# hybrid-browser-toolkit-ts - Doramagic AI Context Pack

> 定位：安装前体验与判断资产。它帮助宿主 AI 有一个好的开始，但不代表已经安装、执行或验证目标项目。

## 充分原则

- **充分原则，不是压缩原则**：AI Context Pack 应该充分到让宿主 AI 在开工前理解项目价值、能力边界、使用入口、风险和证据来源；它可以分层组织，但不以最短摘要为目标。
- **压缩策略**：只压缩噪声和重复内容，不压缩会影响判断和开工质量的上下文。

## 给宿主 AI 的使用方式

你正在读取 Doramagic 为 hybrid-browser-toolkit-ts 编译的 AI Context Pack。请把它当作开工前上下文：帮助用户理解适合谁、能做什么、如何开始、哪些必须安装后验证、风险在哪里。不要声称你已经安装、运行或执行了目标项目。

## Claim 消费规则

- **事实来源**：Repo Evidence + Claim/Evidence Graph；Human Wiki 只提供显著性、术语和叙事结构。
- **事实最低状态**：`supported`
- `supported`：可以作为项目事实使用，但回答中必须引用 claim_id 和证据路径。
- `weak`：只能作为低置信度线索，必须要求用户继续核实。
- `inferred`：只能用于风险提示或待确认问题，不能包装成项目事实。
- `unverified`：不得作为事实使用，应明确说证据不足。
- `contradicted`：必须展示冲突来源，不得替用户强行选择一个版本。

## 它最适合谁

- **AI 研究者或研究型 Agent 构建者**：README 明确围绕研究、实验或论文工作流展开。 证据：`README.md` Claim：`clm_0004` supported 0.86
- **希望把专业流程带进宿主 AI 的用户**：仓库包含 Skill 文档。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md` 等 Claim：`clm_0005` supported 0.86

## 它能做什么

- **AI Skill / Agent 指令资产库**（可做安装前预览）：项目包含可被宿主 AI 读取的 Skill 或 Agent 指令文件，可用于把专业流程带入 Claude、Codex、Cursor 等宿主。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md` 等 Claim：`clm_0001` supported 0.86
- **多宿主安装与分发**（需要安装后验证）：项目包含插件或 marketplace 配置，说明它面向一个或多个 AI 宿主的安装和分发。 证据：`camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json`, `camel/toolkits/open_api_specs/web_scraper/ai-plugin.json` Claim：`clm_0002` unverified 0.25
- **命令行启动或安装流程**（需要安装后验证）：项目文档中存在可执行命令，真实使用需要在本地或宿主环境中运行这些命令。 证据：`README.md` Claim：`clm_0003` supported 0.86

## 怎么开始

- `pip install camel-ai` 证据：`README.md` Claim：`clm_0006` supported 0.86, `clm_0007` supported 0.86
- `pip install 'camel-ai[web_tools]'` 证据：`README.md` Claim：`clm_0007` supported 0.86

## 继续前判断卡

- **当前建议**：仅建议沙盒试装
- **为什么**：项目存在安装命令、宿主配置或本地写入线索，不建议直接进入主力环境，应先在隔离环境试装。

### 30 秒判断

- **现在怎么做**：仅建议沙盒试装
- **最小安全下一步**：先跑 Prompt Preview；若仍要安装，只在隔离环境试装
- **先别相信**：真实输出质量不能在安装前相信。
- **继续会触碰**：命令执行、宿主 AI 配置、本地环境或项目文件

### 现在可以相信

- **适合人群线索：AI 研究者或研究型 Agent 构建者**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`README.md` Claim：`clm_0004` supported 0.86
- **适合人群线索：希望把专业流程带进宿主 AI 的用户**（supported）：有 supported claim 或项目证据支撑，但仍不等于真实安装效果。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md` 等 Claim：`clm_0005` supported 0.86
- **能力存在：AI Skill / Agent 指令资产库**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md` 等 Claim：`clm_0001` supported 0.86
- **能力存在：命令行启动或安装流程**（supported）：可以相信项目包含这类能力线索；是否适合你的具体任务仍要试用或安装后验证。 证据：`README.md` Claim：`clm_0003` supported 0.86
- **存在 Quick Start / 安装命令线索**（supported）：可以相信项目文档出现过启动或安装入口；不要因此直接在主力环境运行。 证据：`README.md` Claim：`clm_0006` supported 0.86, `clm_0007` supported 0.86

### 现在还不能相信

- **真实输出质量不能在安装前相信。**（unverified）：Prompt Preview 只能展示引导方式，不能证明真实项目中的结果质量。
- **宿主 AI 版本兼容性不能在安装前相信。**（unverified）：Claude、Cursor、Codex、Gemini 等宿主加载规则和版本差异必须在真实环境验证。
- **不会污染现有宿主 AI 行为，不能直接相信。**（inferred）：Skill、plugin、AGENTS/CLAUDE/GEMINI 指令可能改变宿主 AI 的默认行为。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json` 等
- **可安全回滚不能默认相信。**（unverified）：除非项目明确提供卸载和恢复说明，否则必须先在隔离环境验证。
- **真实安装后是否与用户当前宿主 AI 版本兼容？**（unverified）：兼容性只能通过实际宿主环境验证。 证据：`camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json`, `camel/toolkits/open_api_specs/web_scraper/ai-plugin.json`
- **项目输出质量是否满足用户具体任务？**（unverified）：安装前预览只能展示流程和边界，不能替代真实评测。
- **安装命令是否需要网络、权限或全局写入？**（unverified）：这影响企业环境和个人环境的安装风险。 证据：`README.md`

