# model-compose - 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 model-compose 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

- **正在使用 Claude/Codex/Cursor/Gemini 等宿主 AI 的开发者**：README 或插件配置提到多个宿主 AI。 证据：`README.md` Claim：`clm_0002` supported 0.86

## 它能做什么

- **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_0001` supported 0.86

## 怎么开始

- `pip install model-compose` 证据：`README.md` Claim：`clm_0003` supported 0.86
- `git clone https://github.com/hanyeol/model-compose.git` 证据：`README.md` Claim：`clm_0004` supported 0.86
- `pip install -e .   # or: uv pip install -e .` 证据：`README.md` Claim：`clm_0005` supported 0.86
- `pip install -e .` 证据：`README.md` Claim：`clm_0005` supported 0.86, `clm_0006` supported 0.86

## 继续前判断卡

- **当前建议**：需要管理员/安全审批
- **为什么**：继续前可能涉及密钥、账号、外部服务或敏感上下文，建议先经过管理员或安全审批。

### 30 秒判断

- **现在怎么做**：需要管理员/安全审批
- **最小安全下一步**：先跑 Prompt Preview；若涉及凭证或企业环境，先审批再试装
- **先别相信**：角色质量和任务匹配不能直接相信。
- **继续会触碰**：角色选择偏差、命令执行、本地环境或项目文件

### 现在可以相信

- **适合人群线索：正在使用 Claude/Codex/Cursor/Gemini 等宿主 AI 的开发者**（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_0002` 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_0001` 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_0003` supported 0.86

### 现在还不能相信

- **角色质量和任务匹配不能直接相信。**（unverified）：角色库证明有很多角色，不证明每个角色都适合你的具体任务，也不证明角色能产生高质量结果。
- **不能把角色文案当成真实执行能力。**（unverified）：安装前只能判断角色描述和任务画像是否匹配，不能证明它能在宿主 AI 里完成任务。
- **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。
- **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。
- **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。

### 继续会触碰什么

- **角色选择偏差**：用户对任务应该由哪个专家角色处理的判断。 原因：选错角色会让 AI 从错误专业视角回答，浪费时间或误导决策。
- **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`
- **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`
- **环境变量 / API Key**：项目入口文档明确出现 API key、token、secret 或账号凭证配置。 原因：如果真实安装需要凭证，应先使用测试凭证并经过权限/合规判断。 证据：`README.ko.md`, `README.md`, `README.zh-cn.md`, `docs/user-guide/04-component-configuration.md` 等
- **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**：先用交互式试用验证任务画像和角色匹配，不要先导入整套角色库。（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。）
- **不要使用真实生产凭证**：环境变量/API key 一旦进入宿主或工具链，可能产生账号和合规风险。（适用：出现 API、TOKEN、KEY、SECRET 等环境线索时。）
- **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。
- **保留原始角色选择记录**：如果输出偏题，可以回到任务画像阶段重新选择角色，而不是继续沿着错误角色推进。
- **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。
- **准备撤销测试 API key 或 token**：测试凭证泄露或误用时，可以快速止损。
- **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_0007` 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_0008` 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

- **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_0001` supported 0.86

### 上下文规模

- 文件总数：1381
- 重要文件覆盖：40/1381
- 证据索引条目：80
- 角色 / Skill 条目：79

### 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 model-compose 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 model-compose 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 model-compose 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 79 个角色 / Skill / 项目文档条目。

