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/architecture-overview

Use when asked about harness9 architecture, module design, or how components interact — explains the system design

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harness9
1437 skills4 agents1 MCP
Install
$ npx -y skills add zhangshenao/harness9 --skill architecture-overview --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

  • Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.Auto-invocation is when the right skill fires by itself at the right moment, driven by a FLOW.md router and a hook, instead of you invoking it by name. It is the difference between a skill being installed and a skill actually getting used.Read the full definition →
  • You can call itInvoke it directly when you want it.
  • Slash command/architecture-overview

Context preview

The summary Claude sees to decide when to auto-load this skill.

Use when asked about harness9 architecture, module design, or how components interact — explains the system design

SKILL.md

architecture-overview.SKILL.md
name: architecture-overview
description: Use when asked about harness9 architecture, module design, or how components interact — explains the system design
trigger: architecture, design, how does, module, component, structure

harness9 架构概览

核心设计原则

| 原则 | 说明 | |------|------| | **简洁** | 最小化抽象层,极少的直接依赖 | | **完备** | 覆盖 Agent 运行所需的全部核心模块 | | **生产可用** | 错误恢复、超时控制、路径沙箱、并发安全 |

标准 ReAct 循环

Turn N:
  LLM(messages + tools) → 推理 + 工具调用决策
  → 并发执行所有工具调用 → Observation 注入 context
  → Turn N+1

自然终止:模型不再发起工具调用 → 输出最终回复

三重终止保障: 1. **自然终止**:`len(responseMsg.ToolCalls) == 0` 2. **MaxTurns**:默认 50,可通过 `WithMaxTurns` 配置 3. **Context 取消**:外部 `cancel()` 或超时

模块依赖关系

cmd/harness9 (入口)
    ├── internal/context (System Prompt 组装)
    │       └── internal/skills (Skills 解析 + 索引)
    ├── internal/engine (ReAct 主循环)
    │       ├── internal/provider (LLM 调用)
    │       ├── internal/tools (工具注册 + 执行)
    │       └── internal/schema (数据类型)
    └── internal/env (配置加载)

**关键设计决策:接口定义在使用者侧**

  • `tools.Registry` 接口定义在 `tools` 包,`engine` 包依赖它
  • `engine.PromptBuilder` 接口定义在 `engine` 包,`context` 包实现它
  • `skills.UseSkillTool` 通过 Go 结构类型满足 `tools.BaseTool` 接口,不需要 import `tools` 包(避免循环依赖)

关键数据流

TUI 模式

用户输入 → RunTUI → eng.RunStream(ctx, prompt)
    → engine.Event stream → 逐 token 追加到对话视图
    → ToolCalls → Spinner 动画 + 耗时计数
    → EventDone → 最终回复渲染到屏幕

CLI 模式(管道 / CI)

用户输入 → RunCLI → eng.Run(ctx, prompt)
    → runLoop → LLM Generate
    → ToolCalls → 并发执行 → ToolResults → 继续循环
    → 最终回复打印到 stdout

System Prompt 组装

`DefaultPromptBuilder.Build()` 按顺序组装:

1. **基础 Prompt**:角色定义 + workDir 2. **AGENTS.md**:项目级规范(文件不存在时跳过) 3. **Skills 索引**:`- name: description` 列表(为空时跳过)

完整内容示例:

You are harness9, an expert coding assistant...

## Project Guidelines (AGENTS.md)
{AGENTS.md 全文}

## Available Skills
Use the `use_skill` tool to load full content of any skill when needed.
- go-coding-standards: Use when writing or reviewing Go code...
- debugging-guide: Use when debugging Go errors...

Provider 抽象

type LLMProvider interface {
    Generate(ctx, messages, tools) (Message, error)
    GenerateStream(ctx, messages, tools) (<-chan StreamChunk, error)
}

当前实现:

  • `OpenAIProvider`:兼容所有 OpenAI Chat Completions API(包括 OpenRouter、Azure)
  • `AnthropicProvider`:Anthropic Messages API

**Anthropic 约束**:user/assistant 消息必须严格交替,禁止连续 assistant 消息。

工具系统

type BaseTool interface {
    Name() string
    Definition() schema.ToolDefinition  // JSON Schema,传给 LLM
    Execute(ctx context.Context, args json.RawMessage) (string, error)
}

内置工具:

| 工具 | 说明 | |------|------| | `bash` | Shell 命令执行,workDir 为 CWD | | `read_file` | 文件读取,4096 字节截断 | | `write_file` | 文件写入,自动 mkdir | | `edit_file` | 字符串替换编辑,多级模糊匹配 | | `use_skill` | 按需加载 Skill 全文 |

所有文件工具通过 `safePath()` 校验路径,防止 Path Traversal 攻击。

Read more
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Local-First · Lightweight · Complete · Production-Ready general-purpose Agent framework, written in Go.

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