### 继续会触碰什么

- **命令执行**：包管理器、网络下载、本地插件目录、项目配置或用户主目录。 原因：运行第一条命令就可能产生环境改动；必须先判断是否值得跑。 证据：`README.md`
- **宿主 AI 配置**：Claude/Codex/Cursor/Gemini/OpenCode 等宿主的 plugin、Skill 或规则加载配置。 原因：宿主配置会改变 AI 后续工作方式，可能和用户已有规则冲突。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json` 等
- **本地环境或项目文件**：安装结果、插件缓存、项目配置或本地依赖目录。 原因：安装前无法证明写入范围和回滚方式，需要隔离验证。 证据：`README.md`, `camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json`, `camel/toolkits/open_api_specs/web_scraper/ai-plugin.json`
- **宿主 AI 上下文**：AI Context Pack、Prompt Preview、Skill 路由、风险规则和项目事实。 原因：导入上下文会影响宿主 AI 后续判断，必须避免把未验证项包装成事实。

### 最小安全下一步

- **先跑 Prompt Preview**：用安装前交互式试用判断工作方式是否匹配，不需要授权或改环境。（适用：任何项目都适用，尤其是输出质量未知时。）
- **只在隔离目录或测试账号试装**：避免安装命令污染主力宿主 AI、真实项目或用户主目录。（适用：存在命令执行、插件配置或本地写入线索时。）
- **先备份宿主 AI 配置**：Skill、plugin、规则文件可能改变 Claude/Cursor/Codex 的默认行为。（适用：存在插件 manifest、Skill 或宿主规则入口时。）
- **安装后只验证一个最小任务**：先验证加载、兼容、输出质量和回滚，再决定是否深用。（适用：准备从试用进入真实工作流时。）

### 退出方式

- **保留安装前状态**：记录原始宿主配置和项目状态，后续才能判断是否可恢复。
- **准备移除宿主 plugin / Skill / 规则入口**：如果试装后行为异常，可以把宿主 AI 恢复到试装前状态。
- **记录安装命令和写入路径**：没有明确卸载说明时，至少要知道哪些目录或配置需要手动清理。
- **如果没有回滚路径，不进入主力环境**：不可回滚是继续前阻断项，不应靠信任或运气继续。

## 哪些只能预览

- 解释项目适合谁和能做什么
- 基于项目文档演示典型对话流程
- 帮助用户判断是否值得安装或继续研究

## 哪些必须安装后验证

- 真实安装 Skill、插件或 CLI
- 执行脚本、修改本地文件或访问外部服务
- 验证真实输出质量、性能和兼容性

## 边界与风险判断卡

- **把安装前预览误认为真实运行**：用户可能高估项目已经完成的配置、权限和兼容性验证。 处理方式：明确区分 prompt_preview_can_do 与 runtime_required。 Claim：`clm_0008` inferred 0.45
- **宿主 AI 插件或 Skill 规则冲突**：新规则可能改变用户现有宿主 AI 的工作方式。 处理方式：安装前先检查插件 manifest 和 Skill 文件，必要时隔离测试。 证据：`camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json`, `camel/toolkits/open_api_specs/web_scraper/ai-plugin.json` Claim：`clm_0009` inferred 0.45
- **命令执行会修改本地环境**：安装命令可能写入用户主目录、宿主插件目录或项目配置。 处理方式：先在隔离环境或测试账号中运行。 证据：`README.md` Claim：`clm_0010` supported 0.86
- **待确认**：真实安装后是否与用户当前宿主 AI 版本兼容？。原因：兼容性只能通过实际宿主环境验证。
- **待确认**：项目输出质量是否满足用户具体任务？。原因：安装前预览只能展示流程和边界，不能替代真实评测。
- **待确认**：安装命令是否需要网络、权限或全局写入？。原因：这影响企业环境和个人环境的安装风险。

## 开工前工作上下文

### 加载顺序

- 先读取 how_to_use.host_ai_instruction，建立安装前判断资产的边界。
- 读取 claim_graph_summary，确认事实来自 Claim/Evidence Graph，而不是 Human Wiki 叙事。
- 再读取 intended_users、capabilities 和 quick_start_candidates，判断用户是否匹配。
- 需要执行具体任务时，优先查 role_skill_index，再查 evidence_index。
- 遇到真实安装、文件修改、网络访问、性能或兼容性问题时，转入 risk_card 和 boundaries.runtime_required。

### 任务路由

- **AI Skill / Agent 指令资产库**：先基于 role_skill_index / evidence_index 帮用户挑选可用角色、Skill 或工作流。 边界：可做安装前 Prompt 体验。 证据：`.camel/skills/docs-incremental-update/SKILL.md`, `.camel/skills/skill-creator/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`, `examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md` 等 Claim：`clm_0001` supported 0.86
- **多宿主安装与分发**：先说明这是安装后验证能力，再给出安装前检查清单。 边界：必须真实安装或运行后验证。 证据：`camel/toolkits/open_api_specs/biztoc/ai-plugin.json`, `camel/toolkits/open_api_specs/outschool/ai-plugin.json`, `camel/toolkits/open_api_specs/web_scraper/ai-plugin.json`
- **命令行启动或安装流程**：先说明这是安装后验证能力，再给出安装前检查清单。 边界：必须真实安装或运行后验证。 证据：`README.md` Claim：`clm_0003` supported 0.86