- **Model-Compose User Guide**（project_doc）：Welcome to the model-compose user guide! This comprehensive documentation will help you master declarative AI workflow orchestration—from basic concepts to advanced deployment strategies. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/user-guide/README.md`
- **Model-Compose 사용자 가이드**（project_doc）：model-compose 사용자 가이드에 오신 것을 환영합니다! 이 포괄적인 문서는 기본 개념부터 고급 배포 전략까지 선언적 AI 워크플로우 오케스트레이션을 마스터하는 데 도움을 드립니다. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/user-guide/ko/README.md`
- **Model-Compose 用户指南**（project_doc）：欢迎使用 model-compose 用户指南！本综合文档将帮助您掌握声明式 AI 工作流编排——从基础概念到高级部署策略。 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/user-guide/zh-cn/README.md`
- **model-compose**（project_doc）：! model-compose - Compose Any AI, Deploy Anywhere docs/images/main-banner.png 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`README.md`
- **Model-Compose Examples**（project_doc）：This directory contains practical examples demonstrating various features and use cases of model-compose. Each example includes a ready-to-run model-compose.yml configuration file. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/README.md`
- **Code Reviewer Agent Example**（project_doc）：This example demonstrates an autonomous agent that reads files, lists directories, and searches code to perform code reviews and provide improvement suggestions. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/code-reviewer/README.md`
- **DESIGN.md Generator Example**（project_doc）：This example demonstrates a declarative pipeline that analyzes a website's visual design system and generates a comprehensive DESIGN.md document, using headless browser automation with the web-browser component and specialized AI sub-agents powered by GPT-4o. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/design-md-generator/README.md`
- **Disk Analyzer Agent Example**（project_doc）：This example demonstrates an autonomous agent that uses shell commands as tools to analyze system disk usage and provide detailed recommendations. It is an agent-powered version of the analyze-disk-usage example. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/disk-analyzer/README.md`
- **Human-in-the-Loop Agent Example**（project_doc）：This example demonstrates a file management agent that requires human approval before executing dangerous operations write, delete , while allowing safe operations read, list to run without interruption. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/human-in-the-loop/README.md`
- **K-POP Fancam Collector Example**（project_doc）：This example demonstrates an autonomous agent workflow that searches YouTube for K-POP fancam videos from a natural-language prompt, and optionally filters them by orientation portrait/vertical . It combines a GPT-4o agent with two private tool workflows backed by the YouTube Data API and a lightweight web scraper. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/kpop-fancam-collector/README.md`
- **Multi-Tool Assistant Agent Example**（project_doc）：This example demonstrates a versatile assistant agent that combines multiple tools — web search, weather lookup, calculator, and clock — to answer a wide range of questions. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/multi-tool/README.md`
- **RAG Assistant Agent Example**（project_doc）：This example demonstrates an autonomous agent that uses Retrieval-Augmented Generation RAG to answer questions by searching and adding knowledge to a ChromaDB vector store. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/rag-assistant/README.md`
- **Web Researcher Agent Example**（project_doc）：This example demonstrates an autonomous agent that searches the web and fetches page content to research a topic and provide a comprehensive answer. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/agents/web-researcher/README.md`
- **Analyze Disk Usage Example**（project_doc）：This example demonstrates a workflow that automatically analyzes system disk usage and provides detailed analysis using GPT-4o. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/analyze-disk-usage/README.md`
- **Audio Extractor Example**（project_doc）：This example demonstrates an audio extractor using the audio-extractor component, showcasing how model-compose can orchestrate ffmpeg-based audio extraction from video or audio files with configurable encoding options. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/audio-extractor/README.md`
- **Telegram Bot**（project_doc）：A Telegram bot that receives messages via webhook, processes them with OpenAI GPT-4o, and sends replies back to the user. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/channels/telegram/README.md`
- **Conditional Routing with if Example**（project_doc）：Conditional Routing with if Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/conditional-routing/if/README.md`
- **Conditional Routing with random-router Example**（project_doc）：Conditional Routing with random-router Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/conditional-routing/random/README.md`
- **Conditional Routing with switch Example**（project_doc）：Conditional Routing with switch Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/conditional-routing/switch/README.md`
- **HuggingFace Datasets Example**（project_doc）：This example demonstrates how to use model-compose with HuggingFace Datasets for loading, processing, and concatenating datasets from the HuggingFace Hub. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/datasets/huggingface/README.md`
- **Docker Nginx Example**（project_doc）：This example demonstrates how to use Docker runtime for components, running an Nginx container that serves static files from a local directory with volume mounting. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/docker/README.md`
- **Echo Server Example**（project_doc）：This example demonstrates a simple HTTP echo server that receives user input and returns it back, showcasing how model-compose can manage and communicate with local HTTP services. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/echo-server/README.md`
- **Cloudflare Named Tunnel Gateway Example**（project_doc）：Cloudflare Named Tunnel Gateway Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/gateway/http-tunnel/cloudflare-named/README.md`
- **Cloudflare Quick Tunnel Gateway Example**（project_doc）：Cloudflare Quick Tunnel Gateway Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/gateway/http-tunnel/cloudflare/README.md`
- **Ngrok HTTP Tunnel Gateway Example**（project_doc）：This example demonstrates how to use ngrok HTTP tunnel gateway to expose local services to the internet. This enables external services to send callbacks to your local endpoints without requiring a public IP or SSH server. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/gateway/http-tunnel/ngrok/README.md`
- **SSH Tunnel Gateway Example**（project_doc）：This example demonstrates how to use SSH tunnel gateway to expose local services to external networks through remote port forwarding. This enables external services to send callbacks to your local endpoints. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/gateway/ssh-tunnel/README.md`
