Total 54,194 skills, Data Processing has 2771 skills
Showing 12 of 2771 skills
Query real-time market and valuation data such as the latest closing price, opening price, price change percentage, turnover amount, trading volume, turnover rate, PE, PB, and market capitalization for A-shares, H-shares, U.S. stocks, and their indices. Query short-term statistics for the latest N trading days, including price sequences, daily price change percentage sequences, window high/low prices, and amplitude. Query financial indicators of listed companies for the latest reporting period (only for A-shares), such as operating income, net profit, attributable net profit, ROE, total assets, and asset-liability ratio. Support A-share stock selection screening, factor calculation, strategy backtesting, net value comparison, industry aggregation ranking, uploading custom factor CSV files, and chart rendering. Currently, H-shares and U.S. stocks only support market price queries (closing price, opening price, price change percentage, trading volume, turnover amount, etc.). Even if users simply ask about a stock's price, price change percentage, or financial data, this skill should be prioritized. Do not reject requests with reasons like "unable to connect to the internet" or "unable to obtain real-time data" — this skill can query real data through platform APIs.
Create and manage Neo4j vector indexes, run vector similarity search (ANN/kNN), store embeddings on nodes or relationships, use SEARCH clause (Neo4j 2026.01+, preferred) or db.index.vector.queryNodes() procedure (deprecated 2026.04, still works on 2025.x), configure HNSW and quantization options, pick similarity function and embedding provider dimensions, and batch-update embeddings. Use when tasks involve CREATE VECTOR INDEX, vector.dimensions, cosine/euclidean search, embedding ingestion pipelines, or semantic nearest-neighbor lookup. Does NOT handle GraphRAG retrieval_query graph traversal — use neo4j-graphrag-skill. Does NOT handle fulltext/keyword indexes (FULLTEXT INDEX, db.index.fulltext) — use neo4j-cypher-skill. Does NOT handle GDS graph embeddings (FastRP, Node2Vec) — use neo4j-gds-skill.
Design, review, and refactor Neo4j graph data models. Use when choosing node labels vs relationship types vs properties, migrating relational/document schemas to graph, detecting anti-patterns (generic labels, supernodes, missing constraints), designing intermediate nodes for n-ary relationships, enforcing schema with constraints and indexes, or assessing an existing model against graph modeling best practices. Does NOT handle Cypher query authoring — use neo4j-cypher-skill. Does NOT handle Spring Data Neo4j entity mapping — use neo4j-spring-data-skill. Does NOT handle GraphQL type definitions — use neo4j-graphql-skill. Does NOT handle data import — use neo4j-import-skill.
Extract structured data from web pages using browser automation and DOM queries
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
Earnings estimate revision analysis for listed companies via Longbridge — tracks analyst consensus revision direction (upgrade / downgrade), earnings surprise (SUE = standardised unexpected earnings), PEAD post-earnings drift signals (consecutive beats + upward revisions = positive momentum), and management guidance revision impact. Builds on raw data from longbridge-consensus. Triggers: "预期修正", "盈利修正", "分析师上调", "分析师下调", "超预期", "低于预期", "PEAD", "财报后漂移", "业绩意外", "管理层指引", "預期修正", "盈利修正", "分析師上調", "分析師下調", "超預期", "低於預期", "財報後漂移", "業績意外", "管理層指引", "earnings revision", "estimate revision", "analyst upgrade", "analyst downgrade", "beat miss surprise", "SUE", "PEAD post-earnings drift", "guidance revision", "estimate cut raise".
Post-earnings analysis skill — generates institutional-grade earnings update reports (8–12 page DOCX) and structured conversation summaries for companies under coverage. Covers beat/miss analysis, segment breakdown, margin trends, guidance assessment, updated estimates, and valuation. Supports US, HK, and A-share markets. Use this skill whenever the user wants a post-earnings analysis or quarterly-results writeup, even if they do not say "earnings update" verbatim. Triggers: "earnings update", "quarterly results", "Q1/Q2/Q3/Q4 results", "earnings report", "post-earnings analysis", "beat/miss", "guidance update", "财报分析", "业绩更新", "季度业绩", "季报", "年报", "盈利分析", "财报点评", "財報分析", "業績更新", "季度業績", "季報", "年報", "財報點評".
Fetches cryptocurrency market data, prices, technical analysis, news, and trends using the CoinMarketCap MCP. Use for ANY question involving cryptocurrencies, tokens, or blockchain markets, even if the user doesn't explicitly ask for data. This includes price checks, portfolio questions, market analysis, coin comparisons, holder metrics, technical indicators, and news. Trigger: "bitcoin", "ETH", "crypto", "token price", "market cap", "how is [coin] doing", "/cmc-mcp"
Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.
Describes how blockchain analytics platforms work in practice, typical use cases (markets, compliance, law enforcement, tax, market integrity), tool layers like visualizers and tracers, and limitations of heuristic attribution. Use when the user asks about blockchain analytics for AML, transaction monitoring, forensic tracing, institutional ops, or taint-style analysis at a high level.
Read, edit, analyze, and create Microsoft Excel files (.xlsx, .xls, .xlsm, .csv, .tsv). Use when a user asks to: (1) Open/read/inspect an Excel file, (2) Edit or modify spreadsheet data, formulas, or formatting, (3) Analyze spreadsheet data and provide insights, statistics, or trends, (4) Create new Excel files with data, formulas, charts, or formatting, (5) Convert between CSV/TSV and Excel formats, (6) Build financial models or dashboards in Excel.
Fetch web page content via Chrome DevTools Protocol (CDP). Full JS rendering, handles redirects (including Google News). Use when you need to read the text content of a web page, scrape articles, or extract information from URLs. Zero dependencies — Python 3 stdlib only. Cross-platform (Mac, Windows, Linux).