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feat: refresh course through workflow and goal loops
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Haoran
@@ -1,38 +1,38 @@
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# s02: Tool Use — 多加一个工具，只加一行
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# s02: Tool Use — Add a Tool, Add Just One Line
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[中文](README.md) · [English](README.en.md) · [日本語](README.ja.md)
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[English](README.md) · [中文](README.zh.md) · [日本語](README.ja.md)
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s01 → `s02` → [s03](../s03_permission/) → s04 → ... → s20
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> *"加一个工具, 只加一个 handler"* — 循环不用动, 新工具注册进 dispatch map 就行。
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s01 → `s02` → [s03](../s03_permission/) → s04 → ... → s20 → s21 → s22
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> *"Add a tool, add just one handler"* — The loop stays the same. Register the new tool in the dispatch map and you're done.
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>
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> **Harness 层**: 工具分发 — 扩展模型能触达的边界。
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> **Harness Layer**: Tool Dispatch — Expanding the model's reach.
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---
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## 只有 bash 一个工具
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## Only One Tool: Bash
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s01 的 Agent 只有一个 bash 工具。读文件要 `cat`，写文件要 `echo "..." > file.py`，改文件要 `sed`。
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The s01 Agent has only one tool: bash. To read a file, `cat`; to write, `echo "..." > file.py`; to edit, `sed`.
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模型想的是"读这个文件"，却要拼出 `cat path/to/file`。多了一层翻译，浪费 token，还容易拼错。
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The model thinks "read this file" but has to spell out `cat path/to/file`. An extra layer of translation that wastes tokens and invites errors.
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---
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## 全局视角：工具分发
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## Overview: Tool Dispatch
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s01 的循环完全保留（LLM 调用、stop_reason 判断、消息追加）。唯一的变动在工具执行那 1 行：`run_bash()` 替换为 `TOOL_HANDLERS[block.name]()` 查表分发。
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The s01 loop is fully preserved (LLM call, stop_reason check, message append — not a single word changed). The only change is in that one line of tool execution: `run_bash()` is replaced with `TOOL_HANDLERS[block.name]()` dispatch lookup.
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给 Agent 加一个工具只需要做两件事：
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Adding a tool to the Agent requires just two things:
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1. **定义工具**：在 `TOOLS` 数组里加一条描述
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2. **注册处理函数**：在 `TOOL_HANDLERS` 字典里加一个映射
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1. **Define the tool**: Add one entry to the `TOOLS` array
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2. **Register the handler**: Add one mapping in the `TOOL_HANDLERS` dict
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---
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## 从 1 个工具到 5 个工具
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## From 1 Tool to 5 Tools
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s01 只有一个 bash：
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s01 had only bash:
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```python
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TOOLS = [{"name": "bash", ...}]
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@@ -40,7 +40,7 @@ TOOLS = [{"name": "bash", ...}]
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def run_bash(command): ...
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```
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s02 加到 5 个，每个工具都是独立定义：
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s02 expands to 5 tools, each independently defined:
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```python
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TOOLS = [
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@@ -52,7 +52,7 @@ TOOLS = [
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]
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```
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每个工具有自己的实现函数：
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Each tool has its own implementation function:
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```python
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def run_read(path, limit=None):
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@@ -79,7 +79,7 @@ def run_glob(pattern):
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---
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## 工具分发
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## Tool Dispatch
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```python
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TOOL_HANDLERS = {
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@@ -90,133 +90,133 @@ TOOL_HANDLERS = {
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"glob": run_glob,
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}
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# 循环里只改了一行——从硬编码 run_bash 变成查表：
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# Only one line changed in the loop — from hardcoded run_bash to dispatch lookup:
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for block in response.content:
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if block.type == "tool_use":
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handler = TOOL_HANDLERS[block.name] # 查表
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output = handler(**block.input) # 调用
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handler = TOOL_HANDLERS[block.name] # lookup
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output = handler(**block.input) # call
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results.append(...)
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```
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加一个工具 = 在 `TOOLS` 数组加一条 + 在 `TOOL_HANDLERS` 字典加一行。循环不变。
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Adding a tool = one entry in `TOOLS` array + one line in `TOOL_HANDLERS` dict. The loop stays the same.
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---
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## 多个工具调用
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## Multiple Tool Calls
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模型经常一次返回多个 tool_use："读一下 a.py 和 b.py，然后列出所有 .py 文件"。
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The model often returns multiple tool_use calls at once — "read a.py and b.py, then list all .py files".
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教学版按 `response.content` 原始顺序逐个执行。CC 的做法更复杂：按原始顺序切成连续 batch，batch 内并发安全的工具并行执行，batch 间严格顺序（见附录）。
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The teaching version executes them one by one in the original `response.content` order. CC's approach is more complex: it slices the original order into consecutive batches, where concurrency-safe tools within a batch run in parallel, and batches are strictly sequential (see appendix).
