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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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gui-yue
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# s07: Skill Loading — 用到的时候才加载
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[中文](README.md) · [English](README.en.md) · [日本語](README.ja.md)
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s01 → s02 → s03 → s04 → s05 → s06 → `s07` → [s08](../s08_context_compact/) → s09 → ... → s20
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> *"用到时再加载, 别全塞 prompt 里"* — 通过 tool_result 注入, 不塞 system prompt。
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>
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> **Harness 层**: 知识 — 按需加载, 不堆满上下文。
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---
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## 问题
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你的项目有一套 React 组件规范、一份 SQL 风格指南、一份 API 设计文档。你希望 Agent 自动遵守这些规范。最直接的想法，全塞进 system prompt：
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```python
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SYSTEM = (
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f"You are a coding agent. "
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+ open("docs/react-style.md").read() # 2000 行
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+ open("docs/sql-style.md").read() # 1500 行
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+ open("docs/api-design.md").read() # 3000 行
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)
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```
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6500 行 system prompt。Agent 每次调用 LLM 都带着这些文档——不管是在改 CSS 颜色还是修 SQL 查询。99% 的内容和当前任务无关，白白消耗 token。
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---
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## 解决方案
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保留上一章的最小 hook 结构、`todo_write` 和子 Agent，本章重点转向新增的 `load_skill` 工具。启动时把技能目录注入 SYSTEM prompt，运行时多注册一个工具加载完整内容，用到才花 token。
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两层设计：
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| 层 | 位置 | 时机 | 代价 |
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|---|------|------|------|
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| 1. 目录 | system prompt | 启动时注入（harness 扫描 skills/） | ~100 tokens/skill，每轮都带 |
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| 2. 内容 | tool_result | Agent 调用 load_skill 时 | ~2000 tokens/skill，按需 |
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dispatch 机制不变，load_skill 通过 `TOOL_HANDLERS[block.name]` 分发。
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---
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## 工作原理
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**skills/ 目录**，每个技能一个子目录，包含 `SKILL.md` 文件：
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```
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skills/
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agent-builder/SKILL.md
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code-review/SKILL.md
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mcp-builder/SKILL.md
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pdf/SKILL.md
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```
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**第一级：启动时注入目录**：harness 启动时调用 `_scan_skills()` 扫描 skills/ 目录，解析每个 SKILL.md 的 YAML frontmatter（`name`、`description`），存入 `SKILL_REGISTRY` 字典。`list_skills()` 从注册表生成目录，注入 SYSTEM prompt。Agent 每轮都能看到"我有哪些技能可用"，不花额外 API 调用：
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```python
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SKILL_REGISTRY: dict[str, dict] = {}
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def _scan_skills():
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if not SKILLS_DIR.exists():
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return
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for d in sorted(SKILLS_DIR.iterdir()):
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if not d.is_dir():
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continue
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manifest = d / "SKILL.md"
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if manifest.exists():
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raw = manifest.read_text()
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meta, body = _parse_frontmatter(raw)
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name = meta.get("name", d.name)
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desc = meta.get("description", raw.split("\n")[0].lstrip("#").strip())
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SKILL_REGISTRY[name] = {"name": name, "description": desc, "content": raw}
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_scan_skills() # runs once at startup
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def list_skills() -> str:
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return "\n".join(f"- **{s['name']}**: {s['description']}" for s in SKILL_REGISTRY.values())
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def build_system() -> str:
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catalog = list_skills()
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return (
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f"You are a coding agent at {WORKDIR}. "
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f"Skills available:\n{catalog}\n"
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"Use load_skill to get full details when needed."
