "wide moat", "pricing power", "value investing", "owner earnings", "would Buffett buy", "Berkshire-style", "quality compounder".
ToolUniverse plugin router. STEP 1 BEFORE ANY ANALYSIS: if the data folder contains `*_executed.ipynb`, run `tu run read_executed_notebook '{\"data_folder\":\"<path>\",\"search\":\"<keyword>\"}'` to extract its cell outputs and apply EVERY filter/sample-exclusion the notebook used — even when the question says 'Using DESeq2/Run X/Compute Y' (this describes the METHOD the notebook used, not a request to rerun). The notebook's cell outputs are the only published authoritative answers; reimplementing or reading stale pre-computed CSVs in the data folder produces different numbers because of outlier-sample removal, library version, and filter steps you don't see by skimming. STEP 2 routing — pick a sub-skill name from this exact list (never invent): tooluniverse-rnaseq-deseq2 (RNA/miRNA-seq DE, correlation, PCA, clustering, dispersion), tooluniverse-gene-enrichment (GO/KEGG/Reactome/GSEA/pathway enrichment), tooluniverse-statistical-modeling (regression, ANOVA, ordinal/logistic, chi-square, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.
Audit experiment integrity before claiming results. Uses cross-model review (external reviewer backend) to check for fake ground truth, score normalization fraud, phantom results, and insufficient scope. Use when user says "审计实验", "check experiment integrity", "audit results", "实验诚实度", or after experiments complete before writing claims
Deep web scraping, screenshots, PDF parsing, and website crawling using Firecrawl API. Use when you need deep content extraction from web pages, page interaction is required (clicking, scrolling, etc.), or you want screenshots or PDF parsing.
Generate an interactive single-file HTML visualization — mockup, wireframe, ERD, flowchart, chart, slides, architecture diagram, dashboard, timeline, mindmap, kanban, or table. Use when the user wants something rendered rather than described: a UI mockup or wireframe, a database schema as an ERD, business logic as a flowchart, data as a chart, or content as slides. Also use when they point at a file (schema, spec, data) and ask to see it visualized.
Industry-standard gradient boosting libraries for tabular data and structured datasets. XGBoost and LightGBM excel at classification and regression tasks on tables, CSVs, and databases. Use when working with tabular machine learning, gradient boosting trees, Kaggle competitions, feature importance analysis, hyperparameter tuning, or when you need state-of-the-art performance on structured data.
Tushare Pro financial data API query assistant. Helps users query financial data on Chinese stocks, funds, futures, bonds, and more. Use this skill when users need stock quotes, financial data, basic information, or macroeconomic data.
Create and edit Obsidian Bases (.base files) with views, filters, formulas, and summaries. Use when working with .base files, creating database-like views of notes, or when the user mentions Bases, table views, card views, filters, or formulas in Obsidian.
Use this skill to extract structured Markdown/JSON from PDFs and document images—tables with cell-level precision, formulas as LaTeX, figures, seals, charts, headers/footers, multi-column layout and correct reading order. Trigger terms: 文档解析, 版面分析, 版面还原, 表格提取, 公式识别, 多栏排版, 扫描件结构化, 发票, 财报, 复杂 PDF, PDF转Markdown, 图表, 阅读顺序; reading order, formula, LaTeX, layout parsing, structure extraction, PP-StructureV3, PaddleOCR-VL.
ALWAYS USE THIS SKILL for anything involving Salesforce Archive (also called Trusted Services Archive) — search, view, unarchive, analyze, mask, and erase (RTBF) archived records via the Archive Connect API, and reading archive job status from the ArchiveActivity object. TRIGGER when: user mentions Salesforce Archive, Trusted Services Archive, archive/unarchive records, ArchiveActivity, archive jobs, archive policy, archive analyzer, archived record search, archive storage, archive failure logs, right to be forgotten / RTBF on archived data, or masking archived PII — including phrasings like 'find records that were archived', 'restore archived data', 'why did the archive job fail', 'download the archive failure log', or 'monitor my archive jobs', AND even when they ask you to explain, give guidance, or write a runbook/doc about these topics rather than run code. SKIP when: the user wants generic data-export/backup unrelated to the Archive add-on, or wants to build the archive policy UI metadata.
Command-line interface for CloudAnalyzer — an agent-friendly harness for CloudAnalyzer, a QA platform for mapping, localization, and perception outputs. Supports 27 commands across 8 groups: point cloud evaluation, trajectory evaluation, ground segmentation QA, config-driven quality gates, baseline evolution, processing, visualization, and interactive REPL.
Simulates real users searching on Amazon’s storefront to retrieve real-time keyword rankings and search results page data. Trigger this skill when the user mentions Amazon product search, search result scraping, keyword rankings on search pages, ASIN ranking position lookup, competitor discovery, search-page price comparisons, sponsored product analysis, new product monitoring, storefront search simulation, Amazon search, keyword ranking, search results, ASIN ranking position, competitor discovery, price comparison, sponsored product analysis, real-time search, or new product monitoring. Even if the user does not explicitly mention “search simulation,” trigger this skill whenever their needs involve real-time Amazon search results, product ranking data, or storefront SERP analysis.
