--- 格式版本: 2 标题: "Brainstorm plugin use cases – Plugins | OpenAI Developers" 原文链接: "https://developers.openai.com/plugins/plan/use-case" 发布日期: "2026-09-04" 发布时间校准状态: "found" 发布时间需复核: "否" 发布时间来源: "rule:local:strict_original_body" 发布时间证据: "Published Time: Fri, 04 Sep 2026 04:42:43 GMT" 发布时间校准原因: "规则确认唯一严格发布时间，来源 local:strict_original_body" 发布时间校准置信度: "high" 发布时间候选数量: 4 发布时间严格候选数量: 1 发布时间原页读取状态: "source template page reused from URL open" 发布时间未找到原因: "" 发布时间校准时间: "2026-09-04T16:28:58+08:00" 发布时间仲裁状态: "skipped" 发布时间仲裁尝试次数: 0 发布时间仲裁耗时毫秒: 0 发现时间: "2026-09-04T16:24:00+08:00" 入库时间: "2026-09-04T08:28:58.799Z" 来源平台: "固定入口" 搜索渠道: "fixed_url" 搜索词: "https://developers.openai.com/blog" 匹配关键词: - "deployment" - "performance" - "latency" - "throughput" - "AI" 相关厂家: - "OpenAI" - "Microsoft" - "AWS" - "Google" - "Oracle" 相关专家: [] 内容类型: "网页" 抓取工具: "Jina Reader" 清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取" 原始附件: [] AI优质: "否" AI打分: 16 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "正文为OpenAI官方开发者文档页面，主题是如何规划ChatGPT/Codex插件用例，完全未涉及超节点、AI Rack、机架级系统、关键部件或相关部署。虽来源权威（OpenAI官方），但主题不相关、无新增技术情报、无商业部署信号，命中‘教程与基础科普’否决项，不进入业务情报库。" AI质检模型: "zj-deepseek-v4-flash" AI质检时间: "2026-09-04T16:39:34+08:00" AI主题相关性: 0 AI来源权威性: 10 AI新颖性: 0 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 6 AI评分提示词版本: "v17-精简生产版" AI评分提示词SHA256: "48fb9777f386026761b4873eaff30807694fb11e9b352d7c69bf2dfde750cc7d" AI评分知识库版本: "knowledge_base_v1-20260819+runtime.87" AI评分知识库SHA256: "0cf9900cb3e9d03b3f4c39f45979aac6e2d5c789a68d1b2802c9dab62cd2710e" AI评分知识库检索词: "[\"OpenAI\",\"https://developers.openai.com/blog\",\"RAS\",\"NPU\",\"Google\",\"Meta\",\"Microsoft\",\"URL\",\"GMT\",\"API\",\"GPT-6\",\"SDKs\"]" AI评分知识库命中: "[{\"id\":\"july-correct-0033\",\"title\":\"Microsoft, Alphabet, Meta Pivot from Buy to Build in AI\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Google\",\"Meta\",\"Microsoft\",\"URL\",\"API\"],\"rank\":-18.229700393321},{\"id\":\"july-correct-0001\",\"title\":\"全球首颗2nm GPU来了！苏姿丰甩出“最强AI机架”，CPU性能干翻英伟达 - 智东西\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"NPU\",\"Meta\"],\"rank\":-12.752907321262027},{\"id\":\"runtime-51cfe3db04ed0bd3efb5e0e3\",\"title\":\"The full stack behind abundant intelligence\",\"sourceType\":\"ai_excellent_article\",\"time\":\"2026-08-25\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Microsoft\",\"URL\",\"API\"],\"rank\":-12.679167019868977},{\"id\":\"july-correct-0018\",\"title\":\"AMD, Cerebras partner on joint Helios rack-scale AI inference platform\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"RAS\",\"Meta\",\"Microsoft\",\"URL\"],\"rank\":-12.180948954382538},{\"id\":\"july-correct-0015\",\"title\":\"AMD to join the optical interconnect party with 2027 Instinct GPUs\",\"sourceType\":\"labeled_article\",\"time\":\"2026-07\",\"matchedTerms\":[\"OpenAI\",\"Meta\",\"Microsoft\",\"URL\"],\"rank\":-12.059703544606952}]" 采集批次: "2026年9月4日15点53分07秒" 采集批次ID: "20260904-155307-58ad1755" 去重键: "https://developers.openai.com/plugins/plan/use-case" --- Title: Brainstorm plugin use cases – Plugins | OpenAI Developers URL Source: https://developers.openai.com/plugins/plan/use-case Published Time: Fri, 04 Sep 2026 04:42:43 GMT Markdown Content: For the complete documentation index, see [llms.txt](https://developers.openai.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to the page URL. 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Copy Page Start by listing the things people will expect your plugin to do. The plugin’s name, description, skills, tools, and connection to an existing product all create expectations. Your implementation should cover those expectations or have a deliberate reason not to. This work determines what belongs in the plugin: * Add a **skill** when instructions, examples, or bundled resources can guide the model through the workflow. * Add an **MCP server** when the workflow needs live data, authentication, controlled tools, or code that runs on infrastructure you operate. * Add **UI to the MCP server** only when visual interaction materially improves part of the workflow. ## Start