Total 30,714 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Plotly Chart Generator - Auto-activating skill for Visual Content. Triggers on: plotly chart generator, plotly chart generator Part of the Visual Content skill category.
Regular expression expert for crafting, debugging, and explaining patterns
Query the ExoPriors Scry API -- SQL-over-HTTPS search across 229M+ entities spanning forums, papers, social media, government records, and prediction markets. Includes cross-platform author identity resolution (actors, people, aliases), OpenAlex academic graph navigation (authors, citations, institutions, concepts), shareable artifacts, and structured agent judgements. Use when the task involves: Scry API, ExoPriors, /v1/scry/query, scry.search, scry.entities, materialized views, corpus search, epistemic infrastructure, 229M entities, lexical search, BM25, structured agent judgements, scry shares, cross-corpus analysis, who is this person, cross-platform identity, OpenAlex, citation graph, coauthor graph, academic papers, author lookup. NOT for: semantic/vector search composition or embedding algebra (use scry-vectors), LLM-based reranking (use scry-rerank), or the user's own local Postgres / non-ExoPriors data sources.
Crypto 单因子量化研究服务 Skill。当用户说"写一个因子"、"研究因子"、"量化打工"、 "提交因子"、"因子回测"时加载此 Skill。 Agent 负责编写因子插件代码并通过 HTTP 接口与服务器交互， 服务器负责所有数据处理和计算，Agent 本地无需任何市场数据。
Use when working with R ggplot2 package, especially ggplot2 4.0+ features. Covers S7 migration (@ property access), theme defaults with ink/paper/accent, element_geom(), from_theme(), theme shortcuts (theme_sub_*), palette themes, labels with dictionary/attributes, discrete scale improvements (palette, continuous.limits, minor_breaks, sec.axis), position aesthetics (nudge_x/nudge_y, order), facet_wrap dir/space/layout, boxplot/violin/label styling, stat_manual(), stat_connect(), coord reversal.
This skill should be used when the user asks to "use pandas", "analyze data with pandas", "work with DataFrames", "clean data with pandas", or needs guidance on pandas best practices, data manipulation, performance optimization, or common pandas patterns.
This skill should be used when the user asks to "validate a DataFrame with pandera", "write a pandera schema", "use pandera DataFrameModel", "add data validation to a pipeline", or needs guidance on pandera best practices for data quality.
This skill should be used when the user asks to "query BigQuery with Python", "use the google-cloud-bigquery SDK", "load data into BigQuery", "define a BigQuery schema", or needs guidance on best practices for the Python BigQuery client library.
This skill should be used when the user asks to "format JSON", "design JSON API", "write JSON response", "structure JSON data", or needs guidance on JSON naming conventions and best practices based on Google's JSON Style Guide.
Read your database schema, generate behavioral user segments with exact queries, and recommend targeted actions per segment. Use when the user wants to understand their user base, find power users, identify churn risk, build email cohorts, or understand usage patterns. Triggers on requests like "segment users", "who are my power users", "find churned users", "user cohorts", "churn analysis", "inactive users", "behavioral segmentation", "who's about to leave", or any mention of grouping users by activity, usage, or lifecycle.
Decide when and how to index Solana data vs direct RPC reads. Covers event design, backfill, storage, and performance. Use for data architecture decisions.
Data pipeline expert for ETL, Apache Spark, Airflow, dbt, and data quality