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Found 32 Skills
Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching.
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.
Extract contact information from business card images using OCR - name, company, email, phone, address.
Export agent data into a Starchild migration bundle. For use by ANY agent (OpenClaw, Claude Code, Cursor, etc.) to migrate into Starchild.
Track cryptocurrency portfolio with real-time valuations, allocation analysis, and P&L tracking. Use when checking portfolio value, viewing holdings breakdown, analyzing allocations, or exporting portfolio data. Trigger with phrases like "show my portfolio", "check crypto holdings", "portfolio allocation", "track my crypto", or "export portfolio".
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".
Download workflow run results, export segment data, and monitor run metrics using the Cargo CLI. Use when the user wants run metrics, error rates, data export, or download results for their Cargo workspace. For billing and credit usage, use the cargo-billing skill instead.
微信公众号文章抓取与导出。自动处理 mp.weixin.qq.com 的登录态获取与续期， 支持按公众号搜索、抓取文章列表与正文、按日期窗口导出 Markdown / JSON / CSV。 Trigger when the user wants to crawl a WeChat public account, export recent articles, or 提到 "wcx"、"微信公众号"、"公众号文章"、"mp.weixin"、"抓公众号"、 "crawl wechat official account"、"wxmp"、"最近十天的文章"。
在Node.js中读取、操作和写入Excel电子表格（XLSX）。完全支持样式、公式、图表和大文件流式处理。
Use this skill when the user uploads Excel (.xlsx/.xls) or CSV files and wants to perform data analysis, generate statistics, create summaries, pivot tables, SQL queries, or any form of structured data exploration. Supports multi-sheet Excel workbooks, aggregation, filtering, joins, and exporting results to CSV/JSON/Markdown.
Interact with Excel files (.xlsx, .xlsm, .xlsb, .xls, .ods) using the agent-xlsx CLI for data extraction, analysis, writing, formatting, visual capture, VBA analysis, and sheet management. Use when the user asks to: (1) Read, analyse, or search data in spreadsheets, (2) Write values or formulas to cells, (3) Inspect formatting, formulas, charts, or metadata, (4) Take screenshots or visual captures of sheets, (5) Export sheets to CSV/JSON/Markdown, (6) Manage sheets (create, rename, delete, copy, hide), (7) Analyse or execute VBA macros, (8) List/export embedded objects (charts, shapes, pictures), (9) Check for formula errors, or (10) Any task involving Excel file interaction. Prefer over openpyxl/pandas scripts — faster, structured JSON optimised for AI.
Fetch, organize, and analyze LangSmith traces for debugging and evaluation. Use when you need to: query traces/runs by project, metadata, status, or time window; download traces to JSON; organize outcomes into passed/failed/error buckets; analyze token/message/tool-call patterns; compare passed vs failed behavior; or investigate benchmark and production failures.