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---
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## 速查
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## Quick Reference
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| 概念 | 一句话 |
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|------|--------|
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| TOOL_HANDLERS | 工具名 → 处理函数的字典。加工具 = 加一行映射 |
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| 工具定义 | 告诉模型"我能做什么"的 JSON schema |
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| 多工具调用 | 模型可一次返回多个 tool_use，教学版按原始顺序逐个执行 |
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| 循环不变 | s01 的 `while True` 循环一行都没改 |
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| Concept | One-Liner |
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|---------|-----------|
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| TOOL_HANDLERS | Tool name → handler function dict. Add a tool = add one mapping line |
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| Tool Definition | JSON schema telling the model "what I can do" |
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| Multiple tool calls | Model may return multiple tool_use at once; teaching version executes them in original order |
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| Loop Unchanged | s01's `while True` loop — not a single line changed |
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---
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## 相对 s01 的变更
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## Changes from s01
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| 组件 | 之前 (s01) | 之后 (s02) |
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|------|-----------|-----------|
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| 工具数量 | 1 (bash) | 5 (+read, write, edit, glob) |
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| 工具执行 | 硬编码 `run_bash()` | TOOL_HANDLERS 查表分发 |
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| 路径安全 | 无 | safe_path 校验（仅 file tools） |
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| 循环 | `while True` + `stop_reason` | 与 s01 完全一致 |
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| Component | Before (s01) | After (s02) |
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|-----------|-------------|-------------|
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| Tool count | 1 (bash) | 5 (+read, write, edit, glob) |
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| Tool execution | Hardcoded `run_bash()` | TOOL_HANDLERS dispatch lookup |
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| Path safety | None | safe_path validation (file tools only) |
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| Loop | `while True` + `stop_reason` | Identical to s01 |
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---
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## 试一下
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## Try It
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```sh
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cd learn-claude-code
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python s02_tool_use/code.py
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```
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试试这些 prompt：
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Try these prompts:
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1. `Read the file README.md and tell me what this project is about`
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2. `Create a file called test.py that prints "hello", then read it back`
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3. `Find all Python files in this directory`
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4. `Read both README.md and requirements.txt, then create a summary file`
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观察重点：模型什么时候只调一个工具，什么时候一次调多个？多个工具调用的顺序和结果是否正确？
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What to watch for: When does the model call just one tool, and when does it call multiple at once? Are multiple tool calls executed in the correct order?
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---
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## 接下来
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## What's Next
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现在 Agent 有 5 个专用工具。file tools 受 `safe_path` 保护，但 bash 不受限制，`rm -rf /` 还是能跑。
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The Agent now has 5 specialized tools. File tools are protected by `safe_path`, but bash is unrestricted — `rm -rf /` still runs.
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s03 Permission → 在工具执行之前加一道门：这个操作安全吗？需要用户批准吗？
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→ s03 Permission: Add a gate before tool execution — is this operation safe? Does it need user approval?
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<details>
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<summary>深入 CC 源码</summary>
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<summary>Dive into CC Source Code</summary>
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> 以下基于 CC 源码 `Tool.ts`、`tools.ts`、`toolOrchestration.ts`、`toolExecution.ts`、`StreamingToolExecutor.ts` 的核查。
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> The following is based on a review of CC source code `Tool.ts`, `tools.ts`, `toolOrchestration.ts`, `toolExecution.ts`, and `StreamingToolExecutor.ts`.
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### 一、工具定义方式
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### 1. Tool Definition Approach
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**教学版**：`TOOLS` 数组 + `TOOL_HANDLERS` 字典。定义和实现分开。
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**CC**：每个工具是 `buildTool()` 创建的独立对象，包含 schema、验证、权限、执行。`getAllBaseTools()` 汇总所有工具。
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**Teaching version**: `TOOLS` array + `TOOL_HANDLERS` dict. Definition and implementation are separate.
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**CC**: Each tool is an independent object created by `buildTool()`, containing schema, validation, permissions, and execution. `getAllBaseTools()` aggregates all tools.
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教学版的分离方式对教学更清晰——读者一眼看到"加一个工具 = 两条定义"。
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The teaching version's separation is clearer for teaching — readers immediately see "add a tool = two definitions".
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### 二、并发安全判断：isConcurrencySafe()
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### 2. Concurrency Safety: isConcurrencySafe()
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教学版按原始顺序逐个执行，不做并发。CC 用 `isConcurrencySafe(input)` 判断能否并发——注意这不是简单的"只读 vs 写"，而是按具体输入判断：
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The teaching version executes tools one by one in original order, without concurrency. CC uses `isConcurrencySafe(input)` to determine concurrency — note this isn't simply "read-only vs write", but judges by specific input:
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| | isReadOnly | isConcurrencySafe |
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|---|---|---|
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| FileRead | true | true |
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| Glob | true | true |
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| Bash `ls` | true | **true** ← 关键差异 |
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| Bash `ls` | true | **true** ← key difference |
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| Bash `rm` | false | false |
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| TaskCreate | false | **true** ← 改状态但可并发（TaskCreate 在 s12 介绍） |
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| TaskCreate | false | **true** ← modifies state but can be concurrent (introduced in s12) |
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CC 的 Bash tool 的 `isConcurrencySafe` 等于 `isReadOnly`——只读命令可并发，写命令不可。TaskCreate 虽然改了任务文件，但每次都写不同的文件，所以可以并发。
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CC's Bash tool's `isConcurrencySafe` equals `isReadOnly` — read-only commands can be concurrent, write commands cannot. TaskCreate modifies task files, but each writes a different file, so it can be concurrent.