- **ArangoDB Graph Store Example**（project_doc）：This example demonstrates how to use model-compose with ArangoDB as a graph store for building and querying social graphs. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/graph-store/arangodb/README.md`
- **Neo4j Graph Store Example**（project_doc）：This example demonstrates how to use model-compose with Neo4j as a graph store for building and querying knowledge graphs. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/graph-store/neo4j/README.md`
- **Image Processor Example**（project_doc）：This example demonstrates a comprehensive image processing service using the image-processor component, showcasing how model-compose can orchestrate various image manipulation operations through a single component with multiple actions. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/image-processor/README.md`
- **Interrupt Example**（project_doc）：This example demonstrates the Human-in-the-Loop HITL interrupt feature, which pauses a workflow for human review before and after executing a shell command. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/interrupt/README.md`
- **Redis Key-Value Store Example**（project_doc）：This example demonstrates how to use model-compose with Redis as a key-value store for storing, retrieving, and managing data in workflows. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/key-value-store/redis/README.md`
- **Make Inspiring Quote Voice Example**（project_doc）：This example demonstrates a complex multi-step workflow that combines text generation with speech synthesis, creating inspiring motivational quotes and converting them to natural-sounding audio using OpenAI GPT-4o and ElevenLabs Text-to-Speech. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/make-inspiring-quote-voice/README.md`
- **Korea DART MCP Server Example**（project_doc）：This example demonstrates how to create an MCP server for querying Korean financial disclosure data from DART Data Analysis, Retrieval and Transfer System https://opendart.fss.or.kr , operated by South Korea's Financial Supervisory Service. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/mcp-servers/korea-dart-mcp/README.md`
- **Anthropic Chat Completions Stream Example**（project_doc）：Anthropic Chat Completions Stream Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/anthropic/anthropic-chat-completions-stream/README.md`
- **Anthropic Chat Completions Example**（project_doc）：This example demonstrates how to create a simple chat interface using Anthropic's Claude model through the Messages API. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/anthropic/anthropic-chat-completions/README.md`
- **ElevenLabs Text-to-Speech Example**（project_doc）：This example demonstrates how to use model-compose with ElevenLabs AI to convert text into high-quality, natural-sounding speech. ElevenLabs provides state-of-the-art voice synthesis with multilingual support and realistic voice cloning capabilities. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/elevenlabs/elevenlabs-text-to-speech/README.md`
- **Google Cloud Vision API Example**（project_doc）：This example demonstrates how to use Google Cloud Vision API for various image analysis tasks including label detection, text recognition OCR , face detection, object localization, landmark detection, and logo detection. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/google/google-cloud-vision/README.md`
- **OpenAI Text-to-Speech Example**（project_doc）：This example demonstrates how to use model-compose with OpenAI's Text-to-Speech TTS API to convert text into natural-sounding speech using multiple high-quality voices and models. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-audio-speech/README.md`
- **OpenAI Audio Transcriptions Example**（project_doc）：OpenAI Audio Transcriptions Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-audio-transciptions/README.md`
- **OpenAI Chat Completions Stream Example**（project_doc）：OpenAI Chat Completions Stream Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-chat-completions-stream/README.md`
- **OpenAI Chat Completions Example**（project_doc）：This example demonstrates how to create a simple chat interface using OpenAI's GPT-4o model through the Chat Completions API. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-chat-completions/README.md`
- **OpenAI Image Edits Example**（project_doc）：This example demonstrates how to use model-compose with OpenAI's Image Editing API to modify images using text prompts and AI-powered image manipulation. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-image-edits/README.md`
- **OpenAI Image Generation Example**（project_doc）：This example demonstrates how to generate images from text prompts using OpenAI's image generation models, including both DALL-E and GPT image models. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-image-generations/README.md`
- **OpenAI Image Variations Example**（project_doc）：This example demonstrates how to use model-compose with OpenAI's Image Variations API to generate creative variations of existing images using DALL·E technology. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/openai/openai-image-variations/README.md`
- **xAI Chat Completion Example**（project_doc）：This example demonstrates how to create a simple chat interface using xAI's Grok model through the Chat Completions API. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-providers/xai/xai-chat-completion/README.md`
- **Chat Completion Model Task Example**（project_doc）：This example demonstrates how to use local language models for chat completion using model-compose's built-in chat-completion task with HuggingFace transformers, providing conversational AI capabilities without external API dependencies. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/chat-completion/huggingface/README.md`
- **Chat Completion llama.cpp Example**（project_doc）：This example demonstrates how to run chat completion locally using GGUF format models with llama.cpp via model-compose's built-in llamacpp driver. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/chat-completion/llamacpp/README.md`
- **Image-to-Text Model Task Example**（project_doc）：This example demonstrates how to use local vision-language models for image captioning and description using model-compose's built-in image-to-text task with HuggingFace transformers, providing offline image understanding capabilities. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/image-to-text/README.md`
- **Image Upscale Model Task Example**（project_doc）：This example demonstrates how to use local super-resolution models for image upscaling using model-compose's built-in image-upscale task with Real-ESRGAN, providing offline image enhancement capabilities. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/image-upscale/README.md`
- **Music Generation Model Task Example**（project_doc）：Music Generation Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/music-generation/README.md`
- **Speech-to-Text Model Task Example**（project_doc）：This example demonstrates how to use a local Whisper model for audio transcription using model-compose's built-in speech-to-text task with HuggingFace transformers, providing offline speech recognition capabilities. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/speech-to-text/README.md`