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)
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SYSTEM = build_system()
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```
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**第二级：load_skill**：Agent 决定"我需要 SQL 风格指南"，调用 `load_skill("sql-style")`。通过注册表查找，不走文件路径，没有路径遍历风险。内容通过 `tool_result` 注入：
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```python
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def load_skill(name: str) -> str:
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skill = SKILL_REGISTRY.get(name)
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if not skill:
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return f"Skill not found: {name}"
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return skill["content"]
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```
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关键区别：技能内容不是 system prompt 的一部分，它作为一次工具结果进入当前 messages。后续调用会随历史一起携带，直到上下文压缩、截断或会话结束。这和 s08 的 compact 自然衔接：按需加载解决了"不该提前带的不要带"，compact 解决"该丢的怎么丢"。
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---
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## 相对 s06 的变更
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| 组件 | 之前 (s06) | 之后 (s07) |
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|------|-----------|-----------|
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| 工具数量 | 7 (bash, read, write, edit, glob, todo_write, task) | 8 (+load_skill) |
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| 知识加载 | 无 | 两级：启动时目录注入 SYSTEM + 运行时 load_skill |
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| SYSTEM 提示 | 静态字符串 | 启动时扫描 skills/ 注入目录 |
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| 技能注册表 | 无 | SKILL_REGISTRY（启动时填充，防路径遍历） |
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| 循环 | 不变 | 不变（skill 工具自动分发） |
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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 s07_skill_loading/code.py
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```
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试试这些 prompt：
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1. `What skills are available?`
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2. `Load the code-review skill and follow its instructions`
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3. `I need to do a code review -- load the relevant skill first`
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观察重点：Agent 是否直接从 SYSTEM 里的目录知道有哪些技能？需要完整规范时是否出现 `[HOOK] load_skill`？加载后回答是否使用了对应 skill 的说明？
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---
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## 接下来
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按需加载解决了"不该带的不要带"。但另一个问题来了：Agent 连续工作 30 分钟后，messages 列表塞满了中间过程。旧的 tool_result、过时的文件内容，占着上下文但不产生价值。
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s08 Context Compact → 四层压缩策略。便宜的先跑，贵的后跑。
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<details>
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<summary>深入 CC 源码</summary>
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> 以下基于 CC 源码 `loadSkillsDir.ts`、`SkillTool.ts`、`bundledSkills.ts`、`commands.ts` 的分析。
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### 一、技能来源：不是只有一个 skills/ 目录
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教学版假设所有技能在 `skills/` 目录下。CC 实际从多个来源加载，分布在多个文件中：`loadSkillsDir.ts` 负责从 user/project/`--add-dir` 目录和 legacy commands（`.claude/commands/`）加载；`bundledSkills.ts` 负责内置技能；`SkillTool.ts` 处理 MCP 远程技能；`commands.ts` 负责命令聚合。类型包括 managed/policy skills、user skills（`~/.claude/skills/`）、project skills（`.claude/skills/`）、`--add-dir` skills、legacy commands、dynamic skills、conditional skills（带 `paths` frontmatter，按文件路径激活）、bundled skills、plugin skills、MCP skills。
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### 二、SKILL.md Frontmatter 常见字段
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CC 的 SKILL.md YAML frontmatter 由 `parseSkillFrontmatterFields()` 解析（`loadSkillsDir.ts`），常见字段包括：
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| 字段 | 用途 |
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|------|------|
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| `name` / `description` | 显示名称和描述 |
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| `when_to_use` | 指导模型何时调用 |
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| `allowed-tools` | 技能可用工具的自动允许列表 |
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| `context` | `inline`（默认）或 `fork`（作为子 Agent 运行） |
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| `model` | 模型覆盖（haiku/sonnet/opus/inherit） |
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| `hooks` | 技能级别的 hook 配置 |
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| `paths` | 条件激活的 glob 模式 |
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| `user-invocable` | 用户可以通过 `/name` 调用 |
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完整字段列表随版本迭代会变化，以上仅列出教学版涉及的核心字段。
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### 三、两级加载的精确实现
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1. **Catalog（启动时）**：`getSkillDirCommands()` 扫描目录 → 注册为 `Command` 对象，只包含元数据。`getSkillListingAttachments()` 把技能列表格式化为附件，预算为上下文窗口的 ~1%（上限 8000 字符）。
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2. **Load（调用时）**：模型调 `Skill` 工具（输入字段是 `skill` + 可选 `args`，教学版用 `name`）→ `getPromptForCommand()` 展开完整 SKILL.md 内容 → `SkillTool` 返回的 tool_result 展示文本只是 `"Launching skill: {name}"`，真正的技能内容通过 `newMessages` 注入对话。教学版把两者合并为"通过 tool_result 注入"是一种简化。
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### 教学版的简化是刻意的
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- 多文件多来源 → 1 个 `skills/` 目录：足以展示两级加载的核心概念
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- 多个 frontmatter 字段 → 只解析 name/description：减少解析复杂度
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- forked skills（`context: 'fork'`）→ 省略：教学版只展开 inline 技能加载
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- `Skill` 工具输入 `skill`+`args` → 教学版用 `name`：避免参数解析的额外复杂度
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</details>
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<!-- translation-sync: zh@v1, en@v1, ja@v1 -->
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