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### 三、分区算法
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### 3. Partition Algorithm
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CC 的 `partitionToolCalls()`（`toolOrchestration.ts:91-115`）不是分两组，而是把工具调用**按连续块分批**：
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CC's `partitionToolCalls()` (`toolOrchestration.ts:91-115`) doesn't split into two groups — it batches tool calls **by consecutive blocks**:
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```
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[read A, read B, glob *.py, bash "rm x", read C]
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→ batch1(并发): [read A, read B, glob *.py]
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→ batch2(串行): [bash "rm x"]
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→ batch3(并发): [read C]
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→ batch1(concurrent): [read A, read B, glob *.py]
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→ batch2(serial): [bash "rm x"]
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→ batch3(concurrent): [read C]
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```
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并发安全的连续块编入同一个 batch，batch 内真正并发执行（`toolOrchestration.ts:152-176`，有并发上限）。遇到非并发安全的就开新 batch 串行执行。batch 之间严格顺序。
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Consecutive concurrency-safe calls are grouped into the same batch for truly concurrent execution (`toolOrchestration.ts:152-176`, with a concurrency limit). When a non-concurrency-safe call is encountered, a new batch starts for serial execution. Batches are strictly sequential.
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### 四、验证管线
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### 4. Validation Pipeline
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CC 的每个工具调用经过严格的 5 步验证（`toolExecution.ts`）：
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Each tool call in CC goes through a strict 5-step validation (`toolExecution.ts`):
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1. **Zod schema 验证**（`614-680`，教学版用 JSON Schema 替代）：参数类型/结构检查
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2. **工具级 validateInput()**（`682-733`）：参数值验证（如路径是否在工作区内）
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3. **PreToolUse hooks**（`800-862`，s04 详细介绍）：钩子可以返回消息、修改输入、阻止执行
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4. **权限检查**（`921-931`，s03 的核心内容）：canUseTool + checkPermissions → allow/deny/ask
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5. **执行 tool.call()**（`1207-1222`）
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1. **Zod schema validation** (`614-680`, teaching version uses JSON Schema): parameter type/structure check
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2. **Tool-level validateInput()** (`682-733`): parameter value validation (e.g., is the path within the working directory)
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3. **PreToolUse hooks** (`800-862`, covered in s04): hooks can return messages, modify input, or block execution
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4. **Permission check** (`921-931`, core topic of s03): canUseTool + checkPermissions → allow/deny/ask
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5. **Execute tool.call()** (`1207-1222`)
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教学版省略了 Zod（用 JSON Schema）、省略了 validateInput（用安全函数）、保留了权限检查和钩子概念。
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The teaching version omits Zod (uses JSON Schema), omits validateInput (uses safety functions), but preserves the permission check and hook concepts.
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### 五、流式工具执行
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### 5. Streaming Tool Execution
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CC 的 `StreamingToolExecutor`（`StreamingToolExecutor.ts`）让工具在模型还在生成时就启动——不等模型说完。`read_file` 可能在模型还在输出"我来分析"的时候就跑完了。教学版不实现这个，目标和 s01 一致——概念清晰，不追求性能极致。
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CC's `StreamingToolExecutor` (`StreamingToolExecutor.ts`) starts tools while the model is still generating — no waiting for the model to finish. `read_file` might complete while the model is still outputting "Let me analyze". The teaching version doesn't implement this, consistent with s01's goal — conceptual clarity, not peak performance.
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### 六、工具结果持久化
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### 6. Tool Result Persistence
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每个工具有一个 `maxResultSizeChars` 字段。结果超过这个值就落盘，模型看到的是预览 + 文件路径。FileRead 特殊——设为 `Infinity`，防止读文件的输出又被当成文件落盘。具体来说，如果 FileRead 的结果超过阈值被落盘，模型下次读那个落盘文件时又会触发落盘 → 无限循环（读文件 → 落盘 → 再读 → 再落盘 → ...）。
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Each tool has a `maxResultSizeChars` field. Results exceeding this threshold are persisted to disk, and the model sees a preview + file path. FileRead is special — set to `Infinity`, preventing file read output from being persisted again. Specifically, if FileRead's result exceeds the threshold and gets persisted, the model's next read of that persisted file would trigger another persistence → infinite loop (read file → persist → re-read → re-persist → ...).
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</details>
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<!-- translation-sync: zh@v1, en@v0, ja@v0 -->
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<!-- translation-sync: zh@v1, en@v1, ja@v1 -->
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