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Integrate PocketBase as a Go library using the github.com/castle-x/goutils/pocketbase (gopb) package to build single-binary full-stack applications. Use when building Go applications that need user authentication, embedding PocketBase into Go binary, registering custom API routes, managing default users, serving embedded SPA frontend, or deploying single-binary applications. NOT for using PocketBase as a standalone separate process.
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Framework for building LLM-powered applications with agents, chains, and RAG. Supports multiple providers (OpenAI, Anthropic, Google), 500+ integrations, ReAct agents, tool calling, memory management, and vector store retrieval. Use for building chatbots, question-answering systems, autonomous agents, or RAG applications. Best for rapid prototyping and production deployments.
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Lay out how a job search will actually run alongside a current job: the hours available each week and what they go to, how weekday daytime slots for interviews will be found, which routes to use (direct applications, job boards, scout sites, agents, referrals, public employment services) and what each one discloses to whom, how to keep the search from reaching the current employer through public profiles or workplace equipment, a single tracker of every application's stage, next action, and deadline, and a date and conditions for reviewing, reducing, or pausing the search. Use when someone has decided to start looking, or to gather information while employed, and asks how to fit it in, which routes to use, whether the applications they are running fit the time they have, or how to keep track of them; do not use to decide whether to change jobs, to pick or rate employers or job postings, to predict outcomes, or to send applications, publish profiles, or contact agents on the user's behalf.
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Build real-time, reactive backend applications with Convex using TypeScript queries, mutations, and actions with automatic reactivity and optimistic updates. Use when building real-time collaborative applications, implementing reactive data synchronization, writing serverless backend functions, creating queries that auto-update, implementing mutations with transactional guarantees, handling file uploads with Convex storage, implementing authentication with Convex Auth, designing reactive database schemas, or building applications requiring instant data synchronization.
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协助编写和审查Python代码，整理PEP 8规范、类型提示、异常处理与包结构，并从可读性、可维护性和内存效率方面改进实现。
帮助编写、审查及改造Perl 5.36及以上版本的代码，提供函数参数定义、异常处理、类设计、文件操作、模块组织与依赖管理示例。
根据网站或业务界面的用途与用户确定设计方向，调整字体、配色、布局和动效，优先使用现有组件，并检查不同屏幕宽度下的显示效果。
帮助医疗应用分类患者、员工及财务敏感信息，设计按医疗机构划分的行级访问控制和审计记录，并检查日志、网址及浏览器存储中的信息泄露。
将应用开发分配给规划、实现和评估代理，检查运行中的界面并按质量标准评分，把具体问题反馈给实现环节，反复修改直至达标或达到次数上限。
按Go语言惯例协助编写、审查和重构代码，整理错误处理、并发任务、接口与包结构，并检查内存使用，帮助形成便于阅读和维护的实现。
Write, review, and refactor C# and .NET code using practical conventions for asynchronous work, configuration, and dependency injection—passing services into classes. Includes examples for data access and APIs to support maintainable applications.
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为Web应用上线整理部署流程与检查项，涵盖CI/CD测试和部署自动化、Docker容器化、健康检查及回滚计划，帮助选择发布方式并检查上线准备情况。
从指令、记忆、工具执行和界面显示等十二个层面排查人工智能代理故障，依据代码和日志梳理原因，按严重程度列出问题并提出修复方案。
通过Playwright操作本地Web应用，检查界面功能和交互行为。支持获取截图、查看浏览器日志来排查问题，并管理测试所需服务器的启动与停止。
实现多个AgentDB数据库的同步与管理，将语义相似度搜索与属性筛选结合，并配置搜索结果去重及分布式AI系统的运行方式。
实现多个AgentDB数据库的同步与管理，将语义相似度搜索与属性筛选结合，并配置搜索结果去重及分布式AI系统的运行方式。
Use Codex to build Python command-line tools for applications with graphical interfaces. Analyze source code, add commands, test against the actual software, validate packaging and documentation, and discover tools already generated or installed.
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Build command-line interfaces for graphical applications in Cursor using the CLI-Anything workflow. It supports creating and extending these interfaces, running tests, checking required criteria, and listing tools through slash commands or natural-language requests.
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Build real-time voice applications in Python with Azure AI Voice Live. Covers two-way audio streaming, transcripts, conversation history, and handling interruptions, with examples for authentication and configuring when the assistant responds.
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Build Java applications that extract text, tables, and fields from documents using Azure AI Document Intelligence. Covers ready-made models for invoices and receipts, custom model creation, document classification, authentication, and error handling.
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Build text translation into Python applications with Azure AI Translator. Covers translation into multiple languages, language detection, transliteration to another writing system, dictionary entries, usage examples, and asynchronous requests for handling larger workloads.
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Build Java applications that store and manage records in Azure Table Storage or Cosmos DB Table API. It guides creating tables, adding and updating records, filtering queries, and grouping changes into transactions that succeed or fail together.
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Build Java applications that detect unusual patterns in time-series data with Azure AI Anomaly Detector. Covers single measurements, related signals, trend changes, model training, and checking detection results.
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Build Python AI applications on Microsoft Foundry with azure-ai-projects. Provides examples for creating agents, connecting tools, managing conversations and project resources, and running evaluations through Foundry and OpenAI-compatible clients.
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