Total 54,951 skills, Data Processing has 2818 skills
Showing 12 of 2818 skills
Master SQL fundamentals including SELECT, INSERT, UPDATE, DELETE, CREATE, ALTER, DROP operations. Learn data types, WHERE clauses, ORDER BY, GROUP BY, and basic joins.
Financial analysis expertise for financial modeling (DCF, LBO, M&A), valuation, financial statement analysis, capital allocation, treasury management, and corporate finance decisions. Use when building financial models, analyzing statements, or making investment decisions.
Access US stock market data including price bars, news with sentiment, and company details via eng0 data API. Use when user asks for stock prices, OHLCV data, price history, stock news, or company information. Triggers include "stock price", "price history", "OHLCV", "stock news", "company info", "market data", "ticker data". Do NOT use for SEC filings (use sec-edgar-skill instead).
Analyze Management Discussion and Analysis (MD&A) sections from SEC filings using Octagon MCP. Use when extracting strategic initiatives, financial performance commentary, macroeconomic challenges, and forward-looking statements from 10-K and 10-Q filings.
Create publication-quality charts and graphs for economics papers.
Complete T-SQL function reference for SQL Server and Azure SQL Database. Use this skill when: (1) User asks about T-SQL string, date, math, or conversion functions, (2) User needs help with window/ranking functions, (3) User works with JSON or XML in T-SQL, (4) User asks about aggregate functions or GROUP BY, (5) User needs system or metadata functions.
Retrieve detailed revenue breakdown by geographic segment for public companies. Use when analyzing regional exposure, geographic concentration, international expansion, or currency risk assessment.
Retrieve historical market capitalization data for any stock using Octagon MCP. Use when tracking market cap changes over time, analyzing valuation trends, identifying peak and trough valuations, and comparing historical size classifications.
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
Collect app events via evalpkgs into sqlite, then filter/report capture_results to Feishu Bitable with retry-safe writeback. Use for collect-start/collect-stop/filter/report/retry-reset workflows.
Retrieve paper metadata from arXiv using keyword queries and save results as JSONL (`papers/papers_raw.jsonl`). **Trigger**: arXiv, arxiv, paper search, metadata retrieval, 文献检索, 论文检索, 拉取元数据, 离线导入. **Use when**: 需要一个初始论文集合（survey/snapshot 的 Stage C1），来源为 arXiv（在线检索或离线导入 export）。 **Skip if**: 已经有可用的 `papers/papers_raw.jsonl`，或数据源不是 arXiv。 **Network**: 在线检索需要网络；离线 `--input <export.*>` 不需要网络。 **Guardrail**: 只做 metadata；不要在 `output/` 写长 prose。
Knowledge about pyecharts chart creation, HTML report generation, and visualization best practices