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Found 13 Skills
Generate read-only MongoDB queries (find) or aggregation pipelines using natural language, with collection schema context and sample documents. Use this skill whenever the user asks to write, create, or generate MongoDB queries, wants to filter/query/aggregate data in MongoDB, asks "how do I query...", needs help with query syntax, or discusses finding/filtering/grouping MongoDB documents. Also use for translating SQL-like requests to MongoDB syntax. Does NOT handle Atlas Search ($search operator), vector/semantic search ($vectorSearch operator), fuzzy matching, autocomplete indexes, or relevance scoring - use search-and-ai for those. Does NOT analyze or optimize existing queries - use mongodb-query-optimizer for that. Does NOT handle aggregation pipelines that involve write operations. Requires MongoDB MCP server.
Search bioRxiv biology preprints with natural language queries. Semantic search powered by Valyu.
Search global patents with natural language queries. Prior art, patent landscapes, and innovation tracking via Valyu.
Search DrugBank comprehensive drug database with natural language queries. Drug mechanisms, interactions, and safety data powered by Valyu.
Search medRxiv medical preprints with natural language queries. Powered by Valyu semantic search.
Search ClinicalTrials.gov with natural language queries. Find clinical trials, enrollment, and outcomes using Valyu semantic search.
Search group Yuque knowledge bases with natural language queries and provide summarized answers with key points and source links. For group use — searches within team/group repositories. Requires group Token.
A Tushare data research skill for Chinese natural language. It converts requests like "How has this stock been performing lately?", "Help me check the financial report trend", "Which sector is the strongest recently?", "What are northbound funds buying?", "Export a market data report for me" into executable workflows for data acquisition, cleaning, comparison, filtering, export, and brief analysis. It applies to research scenarios such as A-shares, indices, ETFs/funds, finance, valuation, capital flows, announcements & news, sector concepts, and macroeconomic data.
Grafana Cloud AI and ML features — Grafana Assistant (natural language queries, dashboard generation, incident investigations), Dynamic Alerting (ML forecasting and outlier detection), Sift (automated root cause analysis with 8 analysis types), Knowledge Graph (entity discovery and RCA Workbench), and the LLM Plugin (OpenAI/Anthropic/Azure integration). Use when setting up AI-powered alerting, using natural language to query metrics/logs, automating incident investigation, or integrating LLMs with Grafana panels and workflows.
This skill should be used when searching Claude Code session transcripts with semantic understanding. Triggers on queries like "find sessions about X", "when did I work on Y", "search previous conversations". Supports natural language queries with synonym matching.
Query Chrome browsing history with natural language. Filter by date range, article type, keywords, and specific sites.
This Skill is built based on Eastmoney's authoritative database and the latest underlying market data, supporting natural language queries for market data (real-time quotes, main capital flows, valuations, etc. of stocks, industries, sectors, indices, funds, bonds), financial data (basic information of listed companies, financial indicators, executive information, main business, etc.), and relationship and operation data (associated relationships, enterprise operation data). It prevents models from answering financial data questions based on outdated knowledge and provides authoritative and timely financial data.