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Found 24 Skills
飞书电子表格：创建和操作电子表格。创建表格并写入表头和数据、读取和写入单元格、追加行数据、在已知电子表格中查找单元格内容、导出表格文件。当用户需要创建电子表格、批量读写数据、在已知表格中查找内容、导出或下载表格时使用。若用户是想按名称或关键词搜索云空间里的表格文件，请改用 lark-doc 的 docs +search 先定位资源。
Filter Etsy stores through multiple dimensions (sales volume, favorites, reviews, store opening time, country, main category, Raving/star label, etc.) via the EHunt MCP tool `_ehunt_storeQuery` (display name: "Etsy Store Query"). Trigger this skill when users mention EHunt Etsy stores, Etsy store search, Etsy seller, Etsy store rankings, Etsy weekly sales stores, ehunt stores, Etsy store query, or _ehunt_storeQuery. Even if users don't mention EHunt, this skill should be triggered as long as they are looking for stores on Etsy, filtering store data, or analyzing store performance.
通过 EHunt Temu 店铺查询（网关路由 `ehunt/temu/storeQuery`）按多维度筛选 Temu 店铺（店名/ID、国家站点、后台类目、全托管/半托管、总/周/月销量与销售额、评分、评论、粉丝、商品数、开店时间等）。当用户提到 EHunt Temu 店铺、Temu 店铺分析、Temu seller、Temu 店铺排行、Temu 半托管店铺、Temu 销售额、temu stores、Temu store query 时触发。即使用户未写 EHunt，只要在 Temu 上找店铺、筛店铺数据或分析店铺表现，也应触发此技能。
通过 EHunt Shopify 店铺查询（网关路由 `ehunt/shopify/storeQuery`）按多维度筛选独立站 Shopify 店铺（店名/域名、国家、创建年限、产品数、广告数、月访问量、月订单量、社媒粉丝等）。当用户提到 EHunt Shopify 店铺、Shopify 店铺分析、独立站店铺、Shopify seller、独立站竞品店铺、Shopify 月访问量、独立站广告库、shopify stores、Shopify store query 时触发。即使用户未写 EHunt，只要在 Shopify 独立站上找店铺、筛店铺数据或分析店铺表现，也应触发此技能。
Use this skill to find high-quality dividend growth stocks (12%+ annual dividend growth, 1.5%+ yield) that are experiencing temporary pullbacks, identified by RSI oversold conditions (RSI ≤40). This skill combines fundamental dividend analysis with technical timing indicators to identify buying opportunities in strong dividend growers during short-term weakness.
Process Excel files, supporting reading, analysis, statistics and export of xlsx data
Generate synthetic training data when you don't have enough real examples. Use when you're starting from scratch with no data, need a proof of concept fast, have too few examples for optimization, can't use real customer data for privacy or compliance, need to fill gaps in edge cases, have unbalanced categories, added new categories, or changed your schema. Covers DSPy synthetic data generation, quality filtering, and bootstrapping from zero.
Query Google Analytics 4 (GA4) data via the Analytics Data API. Use when you need to pull website analytics like top pages, traffic sources, user counts, sessions, conversions, or any GA4 metrics/dimensions. Supports custom date ranges and filtering.
Search for financial reports using Exa advanced search. Near-full filter support for finding SEC filings, earnings reports, and financial documents. Use when searching for 10-K filings, quarterly earnings, or annual reports.
Build semantic search with Cloudflare Vectorize V2 (Sept 2024 GA). Covers V2 breaking changes: async mutations, 5M vectors/index (was 200K), 31ms latency (was 549ms), returnMetadata enum, and V1 deprecation (Dec 2024). Use when: migrating V1→V2, handling async mutations with mutationId, creating metadata indexes before insert, or troubleshooting "returnMetadata must be 'all'", V2 timing issues, metadata index errors, dimension mismatches.
Write and run AQL (Analytic Query Language) queries to answer data questions. Use this whenever the user asks for data, wants to query a dataset, needs to filter/aggregate/join data, or asks about metrics and dimensions in Holistics.
Guidance for counting tokens in datasets, particularly from HuggingFace or similar sources. This skill should be used when tasks involve counting tokens in datasets, understanding dataset schemas, filtering by categories/domains, or working with tokenizers. It helps avoid common pitfalls like incomplete field identification and ambiguous terminology interpretation.