Data analysis, machine learning, LLM apps, and prompt design (9.5K skills)
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
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Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching.
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Apple FoundationModels framework for on-device LLM — text generation, guided generation with @Generable, tool calling, and snapshot streaming in iOS 26+.
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Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns.
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Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
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Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.
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Design, audit, and improve analytics tracking systems that produce reliable, decision-ready data.
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AI-driven data extraction from 55+ Actors across all major platforms. This skill automatically selects the best Actor for your task.
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Track engagement metrics, measure campaign ROI, and analyze content performance across Instagram, Facebook, YouTube, and TikTok.
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Forensic root cause analyzer for Antigravity sessions. Classifies scope deltas, rework patterns, root causes, hotspots, and auto-improves prompts/health.
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Extract product data, prices, reviews, and seller information from any e-commerce platform using Apify's E-commerce Scraping Tool.
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Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.
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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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Fetch any X/Twitter post as clean LLM-friendly JSON. Converts x.com, twitter.com, or adhx.com links into structured data with full article content, author info, and engagement metrics. No scraping or browser required.
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This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration.
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Build production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations.
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根据指定的行切片规则和列映射，将源DataFrame（df_sub）的列数据转换为长格式DataFrame，包含sub_id、trial、start_time、end_time等字段。
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基于历史数据利用统计分析方法预测下一组数字，严格遵守前5位(1-35)和后2位(1-12)的范围限制，输出简洁的预测结果。
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在PostgreSQL中格式化数字显示，去除小数点后的尾随零，并在小数点后无数字时去除小数点。
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用于生成MySQL应付账款统计查询，按到期时间（当前、1个月内、3个月内）分组统计金额，并确保时间范围互不重叠。
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依据用户提供的特定规则执行MySQL数据库开发任务，涵盖命名约束、索引设计策略及海量数据归档方案。
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编写MySQL函数，根据指定的日期范围和字段，按自然周汇总数据并返回特定格式的字符串结果。
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使用Fluentd的regexp插件解析MySQL慢查询日志，将其切分为时间、用户、查询时间和SQL内容四个字段，并配置字段长度限制以避免解析错误。
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