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Azure AI Transcription SDK for Python. Use for real-time and batch speech-to-text transcription with timestamps and diarization.
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Azure Event Hubs SDK for Python streaming. Use for high-throughput event ingestion, producers, consumers, and checkpointing.
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Expert patterns for Azure Functions development including isolated worker model, Durable Functions orchestration, cold start optimization, and production patterns. Covers .NET, Python, and Node.js programming models.
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Azure App Configuration SDK for Python. Use for centralized configuration management, feature flags, and dynamic settings.
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Azure Event Grid SDK for Python. Use for publishing events, handling CloudEvents, and event-driven architectures.
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Azure Tables SDK for Python (Storage and Cosmos DB). Use for NoSQL key-value storage, entity CRUD, and batch operations.
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Azure AI Content Safety SDK for Python. Use for detecting harmful content in text and images with multi-severity classification.
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Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.
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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video.
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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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Build persistent agents on Azure AI Foundry using the Microsoft Agent Framework Python SDK.
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将代码中的现有注释翻译为中文，并根据代码逻辑新增必要的中文注释，以提高代码的可读性。
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根据指定的中文标点符号列表（。？！；）将段落切分为句子，并确保切分后的句子末尾保留对应的标点符号。
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在Tkinter中为已绑定函数的按钮增加点击变色功能，要求第一次点击变绿，第二次及以后点击变红，且不破坏原有操作。
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根据样本表型数据，计算每个SNP位点上表现最好的纯合基因型，忽略杂合基因型和缺失数据，并输出结果文件。
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在对具有多层索引的Pandas DataFrame进行分组计算时，确保返回的结果保留原始的完整索引结构，而不是仅保留分组键。
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使用openpyxl遍历Excel指定列范围，根据关键词列表筛选行，并将匹配到的关键词插入到结果行的最前面。
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用于在PyQt5应用程序中管理类内部变量作用域，通过实例变量(self.variable)替代全局变量(global)，实现跨函数的数据共享、默认值初始化及条件更新。
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用于处理包含时间区间字段和字典字段的Excel表格，计算字典中键位于指定时间区间内且值为正数的平均值。
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在Backtrader中实现基于两个可能异步出现信号的交易逻辑，要求代码结构简单，能够处理信号先后出现或同时出现的情况。
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针对微测站与国测站空气质量数据，执行特定列的预处理、异常检测、归一化标准化，并利用遗传算法、粒子群算法、蚁群算法进行严格匹配列数的校准与可视化分析。
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使用Python pandas或Java POI将Excel文件中的多个Sheet合并到一个Sheet中，并新增一列记录数据来源的Sheet名称。
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Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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Guide for creating high-quality MCP (Model Context Protocol) servers that enable LLMs to interact with external services through well-designed tools. Use when building MCP servers to integrate external APIs or services, whether in Python (FastMCP) or Node/TypeScript (MCP SDK).
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