### 上下文规模

- 文件总数：2037
- 重要文件覆盖：40/2037
- 证据索引条目：80
- 角色 / Skill 条目：5

### 证据不足时的处理

- **missing_evidence**：说明证据不足，要求用户提供目标文件、README 段落或安装后验证记录；不要补全事实。
- **out_of_scope_request**：说明该任务超出当前 AI Context Pack 证据范围，并建议用户先查看 Human Manual 或真实安装后验证。
- **runtime_request**：给出安装前检查清单和命令来源，但不要替用户执行命令或声称已执行。
- **source_conflict**：同时展示冲突来源，标记为待核实，不要强行选择一个版本。

## Prompt Recipes

### 适配判断

- 目标：判断这个项目是否适合用户当前任务。
- 预期输出：适配结论、关键理由、证据引用、安装前可预览内容、必须安装后验证内容、下一步建议。

```text
请基于 hybrid-browser-toolkit-ts 的 AI Context Pack，先问我 3 个必要问题，然后判断它是否适合我的任务。回答必须包含：适合谁、能做什么、不能做什么、是否值得安装、证据来自哪里。所有项目事实必须引用 evidence_refs、source_paths 或 claim_id。
```

### 安装前体验

- 目标：让用户在安装前感受核心工作流，同时避免把预览包装成真实能力或营销承诺。
- 预期输出：一段带边界标签的体验剧本、安装后验证清单和谨慎建议；不含真实运行承诺或强营销表述。

```text
请把 hybrid-browser-toolkit-ts 当作安装前体验资产，而不是已安装工具或真实运行环境。

请严格输出四段：
1. 先问我 3 个必要问题。
2. 给出一段“体验剧本”：用 [安装前可预览]、[必须安装后验证]、[证据不足] 三种标签展示它可能如何引导工作流。
3. 给出安装后验证清单：列出哪些能力只有真实安装、真实宿主加载、真实项目运行后才能确认。
4. 给出谨慎建议：只能说“值得继续研究/试装”“先补充信息后再判断”或“不建议继续”，不得替项目背书。

硬性边界：
- 不要声称已经安装、运行、执行测试、修改文件或产生真实结果。
- 不要写“自动适配”“确保通过”“完美适配”“强烈建议安装”等承诺性表达。
- 如果描述安装后的工作方式，必须使用“如果安装成功且宿主正确加载 Skill，它可能会……”这种条件句。
- 体验剧本只能写成“示例台词/假设流程”：使用“可能会询问/可能会建议/可能会展示”，不要写“已写入、已生成、已通过、正在运行、正在生成”。
- Prompt Preview 不负责给安装命令；如用户准备试装，只能提示先阅读 Quick Start 和 Risk Card，并在隔离环境验证。
- 所有项目事实必须来自 supported claim、evidence_refs 或 source_paths；inferred/unverified 只能作风险或待确认项。

```

### 角色 / Skill 选择

- 目标：从项目里的角色或 Skill 中挑选最匹配的资产。
- 预期输出：候选角色或 Skill 列表，每项包含适用场景、证据路径、风险边界和是否需要安装后验证。

```text
请读取 role_skill_index，根据我的目标任务推荐 3-5 个最相关的角色或 Skill。每个推荐都要说明适用场景、可能输出、风险边界和 evidence_refs。
```

### 风险预检

- 目标：安装或引入前识别环境、权限、规则冲突和质量风险。
- 预期输出：环境、权限、依赖、许可、宿主冲突、质量风险和未知项的检查清单。

```text
请基于 risk_card、boundaries 和 quick_start_candidates，给我一份安装前风险预检清单。不要替我执行命令，只说明我应该检查什么、为什么检查、失败会有什么影响。
```

### 宿主 AI 开工指令

- 目标：把项目上下文转成一次对话开始前的宿主 AI 指令。
- 预期输出：一段边界明确、证据引用明确、适合复制给宿主 AI 的开工前指令。

```text
请基于 hybrid-browser-toolkit-ts 的 AI Context Pack，生成一段我可以粘贴给宿主 AI 的开工前指令。这段指令必须遵守 not_runtime=true，不能声称项目已经安装、运行或产生真实结果。
```


## 角色 / Skill 索引

- 共索引 5 个角色 / Skill / 项目文档条目。

- **docs-incremental-update**（skill）： 激活提示：当用户任务与“docs-incremental-update”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`.camel/skills/docs-incremental-update/SKILL.md`
- **skill-creator**（skill）：Guide for creating effective skills. Use when creating a new skill or updating an existing skill that extends agent capabilities with specialized knowledge, workflows, or tool integrations. 激活提示：当用户任务与“skill-creator”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`.camel/skills/skill-creator/SKILL.md`
- **code-reviewer**（skill）：Review code for quality, bugs, and improvements. Use when user wants code review or quality assessment. 激活提示：当用户任务与“code-reviewer”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`
- **data-analyzer**（skill）：Analyze datasets and extract insights. Use when user needs to understand data patterns, statistics, or trends. 激活提示：当用户任务与“data-analyzer”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md`
- **report-writer**（skill）：Generate professional reports from analysis results. Use when user needs to create formatted documents summarizing findings. 激活提示：当用户任务与“report-writer”描述的流程高度相关时，先用它做安装前体验，再决定是否安装。 证据：`examples/toolkits/skill_toolkit_example/.camel/skills/report-writer/SKILL.md`

