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>

s01_agent_loop/code.py

@@ -0,0 +1,137 @@
+#!/usr/bin/env python3
+"""
+s01_agent_loop.py - The Agent Loop
+
+The entire secret of an AI coding agent in one pattern:
+
+    while stop_reason == "tool_use":
+        response = LLM(messages, tools)
+        execute tools
+        append results
+
+    +----------+      +-------+      +---------+
+    |   User   | ---> |  LLM  | ---> |  Tool   |
+    |  prompt  |      |       |      | execute |
+    +----------+      +---+---+      +----+----+
+                          ^               |
+                          |   tool_result |
+                          +---------------+
+                          (loop continues)
+
+This is the core loop: feed tool results back to the model
+until the model decides to stop. Production agents layer
+policy, hooks, and lifecycle controls on top.
+
+Usage:
+    pip install anthropic python-dotenv
+    ANTHROPIC_API_KEY=... python s01_agent_loop/code.py
+"""
+
+import os
+import subprocess
+
+try:
+    import readline
+    # macOS 的 libedit 在处理中文输入时有退格问题，这四行修复它
+    readline.parse_and_bind('set bind-tty-special-chars off')
+    readline.parse_and_bind('set input-meta on')
+    readline.parse_and_bind('set output-meta on')
+    readline.parse_and_bind('set convert-meta off')
+except ImportError:
+    pass
+
+from anthropic import Anthropic
+from dotenv import load_dotenv
+
+load_dotenv(override=True)
+
+if os.getenv("ANTHROPIC_BASE_URL"):
+    os.environ.pop("ANTHROPIC_AUTH_TOKEN", None)
+
+client = Anthropic(base_url=os.getenv("ANTHROPIC_BASE_URL"))
+MODEL = os.environ["MODEL_ID"]
+
+SYSTEM = f"You are a coding agent at {os.getcwd()}. Use bash to solve tasks. Act, don't explain."
+
+# ── Tool definition: just bash ────────────────────────────
+TOOLS = [{
+    "name": "bash",
+    "description": "Run a shell command.",
+    "input_schema": {
+        "type": "object",
+        "properties": {"command": {"type": "string"}},
+        "required": ["command"],
+    },
+}]
+
+
+# ── Tool execution ────────────────────────────────────────
+def run_bash(command: str) -> str:
+    dangerous = ["rm -rf /", "sudo", "shutdown", "reboot", "> /dev/"]
+    if any(d in command for d in dangerous):
+        return "Error: Dangerous command blocked"
+    try:
+        r = subprocess.run(command, shell=True, cwd=os.getcwd(),
+                           capture_output=True, text=True, timeout=120)
+        out = (r.stdout + r.stderr).strip()
+        return out[:50000] if out else "(no output)"
+    except subprocess.TimeoutExpired:
+        return "Error: Timeout (120s)"
+    except (FileNotFoundError, OSError) as e:
+        return f"Error: {e}"
+
+
+# ── The core pattern: a while loop that calls tools until the model stops ──
+def agent_loop(messages: list):
+    while True:
+        response = client.messages.create(
+            model=MODEL, system=SYSTEM, messages=messages,
+            tools=TOOLS, max_tokens=8000,
+        )
+
+        # Append assistant turn
+        messages.append({"role": "assistant", "content": response.content})
+
+        # If the model didn't call a tool, we're done
+        if response.stop_reason != "tool_use":
+            return
+
+        # Execute each tool call, collect results
+        results = []
+        for block in response.content:
+            if block.type == "tool_use":
+                print(f"\033[33m$ {block.input['command']}\033[0m")
+                output = run_bash(block.input["command"])
+                print(output[:200])
+                results.append({
+                    "type": "tool_result",
+                    "tool_use_id": block.id,
+                    "content": output,
+                })
+
+        # Feed tool results back, loop continues
+        messages.append({"role": "user", "content": results})
+
+
+# ── Entry point ──────────────────────────────────────────
+if __name__ == "__main__":
+    print("s01: Agent Loop")
+    print("输入问题，回车发送。输入 q 退出。\n")
+
+    history = []
+    while True:
+        try:
+            query = input("\033[36ms01 >> \033[0m")
+        except (EOFError, KeyboardInterrupt):
+            break
+        if query.strip().lower() in ("q", "exit", ""):
+            break
+        history.append({"role": "user", "content": query})
+        agent_loop(history)
+        # Print the model's final text response
+        response_content = history[-1]["content"]
+        if isinstance(response_content, list):
+            for block in response_content:
+                if getattr(block, "type", None) == "text":
+                    print(block.text)
+        print()