Use when working with Lightdash YAML files, dbt models with Lightdash metadata, the lightdash CLI (deploy, upload, download, preview, lint, warehouse-catalog, sql, set-warehouse, apps create/preview/validate), or managing charts, dashboards, spaces and access, AI agents, scheduled content, data apps, custom chart types (Chart Studio, `apps create --chart-type`, `--chart-types`), organization Data App themes, data-app external connections, users, groups, custom roles, metrics, and dimensions as code
Analyze Reddit threads for sentiment, consensus opinions, top arguments, and discussion patterns. Use this when users want to understand Reddit community opinions, analyze discussions, or gather insights from subreddit conversations.
Use the Supermemory CLI (`supermemory` 5.x on npm) from the terminal or from scripts and agents. Add content, search, read profiles, forget memories, manage documents, namespaces, connectors, API keys and team members, install coding-agent plugins, migrate a project to the v5 API, and run the local self-hosted server. Covers every command, subcommand, flag and the JSON output contract.
Add PostHog AI Observability to trace AI model usage. Use after implementing LLM features or reviewing PRs to ensure all generations are captured with token counts, latency, and costs. Also handles initial PostHog SDK setup if not yet installed.
Read and write to Upstash Redis-compatible key-value store via REST API. Use when there is a need to save or retrieve key-value data, use Redis features (caching, counters, lists, sets, hashes, sorted sets, etc.) for the current interaction, or when the user explicitly asks to use Upstash or Redis.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to “open a website”, “fill out a form”, “click a button”, “take a screenshot”, “scrape data from a page”, “test this web app”, “login to a site”, “automate browser actions”, or any task requiring programmatic web interaction.
Read and write Google Sheets spreadsheets - get content, update cells, append rows, fetch specific ranges, search for spreadsheets, and view metadata. Use when user asks to: read a spreadsheet, update cells, add data to Google Sheets, find a spreadsheet, check sheet contents, export spreadsheet data, or get cell values. Lightweight integration with standalone OAuth authentication supporting full read/write access.
When you or another skill needs to fetch the content of a social media post by URL — tweet, X thread, LinkedIn post, Instagram post, TikTok video, Bluesky post, Reddit thread, Mastodon status, Threads post, Hacker News thread. Returns normalized structured data (author, posted_at, text, engagement counts, media URLs, replies if requested) regardless of platform. Tries strategies in order: direct API (Bluesky, Mastodon, HN, Reddit), agent-browser with modal dismissal (LinkedIn, X preview), Wayback Machine (older posts), paid APIs (ScrapeCreators / Apify — only if env keys present). Triggers on \"/social-fetch <url>,\" \"fetch this tweet,\" \"fetch this post,\" \"what does this LinkedIn say,\" \"read this thread,\" \"pull this post.\" For what's said inside a video, use watch-video. Used by deep-research (citing specific posts), jab-hook (inspiration account analysis), business-brainstorm (competitor / operator commentary).
Build, fix, audit, and migrate Drift persistence in Dart CLI, server-side, and non-Flutter desktop apps. Use when adding SQLite with package:drift/native.dart, configuring PostgreSQL with drift_postgres and package:postgres, writing type-safe tables, queries, writes, streams, and migrations, resolving build_runner or drift_dev failures, or validating Dart database code with code generation, analysis, tests, and migration checks
Graham cigar-butt batch screener — runs Benjamin Graham's NCAV / net-net / defensive-investor hard filters across an index or market universe and returns a ranked candidate list with NCAV ratio, PE, PB, dividend yield, debt coverage, 5y earnings stability, Graham buy price, and a dynamic value-trap warning. Longbridge CLI first; MCP fallback if CLI unavailable; WebSearch fills genuine gaps (PMI, sector outlook). Every figure footnoted to its source. Auto-switches model for banks / insurance / REITs and flags <2y IPOs and suspended names. Triggers: “格雷厄姆筛选”, “格雷厄姆筛股”, “捡烟蒂榜单”, “烟蒂股榜”, “NCAV筛选”, “NCAV排行榜”, “净流动资产筛选”, “防御型投资者筛股”, “撿煙蒂榜單”, “煙蒂股榜”, “NCAV篩選”, “淨流動資產篩選”, “防禦型投資者篩股”, “Graham screen”, “Graham screener”, “NCAV screen”, “net-net screen”, “net-net list”, “cigar-butt screen”, “defensive investor screen”, “liquidation value screen”, “Benjamin Graham screen”.
Generates Enonic XP content type XML schema definitions from natural-language descriptions. Covers structured content modeling including input types, form layout, option sets, item sets, mixins, x-data, and content-type inheritance. Use when creating, scaffolding, or generating Enonic XP content type definitions, adding fields or sets to existing content types, or querying Enonic XP input types and super-types. Do not use for non-Enonic content modeling, GraphQL queries, JavaScript/TypeScript controllers, or generic XML editing unrelated to Enonic schemas.
Add access control to a ZenStack V3 schema. Use when writing access policies (@@allow / @@deny), the auth() function, field-level rules, post-update rules, or setting up the policy plugin and $setAuth runtime. (For data validation — @email/@length/@@validate — see zenstack-querying.)
Composes, debugs, and optimizes Guillotine GraphQL queries for Enonic XP headless content delivery. Covers query construction, variable usage, filtering, aggregation, pagination, sorting, and TypeScript type generation from the auto-generated Guillotine schema. Use when writing or troubleshooting Guillotine queries, querying custom content types through GraphQL, or generating typed interfaces from Guillotine responses. Don't use for content type XML definitions, non-Enonic GraphQL APIs (Apollo, Hasura), server-side lib-content queries, or Guillotine deployment and CORS configuration.
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.