## 证据索引

- 共索引 80 条证据。

- **How to update the documentation**（documentation）：Helpful article here https://towardsdatascience.com/documenting-python-code-with-sphinx-554e1d6c4f6d . 证据：`docs/README.md`
- **Concept**（documentation）：Agents in CAMEL are autonomous entities capable of performing specific tasks through interaction with language models and other components. Each agent is designed with a particular role and capability, allowing them to work independently or collaboratively to achieve complex goals. 证据：`docs/key_modules/agents.md`
- **docs code map tooling**（documentation）：This folder keeps Mintlify documentation in sync with CAMEL source code. 证据：`docs/mintlify/scripts/docs_sync/README.md`
- **Install CAMEL with Docker**（documentation）：Docker offers an easy way to create a consistent and isolated virtual environment, containers, for setting up the dependencies of CAMEL. This guide will show you how to quickly set up CAMEL, run the examples, and also develop on it, with Docker. 证据：`.container/README.md`
- **Readme**（documentation）：! Documentation docs-image docs-url ! Discord discord-image discord-url ! X x-image x-url ! Reddit reddit-image reddit-url ! Wechat wechat-image wechat-url ! Hugging Face huggingface-image huggingface-url ! Star star-image star-url ! Package License package-license-image package-license-url ! PyPI Download package-download-image package-download-url ! join-us-image join-us 证据：`README.md`
- **CAMEL Model Context Protocol MCP Server**（documentation）：CAMEL Model Context Protocol MCP Server 证据：`services/README.md`
- **Agents app showcases Role Playing API as a Gradio web app**（documentation）：Agents app showcases Role Playing API as a Gradio web app 证据：`apps/agents/README.md`
- **Data Explorer tool to browse Camel dataset**（documentation）：Data Explorer tool to browse Camel dataset 证据：`apps/data_explorer/README.md`
- **Mock Website Benchmarks for Web Agent Testing**（documentation）：Mock Website Benchmarks for Web Agent Testing 证据：`camel/benchmarks/mock_website/README.md`
- **Ubuntu Docker Runtime Example**（documentation）：This example demonstrates how to use the CAMEL framework with Docker runtime in an Ubuntu environment, including role-playing capabilities with the Qwen model. 证据：`examples/runtimes/ubuntu_docker_runtime/README.md`
- **CAMEL-AI with ACI Integration Examples**（documentation）：<!-- Copyright 2023-2025 @ CAMEL-AI.org. All Rights Reserved. Licensed under the Apache License, Version 2.0 the "License" ; you may not use this file except in compliance with the License. You may obtain a copy of the License at 证据：`examples/usecases/aci_mcp/README.md`
- **🏡 Airbnb Listings Search Streamlit + CAMEL-AI + MCP**（documentation）：🏡 Airbnb Listings Search Streamlit + CAMEL-AI + MCP 证据：`examples/usecases/airbnb_mcp/README.md`
- **🤖 GitHub Repo Chat Explorer**（documentation）：A Streamlit-based chat interface powered by CAMEL-AI and MCP Model Context Protocol that lets you conversationally explore any GitHub repository. Ask questions like "How many files?", "Show me the README contents", or follow-up queries—all in context of the chosen repo. 证据：`examples/usecases/chat_with_github/README.md`
- **🎥 Video Content Q&A with CAMEL-AI**（documentation）：An intelligent Streamlit-based application that uses the CAMEL-AI https://www.camel-ai.org/ framework to allow users to extract, understand, and query the contents of YouTube videos. It leverages CAMEL toolkits and OpenAI models for transcription, summarization, and natural language Q&A. 证据：`examples/usecases/chat_with_youtube/README.md`
- **Cloudflare MCP with Camel-ai**（documentation）：<!-- Copyright 2023-2025 @ CAMEL-AI.org. All Rights Reserved. Licensed under the Apache License, Version 2.0 the "License" ; you may not use this file except in compliance with the License. You may obtain a copy of the License at 证据：`examples/usecases/cloudfare_mcp_camel/README.md`
- **🧠 Competitive Programming Problem Solver with CAMEL Agents & Firecrawl**（documentation）：🧠 Competitive Programming Problem Solver with CAMEL Agents & Firecrawl 证据：`examples/usecases/codeforces_question_solver/README.md`
- **CAMEL-AI OCR Demo w/ Mistral**（documentation）：A simple Streamlit app to extract and summarize text from PDFs and images using the Mistral OCR https://mistral.ai/research/mistral-ocr/ API, fully powered by the CAMEL-AI https://github.com/camel-ai/camel framework. 证据：`examples/usecases/mistral_OCR/README.md`
- **🧠 CAMEL Multi-Agent Research Assistant**（documentation）：🧠 CAMEL Multi-Agent Research Assistant 证据：`examples/usecases/multi_agent_research_assistant/README.md`
- **📑 AI-Powered PPTX Generator CAMEL-AI**（documentation）：📑 AI-Powered PPTX Generator CAMEL-AI 证据：`examples/usecases/pptx_toolkit_usecase/README.md`
- **🎥 YouTube Video Q&A with CAMEL Audio + Visual OCR**（documentation）：🎥 YouTube Video Q&A with CAMEL Audio + Visual OCR 证据：`examples/usecases/youtube_ocr/README.md`
- **AI-Generated Code Policy**（documentation）：Thank you for your interest in contributing to the CAMEL project! 🎉 We're excited to have your support. As an open-source initiative in a rapidly evolving and open-ended field, we wholeheartedly welcome contributions of all kinds. Whether you want to introduce new features, enhance the infrastructure, improve documentation, asking issues, add more examples, implement state-of-the-art research ideas, or fix bugs, we appreciate your enthusiasm and efforts. 🙌 You are welcome to join our discord https://discord.camel-ai.org/ for more efficient communication. 💬 证据：`CONTRIBUTING.md`
