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* feat: s01-s14 docs quality overhaul — tool pipeline, single-agent, knowledge & resilience Rewrite code.py and README (zh/en/ja) for s01-s14, each chapter building incrementally on the previous. Key fixes across chapters: - s01-s04: agent loop, tool dispatch, permission pipeline, hooks - s05-s08: todo write, subagent, skill loading, context compact - s09-s11: memory system, system prompt assembly, error recovery - s12-s14: task graph, background tasks, cron scheduler All chapters CC source-verified. Code inherits fixes forward (PROMPT_SECTIONS, json.dumps cache, real-state context, can_start dep protection, etc.). * feat: s15-s19 docs quality overhaul — multi-agent platform: teams, protocols, autonomy, worktree, MCP tools Rewrite code.py and README (zh/en/ja) for s15-s19, the multi-agent platform chapters. Each chapter inherits all previous fixes and adds one mechanism: - s15: agent teams (TeamCreate, teammate threads, shared task list) - s16: team protocols (plan approval, shutdown handshake, consume_inbox) - s17: autonomous agents (idle polling, auto-claim, consume_lead_inbox) - s18: worktree isolation (git worktree, bind_task, cwd switching, safety) - s19: MCP tools (MCPClient, normalize_mcp_name, assemble_tool_pool, no cache) All appendix source code references verified against CC source. Config priority corrected: claude.ai < plugin < user < project < local. * fix: 5 regressions across s05-s19 — glob safety, todo validation, memory extraction, protocol types, dep crash - s05-s09: glob results now filter with is_relative_to(WORKDIR) (inherited from s02) - s06-s08: todo_write validates content/status required fields (inherited from s05) - s09: extract_memories uses pre-compression snapshot instead of compacted messages - s16: submit_plan docstring clarifies protocol-only (not code-level gate) - s17-s19: match_response restores type mismatch validation (from s16) - s17-s19: claim_task deps list handles missing dep files without crashing * fix: s12 Todo V2 logic reversal, s14/s15 cron range validation, s18/s19 worktree name validation - s12 README (zh/en/ja): fix Todo V2 direction — interactive defaults to Task, non-interactive/SDK defaults to TodoWrite. Fix env var name to CLAUDE_CODE_ENABLE_TASKS (not TODO_V2). - s14/s15: add _validate_cron_field with per-field range checks (minute 0-59, hour 0-23, dom 1-31, month 1-12, dow 0-6), step > 0, range lo <= hi. Replace old try/except validation that only caught exceptions. - s18/s19: add validate_worktree_name() to remove_worktree and keep_worktree, not just create_worktree. * fix: align s16-s19 teaching tool consistency * fix pr265 chapter diagrams * Add comprehensive s20 harness chapter * Fix chapter smoke test regressions * Clarify README tutorial track transition --------- Co-authored-by: Haoran <bill-billion@outlook.com>
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# s20: Comprehensive Agent — 全部机制，归到一个循环
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[中文](README.md) · [English](README.en.md) · [日本語](README.ja.md)
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s01 → ... → s18 → s19 → `s20`
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> *"机制很多，循环一个"* — 工具、权限、记忆、任务、团队、插件都挂在同一个 while True 上。
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>
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> **Harness 层**: 综合 — 把前 19 章的机制放回同一个可运行系统。
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---
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## 问题
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前 19 章每章只加一个机制。这样适合学习，但真实 Agent 不会只带一个机制运行。
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一个能长期工作的 coding agent 需要同时拥有：
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- 工具分发和权限边界
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- hooks 扩展点
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- todo 计划和任务图
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- 技能、记忆、系统 prompt 组装
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- 压缩和错误恢复
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- 后台任务和 cron 调度
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- 团队、协议、自治认领
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- worktree 隔离
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- MCP 外部工具接入
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难点不是把功能堆起来，而是看清楚它们都挂在循环的哪个位置。S20 就是终点章：把所有组件归位。
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---
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## 解决方案
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S20 不是再发明一个新机制，而是把前面的教学组件合成一个完整 harness：
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```text
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用户输入
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→ UserPromptSubmit hooks
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→ cron/background 通知注入
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→ context compact
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→ memory + skills + MCP 状态组装 system prompt
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→ LLM
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→ has tool_use block?
