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of Agents (MoA) plugin for DeepSeek Harness with /moa slash command","maintainers":[{"name":"goodandready","email":"Vadimanufriev02@gmail.com"}],"readme":"# 📦 @goodandready/dsh-moa\n\n
\n\n

面向 DeepSeek Harness 的 Mixture of Agents (MoA) 多模型协作与代码综合引擎

\n\n

\n \"npm\n \"license\"\n \"DSH\n \"Node\n

\n\n

\n \"GoodAndReady\n

\n\n

\n 🇬🇧 English •\n 🇷🇺 Русский •\n 🇨🇳 中文说明\n

\n\n\n \n \n \n
\n ⭐ 如果您喜欢这个插件，请在 GitHub 上为它点亮 Star — 这能让我知道插件对您有用，并鼓励我继续开发和维护它。\n

\n 🐛 如果您发现 Bug 或希望增加功能，请使用任意语言在 GitHub 上提交 Issue — 我会评估您的建议，并在后续版本中实现有价值的改进。\n
\n\n
\n\n---\n\n## ⚡ 概述与解决的核心痛点\n\n在面对复杂的软件工程任务时，单一模型生成往往容易受到视角盲区、架构幻觉以及生成质量不稳定的限制。当用户输入模糊或缺乏技术细节的提示词时，单一模型容易做出主观假设，生成未经充分验证的单体代码。\n\n**`@goodandready/dsh-moa`** 通过 `/moa` 斜杠命令将 **Mixture of Agents (MoA)** 混合智能体架构原生引入 DeepSeek Harness：\n\n1. **自适应澄清问卷 (Questionnaire Gate)**：针对宽泛或不明确的提示词，顾问模型（Proposers）自动提炼关键分歧点，裁判模型（Judge）在生成代码前合成结构化的 2–4 题交互式问卷。\n2. **多模型并行生成与工作区沙箱隔离**：多个独立模型并行分析任务。每个候选方案的文件生成均写入独立的磁盘沙箱 (`.moa/candidate-N/`)，彻底避免跨模型文件污染。\n3. **旗舰裁判模型评估与文件自动提升 (Promotion)**：深度推理模型对所有候选方案进行交叉评审，通过机器标记 (`WINNER_CANDIDATE_INDEX: N`) 评选胜出方案，并将胜出者的完整文件自动同步到项目根目录。\n4. **极简对话摘要与 Token 节省**：对话界面不输出冗长的原始代码块，而是生成整洁的文件清单与架构设计摘要。\n5. **单轮会话即时恢复**：作为一次性会话修改器运行，任务完成后自动恢复用户原本的会话主力模型。\n6. **动态定价目录与 Token 成本估算**：300+ 模型的实时价格自动从 OpenRouter 公开目录后台获取（无需鉴权，缓存于 `~/.dsh/storages/dsh-moa-catalog.json`，24 小时刷新），同时支持直连厂商价格与 `settings.yaml` 中的自定义 `prices` 覆盖。\n7. **增量修改模式 (Refinement Mode)**：自动感知现有代码库上下文，生成精确的增量修改而非破坏性的整文件重写。\n8. **快速模式与自定义评审标准**：面向快速任务的单模型极简管线，以及可自定义的裁判评审准则。\n9. **运行历史与胜率排行榜**：对每一类运行（综合、快速模式、问卷）进行持久化记录，并内置 REST 端点（`/dsh-moa/history`、`/dsh-moa/leaderboard`、`/dsh-moa/runs/`）。\n10. **Live Canvas 一键预览（可选）**：当同一 profile 中安装了 `@goodandready/dsh-live-canvas` 时，提升到项目根目录的 HTML 会被推入其沙箱，MoA 回答附带一键预览链接；未安装时该步骤静默跳过。\n\n---\n\n## 🏗️ 架构图\n\n```mermaid\ngraph TD\n subgraph Input [\"用户交互 (聊天输入框)\"]\n Cmd[\"斜杠命令: /moa [preset] <prompt>\"]\n Gate{\"模糊需求判断\"}\n QModal[\"交互式澄清问卷
(多选选项与自定义输入)\"]\n end\n\n subgraph Proposers [\"并行顾问模型层 (Proposers)\"]\n P1[\"提案模型 1
(创新方案)\"]\n P2[\"提案模型 2
(替代架构)\"]\n P3[\"提案模型 3
(高性能策略)\"]\n WS1[\".moa/candidate-1/
(隔离文件)\"]\n WS2[\".moa/candidate-2/
(隔离文件)\"]\n WS3[\".moa/candidate-3/
(隔离文件)\"]\n end\n\n subgraph Judge [\"综合评估与提升层 (Judge)\"]\n Aggregator[\"旗舰裁判模型
(交叉对比与代码审查)\"]\n WinnerMarker{\"WINNER_CANDIDATE_INDEX\"}\n Promote[\"提升胜出者文件