- **Package**（package_manifest）：{ "name": "hybrid-browser-toolkit-ts", "version": "1.0.0", "description": "TypeScript implementation of hybrid browser toolkit with Playwright snapshotForAI integration", "main": "dist/index.js", "scripts": { "build": "npx tsc", "dev": "npx tsc --watch", "test": "jest", "start": "node dist/index.js" }, "keywords": "playwright", "browser", "automation", "ai", "snapshot" , "author": "CAMEL-AI", "license": "Apache-2.0", "dependencies": { "playwright": "^1.57.0", "ws": "^8.14.0" }, "devDependencies": { "@types/jest": "^30.0.0", "@types/node": "^25.0.1", "@types/ws": "^8.5.0", "jest": "^30.2.0", "typescript": "^5.8.3", "ts-jest": "^29.4.6" } } 证据：`camel/toolkits/hybrid_browser_toolkit/ts/package.json`
- **Docs Incremental Update**（skill_instruction）：Update Mintlify .mdx documentation so it stays in sync with CAMEL source code. 证据：`.camel/skills/docs-incremental-update/SKILL.md`
- **Skill Creator**（skill_instruction）：This skill provides guidance for creating effective skills. 证据：`.camel/skills/skill-creator/SKILL.md`
- **Code Reviewer**（skill_instruction）：Perform thorough code reviews focusing on quality and correctness. 证据：`examples/toolkits/skill_toolkit_example/.camel/skills/code-reviewer/SKILL.md`
- **Data Analyzer**（skill_instruction）：Analyze data and provide statistical insights. 证据：`examples/toolkits/skill_toolkit_example/.camel/skills/data-analyzer/SKILL.md`
- **Report Writer**（skill_instruction）：Transform analysis results into professional reports. 证据：`examples/toolkits/skill_toolkit_example/.camel/skills/report-writer/SKILL.md`
- **License**（source_file）：Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ 证据：`LICENSE`
- **Deep Dive into CAMEL’s Practices for Self-Improving CoT Generation 🚀**（documentation）：Deep Dive into CAMEL’s Practices for Self-Improving CoT Generation 🚀 证据：`docs/cookbooks/data_generation/self_improving_cot_generation.md`
- **Table of Contents**（documentation）：You can also check this cookbook in Colab here https://colab.research.google.com/drive/1ZwVmqa5vjpZ0C3H7k1XIseFfbCR4mq17?usp=sharing 证据：`docs/cookbooks/data_processing/summarisation_agent_with_mistral_ocr.md`
- **Claude 4 + Azure OpenAI Collaboration for ARENA AI Alignment Research**（documentation）：Claude 4 + Azure OpenAI Collaboration for ARENA AI Alignment Research 证据：`docs/cookbooks/multi_agent_society/azure_openai_claude_society.md`
- **Tutorial**（documentation）：Tutorial Python Version Requirements 证据：`docs/get_started/installation.md`
- **What is CAMEL-AI?**（documentation）：CAMEL‑AI is an open‑source, modular framework for building intelligent multi‑agent systems. It provides the primitives to: 证据：`docs/get_started/introduction.md`
- **1. OpenAI API**（documentation）：CAMEL-AI supports multiple model backends. Choose one below and configure your environment variables. 证据：`docs/get_started/setup.md`
- **Overview**（documentation）：The Benchmark module in CAMEL provides a framework for evaluating AI agents and language models across various tasks and domains. It includes implementations of multiple benchmarks and provides a interface for running evaluations, measuring performance, and generating detailed reports. 证据：`docs/key_modules/Benchmark.md`
- **Installation**（documentation）：The HybridBrowserToolkit provides a powerful set of browser automation tools for CAMEL agents. It enables web navigation, form interaction, screenshot capture, and data extraction through a unified interface with TypeScript WebSocket-based and Python implementations. 证据：`docs/key_modules/browsertoolkit.md`
- **Chain of Thought CoT Data Generation**（documentation）：This page introduces CAMEL's data generation modules for creating high-quality training data with explicit reasoning, diverse instructions, and advanced automated refinement. 证据：`docs/key_modules/datagen.md`
- **Supported Embedding Types**（documentation）：Embeddings transform text, images, and other media into dense numeric vectors that capture their underlying meaning. This makes it possible for machines to perform semantic search, similarity, recommendations, clustering, RAG, and more. 证据：`docs/key_modules/embeddings.md`
- **What are Interpreters?**（documentation）：Interpreters allow CAMEL agents to execute code snippets in various secure and flexible environments—from local safe execution to isolated Docker containers and managed cloud sandboxes. 证据：`docs/key_modules/interpreters.md`
- **Types**（documentation）：CAMEL’s Loaders provide flexible ways to ingest and process all kinds of data structured files, unstructured text, web content, and even OCR from images. They power your agent’s ability to interact with the outside world. Additionally, several data readers were added, including Apify Reader , Chunkr Reader , Firecrawl Reader , Jina url Reader , and Mistral Reader , which enable retrieval of external data for improved data integration and analysis. Types 证据：`docs/key_modules/loaders.md`
- **Initialize the memory**（documentation）：The CAMEL Memory module gives your AI agents a flexible, persistent way to store, retrieve, and manage information , across any conversation or task. 证据：`docs/key_modules/memory.md`
- **Get Started**（documentation）：The BaseMessage class is the backbone for all message objects in the CAMEL chat system. It offers a consistent structure for agent communication and easy conversion between message types. 证据：`docs/key_modules/messages.md`