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否 → Stop hooks → 返回
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是 → PreToolUse hooks + permission
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→ TOOL_HANDLERS / MCP handlers / background dispatch
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→ PostToolUse hooks
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→ tool_result / task_notification 回 messages
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→ 下一轮
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```
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循环本身仍然是同一个结构：调用模型，检查响应里是否出现 `tool_use` block，执行工具，把结果追加回 `messages`。CC 源码里也不直接信任 `stop_reason == "tool_use"`，而是以实际出现的 tool_use block 作为是否继续工具轮的信号。变化的是循环周围的 harness 变完整了。
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---
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## 组件在循环中的位置
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| 位置 | 组件 | 作用 |
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|------|------|------|
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| 用户输入前后 | `UserPromptSubmit` hooks | 记录、注入、审计用户输入 |
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| LLM 前 | cron queue | 把定时触发的 prompt 注入 `messages` |
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| LLM 前 | background notifications | 后台任务完成后以 `<task_notification>` 注入 |
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| LLM 前 | compaction pipeline | 先压大输出，再裁历史，再压旧 tool_result，必要时摘要 |
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| LLM 前 | memory / skills / MCP state | 组装 system prompt，让模型看到当前能力和长期上下文 |
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| LLM 调用 | error recovery | 429/529 重试，`max_tokens` 升级，prompt too long 触发 reactive compact |
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| 工具执行前 | `PreToolUse` hooks + permission | 拦截危险命令、写越界、破坏性 MCP 工具 |
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| 工具分发 | `assemble_tool_pool` | 组装内置工具和 MCP 动态工具 |
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| 工具执行时 | background dispatch | 慢 bash 操作放 daemon thread，主循环先返回占位结果 |
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| 工具执行后 | `PostToolUse` hooks | 大输出告警、日志等后处理 |
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| 返回循环 | tool_result | 每个 `tool_use` 对应一个 `tool_result`，再回到下一轮 |
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| 本轮没有 tool_use / 停止时 | `Stop` hooks | 统计、清理、审计 |
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---
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## code.py 包含什么
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### 工具与分发
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内置工具池包含 27 个工具：
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```text
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bash, read_file, write_file, edit_file, glob
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todo_write, task, load_skill, compact
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create_task, list_tasks, get_task, claim_task, complete_task
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schedule_cron, list_crons, cancel_cron
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spawn_teammate, send_message, check_inbox
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request_shutdown, request_plan, review_plan
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create_worktree, remove_worktree, keep_worktree
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connect_mcp
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```
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`assemble_tool_pool()` 每轮组装：
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```text
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BUILTIN_TOOLS + connected MCP tools
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BUILTIN_HANDLERS + mcp__server__tool handlers
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```
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所以 `connect_mcp("docs")` 后，下一轮工具池里会出现 `mcp__docs__search`。
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### 权限和 hooks
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权限不写死在工具执行行里，而是作为 `PreToolUse` hook：
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```python
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blocked = trigger_hooks("PreToolUse", block)
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if blocked:
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results.append(tool_result(block.id, blocked))
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continue
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```
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这样 permission、log、审计都可以挂在同一个 hook 点上。执行后再触发 `PostToolUse`。
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### 计划与任务
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S20 同时保留两层计划：
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- `todo_write`：当前会话内的轻量计划，写入 `.tasks/current_todos.json`
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- task graph：跨会话、可依赖、可认领的任务文件，写入 `.tasks/task_*.json`
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前者帮助单个 Agent 不漂移；后者支撑团队协作。
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### 子 agent 与团队
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S20 有两种 delegation：
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- `task`：一次性 subagent。独立 `messages[]`，中间过程丢弃，只返回最终摘要。
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- `spawn_teammate`：持久队友线程。通过 MessageBus 收发消息，能 idle 轮询任务板并自动认领。
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一次性 subagent 解决“上下文隔离”；持久队友解决“长期并行协作”。
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### 记忆、技能和 prompt
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`assemble_system_prompt(context)` 每轮组装：
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- 身份和工具说明
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- workspace
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- skills catalog
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- `.memory/MEMORY.md`
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- 已连接 MCP server
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技能只在 system prompt 里放目录。完整内容通过 `load_skill(name)` 按需加载。
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### 压缩和恢复