- **Text Summarization Stream Model Task Example**（project_doc）：Text Summarization Stream Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/summarization-stream/README.md`
- **Text Summarization Model Task Example**（project_doc）：Text Summarization Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/summarization/README.md`
- **Text Classification Model Task Example**（project_doc）：Text Classification Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-classification/README.md`
- **Text Embedding llama.cpp Example**（project_doc）：This example demonstrates how to generate text embeddings locally using GGUF format embedding models with llama.cpp via model-compose's built-in llamacpp driver. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-embedding-llamacpp/README.md`
- **Text Embedding Model Task Example**（project_doc）：This example demonstrates how to generate text embeddings using local sentence transformer models with model-compose's built-in text-embedding task, providing semantic vector representations of text for similarity search and ML applications. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-embedding/README.md`
- **Text Generation llama.cpp Example**（project_doc）：This example demonstrates how to run text generation locally using GGUF format models with llama.cpp via model-compose's built-in llamacpp driver. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-generation-llamacpp/README.md`
- **Text Generation with Multiple LoRA Adapters**（project_doc）：Text Generation with Multiple LoRA Adapters 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-generation-lora/README.md`
- **Text Generation Model Task Example**（project_doc）：This example demonstrates how to use local language models for text generation using model-compose's built-in model task functionality with HuggingFace transformers. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-generation/README.md`
- **Text to Speech Voice Cloning Model Task Example**（project_doc）：Text to Speech Voice Cloning Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-to-speech-clone/README.md`
- **Text to Speech Voice Design Model Task Example**（project_doc）：Text to Speech Voice Design Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-to-speech-design/README.md`
- **Text to Speech Preset Voice Model Task Example**（project_doc）：Text to Speech Preset Voice Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/text-to-speech-generate/README.md`
- **Text Translation Stream Model Task Example**（project_doc）：Text Translation Stream Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/translation-stream/README.md`
- **Text Translation Model Task Example**（project_doc）：Text Translation Model Task Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/model-tasks/translation/README.md`
- **SQLite Search Engine Example**（project_doc）：This example demonstrates how to use model-compose with SQLite FTS5 as a full-text search engine for indexing, searching, and managing documents in workflows. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/search-engine/sqlite/README.md`
- **Text Splitter Example**（project_doc）：This example demonstrates how to use model-compose with a text splitter component to break down large text documents into smaller, manageable chunks for AI processing and analysis. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/split-text/README.md`
- **ChromaDB Vector Store Example**（project_doc）：This example demonstrates how to use model-compose with ChromaDB as a vector store for semantic search and similarity matching using text embeddings. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/vector-store/chroma/README.md`
- **Milvus Vector Store Example**（project_doc）：This example demonstrates how to use model-compose with Milvus as a vector database for large-scale semantic search and similarity matching using text embeddings. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/vector-store/milvus/README.md`
- **VibeVoice Realtime TTS**（project_doc）：Text-to-speech using Microsoft VibeVoice Realtime 0.5B https://github.com/microsoft/VibeVoice running in a Docker container via WebSocket. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/vibevoice-realtime-tts/README.md`
- **Video Converter Example**（project_doc）：This example demonstrates a video format converter using the video-converter component, showcasing how model-compose can orchestrate ffmpeg-based video processing with configurable encoding options. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/video-converter/README.md`
- **Video Scene Detector Example**（project_doc）：This example demonstrates how to use model-compose with the video-scene-detector component to detect scene changes in video files using different detection backends. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/video-scene-detector/README.md`
- **vLLM Chat Completion Stream Example**（project_doc）：vLLM Chat Completion Stream Example 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/vllm-chat-completion-stream/README.md`
- **vLLM Text to Speech Example**（project_doc）：This example demonstrates how to generate speech audio from text using the Qwen3-TTS model served via vLLM-Omni, with support for custom voice and multilingual synthesis. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/vllm-text-to-speech/README.md`
- **Web Browser Example**（project_doc）：This example demonstrates headless browser automation using the web-browser component, with CAPTCHA detection and human-in-the-loop resolution via noVNC. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/web-browser/README.md`
- **Web Scraper Examples**（project_doc）：This example demonstrates various web scraping capabilities using the web-scraper component with multiple workflows for different scraping scenarios. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/web-scraper/README.md`
- **Workflow Queue Stream Example**（project_doc）：This example demonstrates how to stream workflow output across distributed instances using Redis. A dispatcher receives HTTP requests and forwards them to a remote subscriber, which calls the OpenAI streaming API and delivers chunks back through Redis Streams. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/workflow-queue-stream/README.md`
- **Workflow Queue Example**（project_doc）：This example demonstrates how to distribute workflow execution across multiple instances using Redis as a message queue. A dispatcher receives requests and forwards them to a remote subscriber for processing. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`examples/workflow-queue/README.md`
- **Contributing to model-compose**（project_doc）：Thanks for your interest in model-compose . This guide walks through how to set up the project locally, propose changes, and get them merged. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`CONTRIBUTING.md`
- **Model-Compose CLI Reference**（project_doc）：model-compose provides a command-line interface for managing AI model workflows and orchestration. 激活提示：当用户需要理解项目结构、安装方式或边界时参考。 证据：`docs/reference/cli.md`