- **Supported Model Platforms in CAMEL**（documentation）：In CAMEL, every model refers specifically to a Large Language Model LLM the intelligent core powering your agent's understanding, reasoning, and conversational capabilities. 证据：`docs/key_modules/models.md`
- **Using Prompt Templates**（documentation）：The prompt module in CAMEL guides AI models to produce accurate, relevant, and personalized outputs. It provides a library of templates and dictionaries for diverse tasks — like role description, code generation, evaluation, embeddings, and even object recognition. You can also craft your own prompts to precisely shape your agent’s behavior. 证据：`docs/key_modules/prompts.md`
- **What Are Retrievers?**（documentation）：Retrievers are your AI search engine for large text collections or knowledge bases. They let you find the most relevant information based on a query—using either advanced embeddings semantic search or classic keyword matching. 证据：`docs/key_modules/retrievers.md`
- **What are Runtimes?**（documentation）：CAMEL’s runtime module enables the secure, flexible, and isolated execution of tools and code. Runtimes allow agents to safely run functions in controlled environments—from in-process security checks to Docker isolation and remote/cloud sandboxes. 证据：`docs/key_modules/runtimes.md`
- **🧩 RolePlaying Attributes**（documentation）：The society module simulates agent social behaviors and collaborative workflows. It powers autonomous, multi-role agents that can plan, debate, critique, and solve tasks together, minimizing human intervention while maximizing alignment with your goals. 证据：`docs/key_modules/societies.md`
- **🤝 Frameworks**（documentation）：The society module simulates agent social behaviors and collaborative workflows. It powers autonomous, multi-role agents that can plan, debate, critique, and solve tasks together, minimizing human intervention while maximizing alignment with your goals. 证据：`docs/key_modules/society.md`
- **What Are Storages in CAMEL-AI?**（documentation）：The Storage module in CAMEL-AI gives you a unified interface for saving, searching, and managing your data from simple key-value records to high-performance vector databases and modern graph engines. It’s your plug-and-play toolkit for building robust, AI-ready storage layers. 证据：`docs/key_modules/storages.md`
- **Task Attributes**（documentation）：For more detailed usage information, please refer to our cookbook: Task Generation Cookbook ../cookbooks/multi agent society/task generation.ipynb 证据：`docs/key_modules/tasks.md`
- **Initialization**（documentation）：The Terminal Toolkit provides a secure and powerful way for CAMEL agents to interact with a terminal. It allows agents to execute shell commands, manage files, and even ask for human help, all within a controlled, sandboxed environment. 证据：`docs/key_modules/terminaltoolkit.md`
- **Get Started**（documentation）：For more detailed usage information, please refer to our cookbook: Tools Cookbook ../cookbooks/advanced features/agents with tools.ipynb 证据：`docs/key_modules/tools.md`
- **Core Components Deep Dive**（documentation）：Workforce is CAMEL-AI’s powerful multi-agent collaboration engine. It enables you to assemble, manage, and scale teams of AI agents to tackle complex tasks that are beyond the capabilities of a single agent. By creating a "workforce" of specialized agents, you can automate intricate workflows, foster parallel execution, and achieve more robust and intelligent solutions. 证据：`docs/key_modules/workforce.md`
- **Quick Setup Steps**（documentation）：This guide walks you through turning your CAMEL AI agent into an MCP client, letting your agent easily use tools from multiple MCP servers. 证据：`docs/mcp/camel_agents_as_an_mcp_clients.md`
- **Quick Example**（documentation）：A Toolkit is a bundle of related tools—functions that let agents fetch data, automate, search, or integrate with services. Browse all CAMEL toolkits → 证据：`docs/mcp/camel_toolkits_as_an_mcp_server.md`
- **Overview**（documentation）：You can connect any Model Context Protocol MCP tool—like the official filesystem server—directly to your CAMEL ChatAgent. This gives your agents natural language access to external filesystems, databases, or any MCP-compatible service. 证据：`docs/mcp/connecting_existing_mcp_tools.md`
- **Quick Start**（documentation）：Publishing your ChatAgent as an MCP server turns your agent into a universal AI backend. Any MCP-compatible client Claude, Cursor, editors, or your own app can connect, chat, and run tools through your agent as if it were a native API—no custom integration required. 证据：`docs/mcp/export_camel_agent_as_mcp_server.md`
- **Mcp Hub**（documentation）：--- title: "CAMEL-AI MCPHub" icon: warehouse url: "https://mcp.camel-ai.org/" doc code map: - "camel/toolkits/mcp toolkit.py" --- 证据：`docs/mcp/mcp_hub.md`
- **What is MCP all about?**（documentation）：MCP Model Context Protocol originated from an Anthropic article https://www.anthropic.com/news/model-context-protocol published on November 25, 2024: Introducing the Model Context Protocol . 证据：`docs/mcp/overview.md`
- **cleanup temp files**（documentation）：Manages content accumulation across streaming responses to ensure all responses contain complete cumulative content. 证据：`docs/reference/camel.agents.chat_agent.md`
- 其余 20 条证据见 `AI_CONTEXT_PACK.json` 或 `EVIDENCE_INDEX.json`。

