Files
analysis_claude_code/docs/zh/s03-todo-write.md
gui-yue 1baf1aca5a Follow up PR #265: refine chapters, diagrams, and add S20 (#283)
* 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>
2026-05-20 21:45:38 +08:00

3.4 KiB

s03: TodoWrite (待办写入)

s01 > s02 > [ s03 ] s04 > s05 > s06 | s07 > s08 > s09 > s10 > s11 > s12

"没有计划的 agent 走哪算哪" -- 先列步骤再动手, 完成率翻倍。

Harness 层: 规划 -- 让模型不偏航, 但不替它画航线。

问题

多步任务中, 模型会丢失进度 -- 重复做过的事、跳步、跑偏。对话越长越严重: 工具结果不断填满上下文, 系统提示的影响力逐渐被稀释。一个 10 步重构可能做完 1-3 步就开始即兴发挥, 因为 4-10 步已经被挤出注意力了。

解决方案

+--------+      +-------+      +---------+
|  User  | ---> |  LLM  | ---> | Tools   |
| prompt |      |       |      | + todo  |
+--------+      +---+---+      +----+----+
                    ^                |
                    |   tool_result  |
                    +----------------+
                          |
              +-----------+-----------+
              | TodoManager state     |
              | [ ] task A            |
              | [>] task B  <- doing  |
              | [x] task C            |
              +-----------	------------+
                          |
              if rounds_since_todo >= 3:
                inject <reminder> into tool_result

工作原理

  1. TodoManager 存储带状态的项目。同一时间只允许一个 in_progress
class TodoManager:
    def update(self, items: list) -> str:
        validated, in_progress_count = [], 0
        for item in items:
            status = item.get("status", "pending")
            if status == "in_progress":
                in_progress_count += 1
            validated.append({"id": item["id"], "text": item["text"],
                              "status": status})
        if in_progress_count > 1:
            raise ValueError("Only one task can be in_progress")
        self.items = validated
        return self.render()
  1. todo 工具和其他工具一样加入 dispatch map。
TOOL_HANDLERS = {
    # ...base tools...
    "todo": lambda **kw: TODO.update(kw["items"]),
}
  1. nag reminder: 模型连续 3 轮以上不调用 todo 时注入提醒。
if rounds_since_todo >= 3 and messages:
    last = messages[-1]
    if last["role"] == "user" and isinstance(last.get("content"), list):
        last["content"].insert(0, {
            "type": "text",
            "text": "<reminder>Update your todos.</reminder>",
        })

"同时只能有一个 in_progress" 强制顺序聚焦。nag reminder 制造问责压力 -- 你不更新计划, 系统就追着你问。

相对 s02 的变更

组件 之前 (s02) 之后 (s03)
Tools 4 5 (+todo)
规划 带状态的 TodoManager
Nag 注入 3 轮后注入 <reminder>
Agent loop 简单分发 + rounds_since_todo 计数器

试一试

cd learn-claude-code
python agents/s03_todo_write.py

试试这些 prompt (英文 prompt 对 LLM 效果更好, 也可以用中文):

  1. Refactor the file hello.py: add type hints, docstrings, and a main guard
  2. Create a Python package with __init__.py, utils.py, and tests/test_utils.py
  3. Review all Python files and fix any style issues