## 证据索引

- 共索引 80 条证据。

- **Model-Compose User Guide**（documentation）：Welcome to the model-compose user guide! This comprehensive documentation will help you master declarative AI workflow orchestration—from basic concepts to advanced deployment strategies. 证据：`docs/user-guide/README.md`
- **Model-Compose 사용자 가이드**（documentation）：model-compose 사용자 가이드에 오신 것을 환영합니다! 이 포괄적인 문서는 기본 개념부터 고급 배포 전략까지 선언적 AI 워크플로우 오케스트레이션을 마스터하는 데 도움을 드립니다. 证据：`docs/user-guide/ko/README.md`
- **Model-Compose 用户指南**（documentation）：欢迎使用 model-compose 用户指南！本综合文档将帮助您掌握声明式 AI 工作流编排——从基础概念到高级部署策略。 证据：`docs/user-guide/zh-cn/README.md`
- **model-compose**（documentation）：! model-compose - Compose Any AI, Deploy Anywhere docs/images/main-banner.png 证据：`README.md`
- **Model-Compose Examples**（documentation）：This directory contains practical examples demonstrating various features and use cases of model-compose. Each example includes a ready-to-run model-compose.yml configuration file. 证据：`examples/README.md`
- **Code Reviewer Agent Example**（documentation）：This example demonstrates an autonomous agent that reads files, lists directories, and searches code to perform code reviews and provide improvement suggestions. 证据：`examples/agents/code-reviewer/README.md`
- **DESIGN.md Generator Example**（documentation）：This example demonstrates a declarative pipeline that analyzes a website's visual design system and generates a comprehensive DESIGN.md document, using headless browser automation with the web-browser component and specialized AI sub-agents powered by GPT-4o. 证据：`examples/agents/design-md-generator/README.md`
- **Disk Analyzer Agent Example**（documentation）：This example demonstrates an autonomous agent that uses shell commands as tools to analyze system disk usage and provide detailed recommendations. It is an agent-powered version of the analyze-disk-usage example. 证据：`examples/agents/disk-analyzer/README.md`
- **Human-in-the-Loop Agent Example**（documentation）：This example demonstrates a file management agent that requires human approval before executing dangerous operations write, delete , while allowing safe operations read, list to run without interruption. 证据：`examples/agents/human-in-the-loop/README.md`
- **K-POP Fancam Collector Example**（documentation）：This example demonstrates an autonomous agent workflow that searches YouTube for K-POP fancam videos from a natural-language prompt, and optionally filters them by orientation portrait/vertical . It combines a GPT-4o agent with two private tool workflows backed by the YouTube Data API and a lightweight web scraper. 证据：`examples/agents/kpop-fancam-collector/README.md`
- **Multi-Tool Assistant Agent Example**（documentation）：This example demonstrates a versatile assistant agent that combines multiple tools — web search, weather lookup, calculator, and clock — to answer a wide range of questions. 证据：`examples/agents/multi-tool/README.md`
- **RAG Assistant Agent Example**（documentation）：This example demonstrates an autonomous agent that uses Retrieval-Augmented Generation RAG to answer questions by searching and adding knowledge to a ChromaDB vector store. 证据：`examples/agents/rag-assistant/README.md`
- **Web Researcher Agent Example**（documentation）：This example demonstrates an autonomous agent that searches the web and fetches page content to research a topic and provide a comprehensive answer. 证据：`examples/agents/web-researcher/README.md`
- **Analyze Disk Usage Example**（documentation）：This example demonstrates a workflow that automatically analyzes system disk usage and provides detailed analysis using GPT-4o. 证据：`examples/analyze-disk-usage/README.md`
- **Audio Extractor Example**（documentation）：This example demonstrates an audio extractor using the audio-extractor component, showcasing how model-compose can orchestrate ffmpeg-based audio extraction from video or audio files with configurable encoding options. 证据：`examples/audio-extractor/README.md`
- **Telegram Bot**（documentation）：A Telegram bot that receives messages via webhook, processes them with OpenAI GPT-4o, and sends replies back to the user. 证据：`examples/channels/telegram/README.md`
- **Conditional Routing with if Example**（documentation）：Conditional Routing with if Example 证据：`examples/conditional-routing/if/README.md`
- **Conditional Routing with random-router Example**（documentation）：Conditional Routing with random-router Example 证据：`examples/conditional-routing/random/README.md`
- **Conditional Routing with switch Example**（documentation）：Conditional Routing with switch Example 证据：`examples/conditional-routing/switch/README.md`
- **HuggingFace Datasets Example**（documentation）：This example demonstrates how to use model-compose with HuggingFace Datasets for loading, processing, and concatenating datasets from the HuggingFace Hub. 证据：`examples/datasets/huggingface/README.md`
- **Docker Nginx Example**（documentation）：This example demonstrates how to use Docker runtime for components, running an Nginx container that serves static files from a local directory with volume mounting. 证据：`examples/docker/README.md`
- **Echo Server Example**（documentation）：This example demonstrates a simple HTTP echo server that receives user input and returns it back, showcasing how model-compose can manage and communicate with local HTTP services. 证据：`examples/echo-server/README.md`
- **Cloudflare Named Tunnel Gateway Example**（documentation）：Cloudflare Named Tunnel Gateway Example 证据：`examples/gateway/http-tunnel/cloudflare-named/README.md`
- **Cloudflare Quick Tunnel Gateway Example**（documentation）：Cloudflare Quick Tunnel Gateway Example 证据：`examples/gateway/http-tunnel/cloudflare/README.md`
- **Ngrok HTTP Tunnel Gateway Example**（documentation）：This example demonstrates how to use ngrok HTTP tunnel gateway to expose local services to the internet. This enables external services to send callbacks to your local endpoints without requiring a public IP or SSH server. 证据：`examples/gateway/http-tunnel/ngrok/README.md`
- **SSH Tunnel Gateway Example**（documentation）：This example demonstrates how to use SSH tunnel gateway to expose local services to external networks through remote port forwarding. This enables external services to send callbacks to your local endpoints. 证据：`examples/gateway/ssh-tunnel/README.md`
- **ArangoDB Graph Store Example**（documentation）：This example demonstrates how to use model-compose with ArangoDB as a graph store for building and querying social graphs. 证据：`examples/graph-store/arangodb/README.md`
- **Neo4j Graph Store Example**（documentation）：This example demonstrates how to use model-compose with Neo4j as a graph store for building and querying knowledge graphs. 证据：`examples/graph-store/neo4j/README.md`
- **Image Processor Example**（documentation）：This example demonstrates a comprehensive image processing service using the image-processor component, showcasing how model-compose can orchestrate various image manipulation operations through a single component with multiple actions. 证据：`examples/image-processor/README.md`
- **Interrupt Example**（documentation）：This example demonstrates the Human-in-the-Loop HITL interrupt feature, which pauses a workflow for human review before and after executing a shell command. 证据：`examples/interrupt/README.md`
- **Redis Key-Value Store Example**（documentation）：This example demonstrates how to use model-compose with Redis as a key-value store for storing, retrieving, and managing data in workflows. 证据：`examples/key-value-store/redis/README.md`