from user expectations Imagine that a person has installed your plugin but has not read its documentation. What would they reasonably ask it to do? Gather likely requests from: * Tasks people already complete in your product or service. * User interviews, support requests, search queries, and feature requests. * Common terms people use for your product, data, and workflows. * Existing workarounds that require copying data between tools. * The plugin name, listing, screenshots, and starter prompts. Include direct requests that name your plugin and indirect requests that state the goal. For example, a project-management plugin might need to handle both “Show my Acme launch board” and “What is blocking the launch?” Do not limit the brainstorm to workflows that fit your current API. First capture what people will expect. Then compare those expectations with what you can support safely and reliably. ## Build a use-case inventory For each use case, record: | Field | Question to answer | | --- | --- | | User goal | What is the person trying to accomplish? | | Example requests | How might they ask directly or indirectly? | | Expected result | What would make the interaction successful? | | Required context | What information, account access, or prior state is needed? | | Plugin capability | Can a skill handle it, or does it need an MCP tool? | | Safety boundary | Could it expose data, change state, spend money, or affect another person? | | Support decision | Will the first version support it, defer it, or intentionally exclude it? | Group requests that share the same goal. “List my open tasks,” “What do I need to do today?” and “Show overdue work” may belong to one task-review use case with different filters rather than three unrelated features. ## Check coverage Review every expectation against the proposed plugin capabilities: 1. Confirm that each supported use case has a complete path from request to useful result. 2. Identify missing skills, tools, data, permissions, or error states. 3. Look for tools that expose technical operations without completing a recognizable user goal. 4. Verify that write actions include appropriate authorization and confirmation. 5. Check that the plugin can explain what it cannot do and offer a useful next step. A plugin should not imply broad capability while supporting only a narrow slice of the expected workflow. If users can create projects but cannot list, inspect, or update them, either add the missing coverage or narrow the plugin’s positioning. ## Document intentional exclusions You do not need to implement every imaginable request. You should have a good reason for each important exclusion, such as: * The action would create unacceptable safety or privacy risk. * The underlying product or API does not support it reliably. * The workflow requires permissions that the plugin cannot verify. * The result would be misleading without information the plugin cannot access. * The use case is out of scope for the first release and the plugin’s listing sets that expectation. Record these decisions. They should inform skill boundaries, tool descriptions, refusal behavior, test cases, and public listing copy. ## Turn use cases into build decisions For each supported use case, choose the smallest implementation that can complete it: * [Build a skill](https://developers.openai.com/plugins/build/skills) for repeatable instructions and resources. * [Build an MCP server](https://developers.openai.com/plugins/build/mcp-server) for live data and controlled actions. * [Add UI to the MCP server](https://developers.openai.com/plugins/build/chatgpt-ui) when people need to inspect, compare, edit, confirm, or navigate structured information. Keep the use-case inventory as a test plan. Add representative direct, indirect, edge-case, and out-of-scope requests, then verify that the finished plugin behaves as intended for each one. If the plugin needs live data or controlled actions, continue with [Define tools](https://developers.openai.com/plugins/plan/tools). 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