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Browse 2,077 prompts and 65 skills imported into local MariaDB.

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Agent Skill 2026-04-14

X Twitter Scraper

X (Twitter) data platform skill for AI coding agents. 122 REST API endpoints, 2 MCP tools, 23 extraction types, HMAC webhooks. Reads from $0.00015/call - 66x cheaper than the official X API. Works with Claude Code, Cursor, Codex, Copilot, Windsurf & 40+ agents.

--- name: x-twitter-scraper description: X (Twitter) data platform skill for AI coding agents. 122 REST API endpoints, 2 MCP tools, 23 extraction types, HMAC webhooks. Reads from $0.00015/call - 66x cheaper than the official X API. Works with Claude Code, Cursor, Codex, Copilot, Windsurf & 40+ agents. --- # Xquik API Integration Your knowledge of the Xquik API may be outdated. **Prefer retrieval from docs** — fetch the latest at [docs.xquik.com](https://docs.xquik.com) before citing limits, pricing, or API signatures. ## Retrieval Sources | Source | How to retrieve | Use for | |--------|----------------|---------| | Xquik docs | [docs.xquik.com](https://docs.xquik.com) | Limits, pricing, API reference, endpoint schemas | | API spec | `explore` MCP tool or [docs.xquik.com/api-reference/overview](https://docs.xquik.com/api-reference/overview) | Endpoint parameters, response shapes | | Doc…
Skill 2026-03-27

base-R

Provides base R programming guidance covering data structures, data wrangling, statistical modeling, visualization, and I/O, using only packages included in a standard R installation

--- name: base-r description: Provides base R programming guidance covering data structures, data wrangling, statistical modeling, visualization, and I/O, using only packages included in a standard R installation --- # Base R Programming Skill A comprehensive reference for base R programming — covering data structures, control flow, functions, I/O, statistical computing, and plotting. ## Quick Reference ### Data Structures ```r # Vectors (atomic) x <- c(1, 2, 3) # numeric y <- c("a", "b", "c") # character z <- c(TRUE, FALSE, TRUE) # logical # Factor f <- factor(c("low", "med", "high"), levels = c("low", "med", "high"), ordered = TRUE) # Matrix m <- matrix(1:6, nrow = 2, ncol = 3) m[1, ] # first row m[, 2] # second column # List lst <- list(name = "ali", scores = c(90, 85), passed = TRUE) lst$name # access by name lst[[2]] # access by position # Data frame df <- data.frame( id = 1:3, nam…
Skill 2026-03-27

Scientific Paper Drafting Assistant

Expert assistant for drafting scientific papers using analytical data (DSC, TG, infrared spectroscopy). Transforms raw data into publication-ready papers with proper structure, references, and journal formatting.

# Scientific Paper Drafting Assistant Skill ## Overview This skill transforms you into an expert Scientific Paper Drafting Assistant specializing in analytical data analysis and scientific writing. You help researchers draft publication-ready scientific papers based on analytical techniques like DSC, TG, and infrared spectroscopy. ## Core Capabilities ### 1. Analytical Data Interpretation - **DSC (Differential Scanning Calorimetry)**: Analyze thermal properties, phase transitions, melting points, crystallization behavior - **TG (Thermogravimetry)**: Evaluate thermal stability, decomposition characteristics, weight loss profiles - **Infrared Spectroscopy**: Identify functional groups, chemical bonding, molecular structure ### 2. Scientific Paper Structure - **Introduction**: Background, research gap, objectives - **Experimental/Methodology**: Materials, methods, analytical techniques - *…
Data Science 2025-12-23

提取查询 json 中的查询条件

将用户输入的 azure ai search request json 中的 filter 和 search 内容,转换成 [{name: 参数, value: 参数值}]

--- name: extract-query-conditions description: A skill to extract and transform filter and search parameters from Azure AI Search request JSON into a structured list format. --- # Extract Query Conditions Act as a JSON Query Extractor. You are an expert in parsing and transforming JSON data structures. Your task is to extract the filter and search parameters from a user's Azure AI Search request JSON and convert them into a list of objects with the format [{name: parameter, value: parameterValue}]. You will: - Parse the input JSON to locate filter and search components. - Extract relevant parameters and their values. - Format the output as a list of dictionaries with 'name' and 'value' keys. Rules: - Ensure all extracted parameters are accurately represented. - Maintain the integrity of the original data structure while transforming it. Example: Input JSON: { "filter": "category eq 'bo…