- **Make Inspiring Quote Voice Example**（documentation）：This example demonstrates a complex multi-step workflow that combines text generation with speech synthesis, creating inspiring motivational quotes and converting them to natural-sounding audio using OpenAI GPT-4o and ElevenLabs Text-to-Speech. 证据：`examples/make-inspiring-quote-voice/README.md`
- **Korea DART MCP Server Example**（documentation）：This example demonstrates how to create an MCP server for querying Korean financial disclosure data from DART Data Analysis, Retrieval and Transfer System https://opendart.fss.or.kr , operated by South Korea's Financial Supervisory Service. 证据：`examples/mcp-servers/korea-dart-mcp/README.md`
- **Anthropic Chat Completions Stream Example**（documentation）：Anthropic Chat Completions Stream Example 证据：`examples/model-providers/anthropic/anthropic-chat-completions-stream/README.md`
- **Anthropic Chat Completions Example**（documentation）：This example demonstrates how to create a simple chat interface using Anthropic's Claude model through the Messages API. 证据：`examples/model-providers/anthropic/anthropic-chat-completions/README.md`
- **ElevenLabs Text-to-Speech Example**（documentation）：This example demonstrates how to use model-compose with ElevenLabs AI to convert text into high-quality, natural-sounding speech. ElevenLabs provides state-of-the-art voice synthesis with multilingual support and realistic voice cloning capabilities. 证据：`examples/model-providers/elevenlabs/elevenlabs-text-to-speech/README.md`
- **Google Cloud Vision API Example**（documentation）：This example demonstrates how to use Google Cloud Vision API for various image analysis tasks including label detection, text recognition OCR , face detection, object localization, landmark detection, and logo detection. 证据：`examples/model-providers/google/google-cloud-vision/README.md`
- **OpenAI Text-to-Speech Example**（documentation）：This example demonstrates how to use model-compose with OpenAI's Text-to-Speech TTS API to convert text into natural-sounding speech using multiple high-quality voices and models. 证据：`examples/model-providers/openai/openai-audio-speech/README.md`
- **OpenAI Audio Transcriptions Example**（documentation）：OpenAI Audio Transcriptions Example 证据：`examples/model-providers/openai/openai-audio-transciptions/README.md`
- **OpenAI Chat Completions Stream Example**（documentation）：OpenAI Chat Completions Stream Example 证据：`examples/model-providers/openai/openai-chat-completions-stream/README.md`
- **OpenAI Chat Completions Example**（documentation）：This example demonstrates how to create a simple chat interface using OpenAI's GPT-4o model through the Chat Completions API. 证据：`examples/model-providers/openai/openai-chat-completions/README.md`
- **OpenAI Image Edits Example**（documentation）：This example demonstrates how to use model-compose with OpenAI's Image Editing API to modify images using text prompts and AI-powered image manipulation. 证据：`examples/model-providers/openai/openai-image-edits/README.md`
- **OpenAI Image Generation Example**（documentation）：This example demonstrates how to generate images from text prompts using OpenAI's image generation models, including both DALL-E and GPT image models. 证据：`examples/model-providers/openai/openai-image-generations/README.md`
- **OpenAI Image Variations Example**（documentation）：This example demonstrates how to use model-compose with OpenAI's Image Variations API to generate creative variations of existing images using DALL·E technology. 证据：`examples/model-providers/openai/openai-image-variations/README.md`
- **xAI Chat Completion Example**（documentation）：This example demonstrates how to create a simple chat interface using xAI's Grok model through the Chat Completions API. 证据：`examples/model-providers/xai/xai-chat-completion/README.md`
- **Chat Completion Model Task Example**（documentation）：This example demonstrates how to use local language models for chat completion using model-compose's built-in chat-completion task with HuggingFace transformers, providing conversational AI capabilities without external API dependencies. 证据：`examples/model-tasks/chat-completion/huggingface/README.md`
- **Chat Completion llama.cpp Example**（documentation）：This example demonstrates how to run chat completion locally using GGUF format models with llama.cpp via model-compose's built-in llamacpp driver. 证据：`examples/model-tasks/chat-completion/llamacpp/README.md`
- **Image-to-Text Model Task Example**（documentation）：This example demonstrates how to use local vision-language models for image captioning and description using model-compose's built-in image-to-text task with HuggingFace transformers, providing offline image understanding capabilities. 证据：`examples/model-tasks/image-to-text/README.md`
- **Image Upscale Model Task Example**（documentation）：This example demonstrates how to use local super-resolution models for image upscaling using model-compose's built-in image-upscale task with Real-ESRGAN, providing offline image enhancement capabilities. 证据：`examples/model-tasks/image-upscale/README.md`
- **Music Generation Model Task Example**（documentation）：Music Generation Model Task Example 证据：`examples/model-tasks/music-generation/README.md`
- **Speech-to-Text Model Task Example**（documentation）：This example demonstrates how to use a local Whisper model for audio transcription using model-compose's built-in speech-to-text task with HuggingFace transformers, providing offline speech recognition capabilities. 证据：`examples/model-tasks/speech-to-text/README.md`
- **Text Summarization Stream Model Task Example**（documentation）：Text Summarization Stream Model Task Example 证据：`examples/model-tasks/summarization-stream/README.md`
- **Text Summarization Model Task Example**（documentation）：Text Summarization Model Task Example 证据：`examples/model-tasks/summarization/README.md`
- **Text Classification Model Task Example**（documentation）：Text Classification Model Task Example 证据：`examples/model-tasks/text-classification/README.md`
- **Text Embedding llama.cpp Example**（documentation）：This example demonstrates how to generate text embeddings locally using GGUF format embedding models with llama.cpp via model-compose's built-in llamacpp driver. 证据：`examples/model-tasks/text-embedding-llamacpp/README.md`
- **Text Embedding Model Task Example**（documentation）：This example demonstrates how to generate text embeddings using local sentence transformer models with model-compose's built-in text-embedding task, providing semantic vector representations of text for similarity search and ML applications. 证据：`examples/model-tasks/text-embedding/README.md`
- **Text Generation llama.cpp Example**（documentation）：This example demonstrates how to run text generation locally using GGUF format models with llama.cpp via model-compose's built-in llamacpp driver. 证据：`examples/model-tasks/text-generation-llamacpp/README.md`
- **Text Generation with Multiple LoRA Adapters**（documentation）：Text Generation with Multiple LoRA Adapters 证据：`examples/model-tasks/text-generation-lora/README.md`
- **Text Generation Model Task Example**（documentation）：This example demonstrates how to use local language models for text generation using model-compose's built-in model task functionality with HuggingFace transformers. 证据：`examples/model-tasks/text-generation/README.md`
- **Text to Speech Voice Cloning Model Task Example**（documentation）：Text to Speech Voice Cloning Model Task Example 证据：`examples/model-tasks/text-to-speech-clone/README.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/user-guide/README.md`, `docs/user-guide/ko/README.md`, `docs/user-guide/zh-cn/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/user-guide/README.md`, `docs/user-guide/ko/README.md`, `docs/user-guide/zh-cn/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。

