Loading...
Loading...
Found 278 Skills
Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads
Skill for BigQuery AI and Machine Learning queries using standard SQL and `AI.*` functions (preferred over dedicated tools).
Aggregated news, regulatory filings, and Longbridge-community discussion for a single stock — classified into catalyst / regulatory / strategic / financial / opinion / other, with a fact-only key-takeaway summary and a sentiment skew. Falls back to WebSearch only when data is sparse or stale, and labels the source. Triggers: "X 最近新闻", "X 公告", "市场对 X 财报怎么看", "X 社区讨论", "X 公司动态", "市场情绪", "最近怎么了", "X 最近新聞", "X 公告", "市場對 X 財報怎麼看", "X 社區討論", "recent news", "company filings", "market reaction", "what is everyone saying about X", "community sentiment", "8-K", "港交所披露", "earnings reaction".
Pull every meal you ever logged out of MyFitnessPal — per-food CSV, agent-shaped trends, and a local SQLite store. Trigger phrases: `what did I eat this week`, `export my food diary`, `find every time I logged X`, `top foods driving my protein`, `am I hitting my calorie streak`, `use myfitnesspal`, `run myfitnesspal`.
Reads AR/AP, historical cash timing, and known fixed costs from QuickBooks, PayPal, Stripe, or Square — or a CSV upload — and produces a 30/60/90-day cash flow forecast with percentage-variance confidence bands and named risk flags. Delivers a chat summary and a downloadable XLSX. Use when the user asks "forecast my cash flow," "will I make payroll," mentions "runway," or says "cash crunch." Falls back to CSV upload when no connector is live.
万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取（iter_rows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用 pd.read_excel() 导致超时或内存溢出；④用户明确要求对大规模数据集进行高性能处理。仅不用于：小于 10k 行的常规 Excel 分析（使用 sn-da-excel-workflow 即可）。
Vendor-neutral skill to analyze onboarding funnel dropoff and propose prioritized interventions.
Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.
Use when writing QGIS expressions for filtering, labeling, symbology, or field calculations. Prevents expression syntax errors and context misconfiguration. Covers QgsExpression parsing, evaluation contexts, field calculator, data-defined properties, and custom functions. Keywords: QgsExpression, expression, field calculator, label expression, data-defined, @qgsfunction, filter, evaluate, calculate field, formula, conditional label, dynamic value.
Create, edit, audit, and extract Excel spreadsheets (.xlsx): generate reports/exports, apply formulas/formatting/charts/data validation, parse existing workbooks, and avoid spreadsheet risks (formula injection, broken links, hidden rows). Supports ExcelJS, openpyxl, pandas, XlsxWriter, and SheetJS.
Sync dividend data from Fidelity CSV to Dividends sheet. Reads dividend.csv from notebooks/updates/, calculates actual dividends received (shares × amount per share), writes to input area (rows 2-46), then clicks Add Dividend button to process. Triggers on sync dividends, update dividends, dividend tracker, layer 2 income, or monthly dividend analysis.
Polars fast DataFrame library. Use for fast data processing.