## 宿主 AI 必须遵守的规则

- **把本资产当作开工前上下文，而不是运行环境。**：AI Context Pack 只包含证据化项目理解，不包含目标项目的可执行状态。 证据：`docs/README.md`, `docs/key_modules/agents.md`, `docs/mintlify/scripts/docs_sync/README.md`
- **回答用户时区分可预览内容与必须安装后才能验证的内容。**：安装前体验的消费者价值来自降低误装和误判，而不是伪装成真实运行。 证据：`docs/README.md`, `docs/key_modules/agents.md`, `docs/mintlify/scripts/docs_sync/README.md`

## 用户开工前应该回答的问题

- 你准备在哪个宿主 AI 或本地环境中使用它？
- 你只是想先体验工作流，还是准备真实安装？
- 你最在意的是安装成本、输出质量、还是和现有规则的冲突？

## 验收标准

- 所有能力声明都能回指到 evidence_refs 中的文件路径。
- AI_CONTEXT_PACK.md 没有把预览包装成真实运行。
- 用户能在 3 分钟内看懂适合谁、能做什么、如何开始和风险边界。

---

## Doramagic Context Augmentation

下面内容用于强化 Repomix/AI Context Pack 主体。Human Manual 只提供阅读骨架；踩坑日志会被转成宿主 AI 必须遵守的工作约束。

## Human Manual 骨架

使用规则：这里只是项目阅读路线和显著性信号，不是事实权威。具体事实仍必须回到 repo evidence / Claim Graph。

宿主 AI 硬性规则：
- 不得把页标题、章节顺序、摘要或 importance 当作项目事实证据。
- 解释 Human Manual 骨架时，必须明确说它只是阅读路线/显著性信号。
- 能力、安装、兼容性、运行状态和风险判断必须引用 repo evidence、source path 或 Claim Graph。

- **项目简介**：importance `high`
  - source_paths: README.md, pyproject.toml, camel/__init__.py
- **快速入门指南**：importance `high`
  - source_paths: docs/get_started/installation.md, docs/get_started/setup.md, .env.example, examples/agents/create_chat_agent.py
- **系统架构设计**：importance `high`
  - source_paths: camel/types/enums.py, camel/schemas/base.py, camel/messages/base.py, camel/utils/commons.py
- **智能体(Agents)**：importance `high`
  - source_paths: camel/agents/base.py, camel/agents/chat_agent.py, camel/agents/critic_agent.py, camel/agents/embodied_agent.py, camel/agents/repo_agent.py
- **模型集成(Models)**：importance `high`
  - source_paths: camel/models/__init__.py, camel/models/base_model.py, camel/models/model_factory.py, camel/models/model_manager.py, camel/models/openai_model.py
- **工具集(Toolkits)**：importance `high`
  - source_paths: camel/toolkits/__init__.py, camel/toolkits/base.py, camel/toolkits/function_tool.py, camel/toolkits/search_toolkit.py, camel/toolkits/browser_toolkit.py
- **MCP协议集成**：importance `medium`
  - source_paths: camel/toolkits/mcp_toolkit.py, camel/agents/mcp_agent.py, camel/parsers/mcp_tool_call_parser.py, camel/types/mcp_registries.py, camel/utils/mcp_client.py
- **智能体社会(Societies)**：importance `high`
  - source_paths: camel/societies/__init__.py, camel/societies/role_playing.py, camel/societies/babyagi_playing.py, camel/personas/persona.py, camel/personas/persona_hub.py

## Repo Inspection Evidence / 源码检查证据

- repo_clone_verified: true
- repo_inspection_verified: true
- repo_commit: `4d195355329dd5d023cc9d1b546fcf5e0eb88f8d`
- inspected_files: `pyproject.toml`, `README.md`, `uv.lock`, `docs/conf.py`, `docs/README.md`, `docs/key_modules/tools.md`, `docs/key_modules/Benchmark.md`, `docs/key_modules/tasks.md`, `docs/key_modules/societies.md`, `docs/key_modules/society.md`, `docs/key_modules/runtimes.md`, `docs/key_modules/browsertoolkit.md`, `docs/key_modules/datagen.md`, `docs/key_modules/memory.md`, `docs/key_modules/terminaltoolkit.md`, `docs/key_modules/messages.md`, `docs/key_modules/retrievers.md`, `docs/key_modules/embeddings.md`, `docs/key_modules/workforce.md`, `docs/key_modules/prompts.md`

宿主 AI 硬性规则：
- 没有 repo_clone_verified=true 时，不得声称已经读过源码。
- 没有 repo_inspection_verified=true 时，不得把 README/docs/package 文件判断写成事实。
- 没有 quick_start_verified=true 时，不得声称 Quick Start 已跑通。

## Doramagic Pitfall Constraints / 踩坑约束

这些规则来自 Doramagic 发现、验证或编译过程中的项目专属坑点。宿主 AI 必须把它们当作工作约束，而不是普通说明文字。

### Constraint 1: 来源证据：[Feature Request] Expand WorkforceCallback to support stream chunk events

- Trigger: GitHub 社区证据显示该项目存在一个安装相关的待验证问题：[Feature Request] Expand WorkforceCallback to support stream chunk events
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能增加新用户试用和生产接入成本。
- Evidence: community_evidence:github | cevd_6e53abdf5bc6401d8d9e105ad6bc199f | https://github.com/camel-ai/camel/issues/3676 | 来源类型 github_issue 暴露的待验证使用条件。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 2: 来源证据：[Feature Request] Refactor to use `api_keys_required` and `dependencies_required` decorators