(移至根目录并清理沙箱)\"]\n Summary[\"紧凑型摘要
(文件清单与架构亮点)\"]\n end\n\n Cmd --> Gate\n Gate -->|需求宽泛/模糊| QModal\n QModal -->|用户确认选项| P1 & P2 & P3\n Gate -->|需求明确/详尽| P1 & P2 & P3\n P1 --> WS1\n P2 --> WS2\n P3 --> WS3\n WS1 & WS2 & WS3 --> Aggregator\n Aggregator --> WinnerMarker\n WinnerMarker --> Promote\n Promote --> Summary\n```\n\n---\n\n## ✨ 特性与能力\n\n### 1. 斜杠命令 (`/moa`) 与实时补全\n深度集成于 DeepSeek Harness 输入框。输入 `/moa` 即可触发预设菜单与自动补全：\n\n```text\n/moa 开发一个包含图表与 WebSocket 实时更新的响应式仪表盘\n```\n\n或指定命名预设：\n\n```text\n/moa code-review 审查身份验证中间件和安全边界\n```\n\n等效的 flag 形式：\n\n```text\n/moa --preset=deep-reasoning 逐步求解这道数学题\n```\n\n### 2. 自适应澄清问卷\n当需求过于抽象（例如 *\"制作一个计算器\"*）时，系统会在生成代码前主动询问 UI 风格、数据持久化方式或框架偏好。\n\n### 3. 并行调用与实时心跳反馈\n* 多个顾问模型并发执行，并伴随实时心跳进度条 (`⏳ [3s] Processing...`，显示各模型独立进度)。\n* 自动清理顾问上下文中的冗余系统提示词与工具定义，消除“缺少工具”的拒绝报错并大幅节约 Token。\n\n### 4. 磁盘级沙箱隔离与胜出者提升\n与仅停留在聊天文本层面的 MoA 不同，`dsh-moa` 针对真实工程项目：\n* 各提案模型在 `.moa/candidate-1/`、`.moa/candidate-2/` 等独立目录生成工程代码。\n* 裁判模型对比各版本实现，通过 `WINNER_CANDIDATE_INDEX: N` 指定最优方案。\n* 胜出方案自动提升至项目根目录，临时沙箱随后自动清理。\n\n### 5. 原生设置卡片与预设管理\n在 `设置 → 插件 → Mixture of Agents` 中可视化配置模型：\n* 配置顾问模型列表（快速生成多样化构想）。\n* 配置裁判模型（强推理模型进行严谨审查）。\n* 自定义命名预设 (`default`, `fast`, `deep-reasoning`)、裁判评审准则与温度。\n* 启用/停用 MoA 开关并查看真实的主机状态徽章；遥测网格展示总运行次数与平均运行成本。\n\n### 7. 候选方案差异对比器 (Candidate Diff Viewer)\n直接在界面中可视化对比顾问方案之间以及顾问与裁判综合方案之间的逐行差异。支持文件筛选、增删改高亮，基于纯 JavaScript LCS 差异算法，零第三方依赖。\n\n### 8. 提升前 Git 快照检查点 (`dsh-time-machine`)\n在将胜出候选者的文件提升并覆盖至项目根目录前，`dsh-moa` 自动调用本地 `dsh-time-machine` 建立影子 Git 检查点 (`moa-pre-promotion: candidate-N`)。如未安装则静默回退，不阻断提升流程。\n\n### 6. Live Canvas 一键预览（可选）\n若同一 profile 中安装了 `@goodandready/dsh-live-canvas`，`dsh-moa` 会将提升后的 HTML 文件推送到 Live Canvas 的 REST 契约（`POST /dsh-live-canvas/api/preview`，由同一 harness webServer 提供服务），并在回答中附上一键预览链接（`/dsh-live-canvas/sandbox/`）。未安装该插件时此步骤静默跳过——日志无报错，也不会出现死链接。\n\n---\n\n## 📦 安装\n\n在 DeepSeek Harness Web 配置文件中安装：\n\n```bash\ndsh plugin --profile web add @goodandready/dsh-moa\n```\n\n重启 DeepSeek Harness 实例并刷新浏览器页面。