- **项目概述与快速入门**：importance `high`
  - source_paths: README.md, pyproject.toml, src/mindor/__init__.py, src/mindor/version.py, src/mindor/cli/compose.py
- **核心架构与控制器**：importance `high`
  - source_paths: src/mindor/core/controller/controller.py, src/mindor/core/controller/runner.py, src/mindor/core/controller/base.py, src/mindor/core/compose/compose.py, src/mindor/core/compose/manager.py
- **组件系统**：importance `high`
  - source_paths: src/mindor/core/component/component.py, src/mindor/core/component/base.py, src/mindor/core/component/context.py, src/mindor/core/component/services/agent.py, src/mindor/core/component/services/http_client.py
- **工作流与作业编排**：importance `high`
  - source_paths: src/mindor/core/workflow/workflow.py, src/mindor/core/workflow/runner.py, src/mindor/core/workflow/job/job.py, src/mindor/core/workflow/job/impl/component.py, src/mindor/core/workflow/job/impl/if_.py
- **模型任务与 AI 集成**：importance `high`
  - source_paths: src/mindor/core/component/services/model/model.py, src/mindor/core/component/services/model/base/huggingface, src/mindor/core/component/services/model/base/llamacpp.py, src/mindor/core/component/services/model/base/vllm.py, src/mindor/core/component/services/model/base/unsloth.py
- **适配器、协议与运行时**：importance `medium`
  - source_paths: src/mindor/core/controller/adapters/adapter.py, src/mindor/core/controller/adapters/services/http_server.py, src/mindor/core/controller/adapters/services/mcp_server.py, src/mindor/core/controller/adapters/services/queue_subscriber/queue_subscriber.py, src/mindor/core/listener/listener.py
- **Web UI、网关与隧道**：importance `medium`
  - source_paths: src/mindor/core/controller/webui/webui.py, src/mindor/core/controller/webui/gradio/builder.py, src/mindor/core/controller/webui/gradio/driver.py, src/mindor/dsl/schema/controller/webui, src/mindor/core/gateway/gateway.py
- **自定义开发、示例与故障排查**：importance `medium`
  - source_paths: src/mindor/dsl/schema/component/impl/types.py, src/mindor/core/component/services/file_store/drivers, src/mindor/core/component/services/vector_store/drivers, src/mindor/core/component/services/key_value_store/drivers, src/mindor/core/tracer/tracer.py

