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Found 323 Skills
Deep multi-platform intelligence analysis combining LinkedIn (profile, posts, activity), Twitter/X (tweets, engagement), Reddit (discussions, community), web presence (articles, GitHub, blogs), and company intelligence. Use when analyzing people for networking, sales, partnerships, or recruitment. Accepts LinkedIn URL or name+context. Produces comprehensive cross-platform reports with conversation strategies and strategic value assessment for AnySite.
Search and analyze Xiaohongshu (RED / Little Red Book) content including notes, products, and creator profiles. Use when the user asks about Xiaohongshu trends, wants to find products or reviews on RED, or needs creator/influencer data. Triggers: 小红书, xiaohongshu, RED, Little Red Book, xhs.
Data analysis, SQL queries, BigQuery operations, and data insights. Use for data analysis tasks and queries.
Analyze workforce data — attrition, engagement, diversity, and productivity. Trigger with "attrition rate", "turnover analysis", "diversity metrics", "engagement data", "retention risk", or when the user wants to understand workforce trends from HR data.
Who does this wallet transact with? Direct counterparties, entity clusters, and multi-hop BFS network trace.
Analyze stock correlations to find related companies and trading pairs. Use this skill whenever the user asks about correlated stocks, related companies, sector peers, trading pairs, or how two or more stocks move together. Triggers include: "what correlates with NVDA", "find stocks related to AMD", "correlation between AAPL and MSFT", "what moves with", "sector peers", "pair trading", "correlated stocks", "when NVDA drops what else drops", "find me a pair for", "stocks that move together", "beta to", "relative performance", "which stocks follow AMD", "supply chain partners", "correlation matrix", "co-movement", "related tickers", "sympathy plays", "if GOOGL moves what else moves", "semiconductor peers", "compare correlation", "hedging pair", "sector clustering", "realized correlation", "rolling correlation", or any request about finding stocks that move in tandem or inversely. Also triggers when the user mentions well-known pairs like AMD/NVDA, GOOGL/AVGO, LITE/COHR and wants to understand or find similar relationships. Always use this skill even if the user only provides one ticker — infer that they want to find correlated peers.
Read Discord for financial research using the discord-cli tool (read-only). Use this skill whenever the user wants to read Discord channels, search for messages in trading servers, view guild/channel info, monitor crypto or market discussion groups, or gather financial sentiment from Discord. Triggers include: "check my Discord", "search Discord for", "read Discord messages", "what's happening in the trading Discord", "show Discord channels", "list my servers", "Discord sentiment on BTC", "what are people saying in Discord about AAPL", "monitor crypto Discord", "export Discord messages", any mention of Discord in context of reading financial news, market research, or trading community discussions. This skill is READ-ONLY — it does NOT support sending messages, reacting, or any write operations.
Is this token held by quality wallets or retail noise? SM holder ratio, flow breakdown by label, and recent buyer quality.
Amazon Athena integration. Manage data, records, and automate workflows. Use when the user wants to interact with Amazon Athena data.
Calculate price elasticity of demand to quantify how price changes affect sales volume. Use this skill when the user needs to estimate demand sensitivity, set optimal prices, or evaluate the revenue impact of price changes — even if they say 'how sensitive are customers to price', 'will a price increase hurt sales', or 'elasticity calculation'.
This skill guides the use of Jupyter notebooks for data analysis, exploration, and visualization, particularly with BigQuery. It outlines best practices for notebook execution and validation (supporting both cell-by-cell execution and full notebook generation depending on tool availability), library installation, and structuring notebooks for clarity. It also covers specific rules for data cleaning, plotting, and integrating with BigQuery SQL and machine learning workflows. Relevant when any of the following conditions are true: 1. The user request involves a data analysis, data exploration, data visualization, or data insights task that requires multiple steps, queries, or visualizations to answer. 2. The user explicitly requests a notebook (.ipynb). 3. You are creating, editing, or executing cells in a Jupyter notebook. 4. You need to query BigQuery from within a notebook. DO NOT use the Python BigQuery client library; instead, you MUST use the `%%bqsql` magics explained in this skill.
Detect and classify candlestick patterns from ingested OHLCV data