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LLM 前先跑压缩管线：
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```text
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tool_result_budget → snip_compact → micro_compact → compact_history
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```
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调用模型时再包一层恢复：
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- 429：指数退避重试
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- 529：指数退避，连续失败可切 fallback model
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- `max_tokens`：先提高 max_tokens，再要求 continuation
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- prompt too long：reactive compact 后重试
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### 后台和 cron
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慢 bash 操作不会阻塞主循环：
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```text
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should_run_background → start_background_task → placeholder tool_result
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后台完成 → task_notification → 下一轮注入 messages
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```
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cron 调度器独立 daemon thread 每秒检查一次。CLI 会监听 `cron_queue`，命中后主动把 `[Scheduled] ...` 注入并运行一轮 Agent。
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### worktree 与 MCP
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worktree 负责隔离目录：
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- `create_worktree(name, task_id)` 创建独立分支和目录
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- task 的 `worktree` 字段绑定目录
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- 队友 claim 到带 worktree 的 task 后，bash/read/write 自动在对应目录下执行
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MCP 负责外部能力：
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- `connect_mcp(name)` 连接 mock server
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- `assemble_tool_pool()` 把 MCP 工具组装进工具池
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- 工具名统一为 `mcp__server__tool`
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---
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## 相对 s19 的变化
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| 组件 | s19 | s20 |
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|------|-----|-----|
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| 工具池 | 内置 + MCP | 内置 + MCP，补齐 s01-s18 的工具 |
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| 权限 | 教学主体省略 | `PreToolUse` hook 中执行 |
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| hooks | 省略 | UserPromptSubmit / PreToolUse / PostToolUse / Stop |
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| todo | 省略 | `todo_write` + reminder |
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| skill | 省略 | catalog in system prompt + `load_skill` |
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| compact | 省略 | LLM 前压缩 + `compact` 工具 + reactive compact |
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| error recovery | 简化 try/except | retry / max_tokens / prompt too long |
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| background | 省略 | 慢操作后台线程 + task notification |
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| cron | 省略 | daemon scheduler + durable jobs |
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| multi-agent | 保留 | 保留；队友使用隔离目录下的基础工具 |
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| worktree | 保留 | 保留 |
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| MCP | 新增 | 保留，作为最终工具池的一部分 |
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---
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## 试一下
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```sh
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cd learn-claude-code
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python s20_comprehensive/code.py
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```
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可以试：
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1. `Create a todo list for inspecting this repo, then list Python files`
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2. `Connect to the docs MCP server and search for agent loop`
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3. `Create two tasks, create worktrees for them, then spawn alice and bob. Ask them to submit plans before claiming tasks.`
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4. `remind me of the meeting in 3 minutes.`
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5. `Run npm install in the background and continue reading README.md`
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观察重点：
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- 工具调用前是否经过 hooks/permission
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- `connect_mcp` 后下一轮是否出现 MCP 工具
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- 慢操作是否返回 background placeholder
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- 到点是不是自动提醒开会
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- 队友是否提交 plan，并在 approval 前暂停
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- plan 批准后，队友是否能认领任务
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- worktree 绑定后，队友是否切到对应目录
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---
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## 结束亦是开始
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从 s01 到 s20，代码表面越来越复杂，但核心始终没变：
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```python
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while True:
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response = LLM(messages, tools)
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if not has_tool_use(response.content):
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return
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results = execute_tools(response.content)
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messages.append(tool_results)
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```
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Claude Code 的复杂性不是“另一个 agent 大脑”，而是一个成熟 harness 的复杂性。模型负责判断和行动选择；harness 负责把环境、工具、权限、记忆、团队和外部能力组织好。
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这就是全书的终点：机制很多，循环一个。
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