\n\n---\n\n## ⚙️ 配置 (`settings.yaml`)\n\n可在 `settings.yaml` 中配置预设与模型管道，或通过 Web UI 设置面板（设置 → 插件 → Mixture of Agents）进行调整：\n\n```yaml\n# settings.yaml\ndsh-moa:\n enabled: true\n default_preset: \"default\"\n prices:\n \"my-provider/my-model\":\n input: 0.20\n output: 0.80\n \"ollama/*\":\n input: 0\n output: 0\n presets:\n - name: default\n ask_clarifying_questions: true\n reference_models:\n - provider: \"your-fast-provider\"\n model: \"your-creative-model\"\n - provider: \"your-fast-provider\"\n model: \"your-balanced-model\"\n aggregator:\n provider: \"your-reasoning-provider\"\n model: \"your-judge-model\"\n reference_temperature: 0.6\n aggregator_temperature: 0.4\n max_tokens: 4096\n judge_criteria: \"\"\n - name: fast\n ask_clarifying_questions: false\n reference_models:\n - provider: \"your-fast-provider\"\n model: \"your-fast-model\"\n aggregator:\n provider: \"your-fast-provider\"\n model: \"your-fast-model\"\n```\n\n### 配置项说明\n\n| 参数 | 类型 | 默认值 | 说明 |\n|:---|:---|:---|:---|\n| `enabled` | `boolean` | `true` | `/moa` 命令、回合路由与 `POST /dsh-moa/run` 的总开关（可在设置卡片中切换） |\n| `default_preset` | `string` | `\"default\"` | 输入 `/moa ` 且未显式指定预设时调用的预设 |\n| `presets` | `array` | `[...]` | 命名预设列表；通过 `/moa ` 或 `/moa --preset= ` 选择 |\n| `presets[].reference_models` | `array` | `[...]` | 并行提案阶段并发调用的顾问模型列表 |\n| `presets[].aggregator` | `object` | `{...}` | 负责综合评审、代码审查与裁决胜出者的裁判模型 |\n| `presets[].ask_clarifying_questions` | `boolean` | `true` | 针对宽泛需求合成澄清问卷（预设级别开关） |\n| `presets[].reference_temperature` / `.aggregator_temperature` | `number` | `0.6` / `0.4` | 顾问与裁判的采样温度 |\n| `presets[].max_tokens` | `number` | `4096` | 每次模型调用的最大输出 Token 数 |\n| `presets[].judge_criteria` | `string` | `\"\"` | 传给裁判的可选附加评审准则 |\n| `prices` | `map` | `{}` | 自定义美元/百万 Token 费率（`\"provider/model\"`、`\"provider/*\"`、`\"*\"`），用于成本估算 |\n\n> **隐私提示：** 在增量修改模式下，可读的项目文件（最多约 1.6 万字符；`.env*` 等点文件已被排除）会随提示词发送给所配置的候选模型与裁判模型。请勿在非点文件中包含密钥的项目里运行 `/moa`。\n\n---\n\n## 📊 REST API 端点\n\n| 端点 | 方法 | 说明 |\n|:---|:---|:---|\n| `/dsh-moa/status` | `GET` | 供设置卡片状态徽章使用的健康/启用状态快照 |\n| `/dsh-moa/presets` | `GET` | 返回已配置的 MoA 预设与默认预设 |\n| `/dsh-moa/presets` | `POST` | 经 schema 校验后替换预设/默认预设/enabled（非法载荷返回 400） |\n| `/dsh-moa/models` | `GET` | 列出可用于候选/裁判槽位的模型 |\n| `/dsh-moa/history?limit=20&offset=0` | `GET` | 返回近期运行记录（含候选、胜出者、Token 与成本） |\n| `/dsh-moa/leaderboard` | `GET` | 计算模型胜率排行榜与平均执行成本 |\n| `/dsh-moa/runs/` | `GET` | 按 id 返回单条运行记录 |\n| `/dsh-moa/run` | `POST` | 通过 HTTP 运行完整 MoA 管线（`enabled: false` 时返回 400） |\n\n---\n\n## 🧪 测试\n\n运行自动化测试套件：\n\n```bash\nnpm test\n```\n\n---\n\n## 📄 许可证\n\nMIT © [GooDAnDReaDY](https://github.com/GooDAnDReaDY)\n","readmeFilename":"README.zh.md"}