Total 55,561 skills, Data Processing has 2840 skills
Showing 12 of 2840 skills
Use when defining field mappings for data streams, populating ecs.yml with ECS field references, selecting ECS categorization values, choosing custom field types, or troubleshooting mapping validation failures.
API gratuita de la Reserva Federal (FRED): 840K+ series macroeconómicas (GDP, CPI, tasas, empleo, M2, VIX, treasuries).
Financial statements, business segments, dividends, valuation multiples (PE/PB/PS), industry comparison, operating data, corporate actions, company and executive profiles, cross-stock comparison, and valuation ranking via Longbridge. Also: DCF models, value investing screens (low PE/PB, margin of safety), and behavioral finance analysis frameworks. Triggers: "财报", "三表", "利润表", "资产负债", "现金流", "估值", "PE", "PB", "分红", "公司信息", "高管", "行业估值", "并购", "DCF", "内在价值", "低估值", "安全边际", "行为金融", "小盘成长", "专精特新", "財報", "估值", "分紅", "內在價值", "安全邊際", "financial report", "income statement", "balance sheet", "valuation", "dividend", "company info", "industry valuation", "DCF", "value screen", "behavioral finance", "利潤表", "資產負債", "現金流", "行業估值", "併購", "行為金融", "小盤成長"
Query and analyze business data in NocoBase via MCP. Use when users want current counts, grouped breakdowns, owner/source distributions, or business summaries across collections, with main data source first and fallback discovery to other enabled data sources.
Search and use visualizations that already exist in the project to provide fast and curated data answers.
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
When the user wants to optimize inventory levels, calculate safety stock, determine reorder points, or minimize inventory costs. Also use when the user mentions "inventory management," "safety stock," "EOQ," "reorder point," "service level," "stockout prevention," "ABC analysis," "inventory turns," or "working capital reduction." For warehouse slotting, see warehouse-slotting-optimization. For multi-echelon systems, see multi-echelon-inventory.
Web scraping via progressive escalation and social media platform scrapers. USE WHEN scraping, crawl, scrape URL, bot detection, CAPTCHA, spider.
Confluent integration. Manage data, records, and automate workflows. Use when the user wants to interact with Confluent data.
Read-only: cross-references inventory levels with order velocity to flag items with positive stock but zero sales in N days.
Design and manage reference data systems — security master, client master, account master, identifier mapping, pricing data, and governance. Use when building or evaluating a security master database, mapping identifiers across systems (CUSIP to ISIN, SEDOL to FIGI), designing client master models for onboarding or KYC, defining account master attributes across custodians, implementing pricing validation with vendor hierarchy, establishing reference data governance and stewardship, handling identifier changes from corporate actions, or troubleshooting data quality issues traced to stale prices or missing identifiers. Trigger on: security master, CUSIP, ISIN, SEDOL, FIGI, client master, account master, pricing data, reference data, golden source, MDM, master data, identifier mapping, data governance, pricing validation.
Implement LDA topic modeling to discover latent topics in document collections. Use this skill when the user needs to extract topics from a text corpus, categorize documents by theme, or explore thematic structure — even if they say 'what are the main topics', 'topic extraction', or 'document clustering by theme'.