zhangshenao/harness9/architecture-overview

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

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)

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

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

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...

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

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