## Repo Inspection Evidence / 源码检查证据

- repo_clone_verified: true
- repo_inspection_verified: true
- repo_commit: `1f7d7c4a834e4eb45491aa8d619a51842eb13fd3`
- inspected_files: `README.md`, `pyproject.toml`, `docs/reference/cli.md`, `docs/reference/compose/component.md`, `docs/reference/compose/components/agent.md`, `docs/reference/compose/components/datasets.md`, `docs/reference/compose/components/file-store.md`, `docs/reference/compose/components/graph-store.md`, `docs/reference/compose/components/http-client.md`, `docs/reference/compose/components/http-server.md`, `docs/reference/compose/components/image-processor.md`, `docs/reference/compose/components/key-value-store.md`, `docs/reference/compose/components/mcp-client.md`, `docs/reference/compose/components/mcp-server.md`, `docs/reference/compose/components/model-memory.md`, `docs/reference/compose/components/model-tokenizer.md`, `docs/reference/compose/components/model-trainer.md`, `docs/reference/compose/components/model.md`, `docs/reference/compose/components/search-engine.md`, `docs/reference/compose/components/shell.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: 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 | https://github.com/hanyeol/model-compose | 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 2: 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 | https://github.com/hanyeol/model-compose | 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。

- Trigger: no_demo
- Evidence: downstream_validation.risk_items | https://github.com/hanyeol/model-compose | no_demo; severity=medium
- 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: 存在评分风险

- Trigger: no_demo
- Why it matters: 风险会影响是否适合普通用户安装。
- Evidence: risks.scoring_risks | https://github.com/hanyeol/model-compose | no_demo; severity=medium
- 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: issue/PR 响应质量未知

- Trigger: issue_or_pr_quality=unknown。
- Host AI rule: 抽样最近 issue/PR，判断是否长期无人处理。
- Why it matters: 用户无法判断遇到问题后是否有人维护。
- Evidence: evidence.maintainer_signals | https://github.com/hanyeol/model-compose | issue_or_pr_quality=unknown
- 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: 发布节奏不明确

- Trigger: release_recency=unknown。
- Host AI rule: 确认最近 release/tag 和 README 安装命令是否一致。
- Why it matters: 安装命令和文档可能落后于代码，用户踩坑概率升高。
- Evidence: evidence.maintainer_signals | https://github.com/hanyeol/model-compose | release_recency=unknown
- 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。