- Trigger: GitHub 社区证据显示该项目存在一个安装相关的待验证问题：[Feature Request] Refactor to use `api_keys_required` and `dependencies_required` decorators
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能增加新用户试用和生产接入成本。
- Evidence: community_evidence:github | cevd_5ca8493db7f140ca97d289664d6eb484 | https://github.com/camel-ai/camel/issues/1043 | 来源类型 github_issue 暴露的待验证使用条件。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 3: 来源证据：[Question] when using VLLM / gemma4 / unsloath studio and the right jinja template i got problems with tool calls

- Trigger: GitHub 社区证据显示该项目存在一个安装相关的待验证问题：[Question] when using VLLM / gemma4 / unsloath studio and the right jinja template i got problems with tool calls
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能增加新用户试用和生产接入成本。
- Evidence: community_evidence:github | cevd_53427dbbe45a4c2dacf26673addcd0c4 | https://github.com/camel-ai/camel/issues/4045 | 来源讨论提到 docker 相关条件，需在安装/试用前复核。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 4: 来源证据：[BUG] Both Bedrock options are broken

- Trigger: GitHub 社区证据显示该项目存在一个安全/权限相关的待验证问题：[BUG] Both Bedrock options are broken
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能影响授权、密钥配置或安全边界。
- Evidence: community_evidence:github | cevd_0dd321222ec44d14aa25d8cde3aeaef9 | https://github.com/camel-ai/camel/issues/4034 | 来源讨论提到 python 相关条件，需在安装/试用前复核。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 5: 来源证据：[Feature Request] Add OrcaRouter as a dedicated model platform

- Trigger: GitHub 社区证据显示该项目存在一个安全/权限相关的待验证问题：[Feature Request] Add OrcaRouter as a dedicated model platform
- Host AI rule: 来源问题仍为 open，Pack Agent 需要复核是否仍影响当前版本。
- Why it matters: 可能影响授权、密钥配置或安全边界。
- Evidence: community_evidence:github | cevd_9b8e4797fc294e8e8c795de01b5418da | https://github.com/camel-ai/camel/issues/4047 | 来源讨论提到 python 相关条件，需在安装/试用前复核。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 6: 社区讨论暴露的待验证问题：The best open source general AI agent is on !

- Trigger: The best open source general AI agent is on ! Excited to share our new project OWL - an open-source alternative to Manus AI with 6K+ stars and climbing… OWL: github.com/camel-ai/owl
- Host AI rule: Pack Agent 需要打开来源链接，确认问题是否仍然存在，并把验证结论写入说明书和边界卡。
- Why it matters: 这类外部讨论可能代表真实用户在安装、配置、升级或生产使用时遇到阻力；发布前不能只依赖官方 README。
- Evidence: social_signal:x | ssig_9705a4152aac4e7db1ec2472633ef681 | https://x.com/CamelAIOrg/status/1899069486587593176 | The best open source general AI agent is on !
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 7: 能力判断依赖假设

- Trigger: README/documentation is current enough for a first validation pass.
- Host AI rule: 将假设转成下游验证清单。
- Why it matters: 假设不成立时，用户拿不到承诺的能力。
- Evidence: capability.assumptions | github_repo:615510678 | https://github.com/camel-ai/camel | README/documentation is current enough for a first validation pass.
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 8: 来源证据：v0.2.91a2

- Trigger: GitHub 社区证据显示该项目存在一个运行相关的待验证问题：v0.2.91a2
- Host AI rule: 来源显示可能已有修复、规避或版本变化，说明书中必须标注适用版本。
- Why it matters: 可能阻塞安装或首次运行。
- Evidence: community_evidence:github | cevd_0c6ec0ec1a0a421a9dc7bf0e05814f20 | https://github.com/camel-ai/camel/releases/tag/v0.2.91a2 | 来源类型 github_release 暴露的待验证使用条件。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 9: 来源证据：[Question] About the Deprecation of the `reasoning_content` Field in vLLM

- Trigger: GitHub 社区证据显示该项目存在一个维护/版本相关的待验证问题：[Question] About the Deprecation of the `reasoning_content` Field in vLLM
- Host AI rule: 来源显示可能已有修复、规避或版本变化，说明书中必须标注适用版本。
- Why it matters: 可能影响升级、迁移或版本选择。
- Evidence: community_evidence:github | cevd_d44976120f444580a5a9afefb7711f0c | https://github.com/camel-ai/camel/issues/3939 | 来源类型 github_issue 暴露的待验证使用条件。
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。

### Constraint 10: 维护活跃度未知

- Trigger: 未记录 last_activity_observed。
- Host AI rule: 补 GitHub 最近 commit、release、issue/PR 响应信号。
- Why it matters: 新项目、停更项目和活跃项目会被混在一起，推荐信任度下降。
- Evidence: evidence.maintainer_signals | github_repo:615510678 | https://github.com/camel-ai/camel | last_activity_observed missing
- Hard boundary: 不要把这个坑点包装成已解决、已验证或可忽略，除非后续验证证据明确证明它已经关闭。