Total 30,737 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Data acquisition for web scraping and data collection. Use when user needs "爬取数据/抓取网页/scrape data". Outputs structured JSON/CSV for analysis.
Columnar file patterns including partitioning, predicate pushdown, and schema evolution.
Adds visual descriptions to transcripts by extracting and analyzing video frames with ffmpeg. Creates visual transcript with periodic visual descriptions of the video clip. Use when all files have audio transcripts present (transcript) but don't yet have visual transcripts created (visual_transcript).
Comprehensive portfolio analysis using Alpaca MCP Server integration to fetch holdings and positions, then analyze asset allocation, risk metrics, individual stock positions, diversification, and generate rebalancing recommendations. Use when user requests portfolio review, position analysis, risk assessment, performance evaluation, or rebalancing suggestions for their brokerage account.
Analyze China's macroeconomic data, PBOC monetary policy, fiscal policy, and their impact on A-share and Hong Kong stock markets. Track GDP, CPI, PPI, PMI, social financing, and interpret policy signals. Apply this when users inquire about China's macroeconomy, PBOC policies, the impact of economic data on markets, or need to understand policy implications for investment.
Strategic guidance for designing modern data platforms, covering storage paradigms (data lake, warehouse, lakehouse), modeling approaches (dimensional, normalized, data vault, wide tables), data mesh principles, and medallion architecture patterns. Use when architecting data platforms, choosing between centralized vs decentralized patterns, selecting table formats (Iceberg, Delta Lake), or designing data governance frameworks.
Fetch journal articles from Crossref published after a user-specified date and insert them into PostgreSQL `journals` with DOI deduplication. Use when incrementally ingesting journal metadata from `journals_issn` into `journals`.
Automatic CSV Data Analysis and Insight Generation Tool
Create professional financial charts and visualizations using Python/Plotly. Use when building Sankey diagrams (income statement flows, revenue breakdowns), waterfall charts (profit walkdowns, revenue bridges), bar charts (margin comparisons, segment breakdowns), or line charts (trend analysis, multi-company comparisons). Triggers on chart creation requests, financial visualization needs, or data presentation tasks.
Formal Design of Experiments (DOE) methodology for maximizing information from experiments while minimizing resources. Covers factorial designs, blocking, randomization, and optimal design strategies. Use when ", " mentioned.
Best practices for Open Finance data retrieval and management. Use when working with accounts, transactions, investments, loans, or identity data.
Stakeholder-ready summary document for any Intelligems A/B test. Combines verdict, financial impact, segment analysis, and recommendations into a single shareable brief.