feat(course): build out all 27 modules, capstone, scaffold, and conventions

Scaffold the course repo and author the full curriculum in dependency-chain
order, following the settled build decisions in handoff.md.

- Scaffold: course README, vendor-neutral AGENTS.md (dogfoods Module 5),
  _TEMPLATE.md (the fixed 9-section module shape), root .gitignore, ship config.
- Modules 1-2: reference exemplars (locked for tone/depth/lab style).
- Modules 3-27: full lessons + runnable labs, each following the template,
  respecting the chain, vendor/model-agnostic, with "feel the pain" labs.
- Module 8 hosting comparison web-researched and date-stamped (as of 2026-06-22),
  not written from memory; expansion-zone modules carry Verify-before-publish.
- Capstone: the full loop end to end on the running tasks-app example.

Lab code syntax-checked (Python/shell/YAML); every module has the 7 core
template sections.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT

modules/20-mcp-servers-giving-the-ai-hands/lab/mcp-config-example.json

@@ -0,0 +1,9 @@
+{
+  "_comment": "Common shape of an MCP server entry for a local (stdio) server. Many agentic tools accept this 'mcpServers' map; yours may use a different key or location (check its docs). Replace the path with the ABSOLUTE path to tasks_mcp_server.py in your tasks-app. Use 'python3' instead of 'python' if that's what your system calls it, or the full path to a virtualenv's python.",
+  "mcpServers": {
+    "tasks": {
+      "command": "python",
+      "args": ["/ABSOLUTE/PATH/TO/workflow-course/tasks-app/tasks_mcp_server.py"]
+    }
+  }
+}

modules/20-mcp-servers-giving-the-ai-hands/lab/tasks_mcp_server.py

@@ -0,0 +1,65 @@
+"""A tiny MCP server that gives an AI client hands on the tasks-app.
+
+It exposes the tasks-app over the Model Context Protocol (MCP) so an agentic tool can read and
+change your real task list directly — no copy-paste, no pasting tasks.json into a chat window.
+
+The whole server is the decorated functions below. FastMCP (from the official Python SDK) turns
+each `@mcp.tool()` function into a tool the AI client can discover and call. That's it — a tool is
+a normal Python function plus a docstring the client reads to know what it does.
+
+Setup (once):
+    pip install "mcp[cli]"
+
+Drop this file into your tasks-app folder, next to tasks.py and cli.py (it reuses them, and shares
+the same tasks.json — so a task the AI adds through this server shows up in `python cli.py list`).
+
+Sanity-check that it starts (it will sit waiting for a client to talk to it; Ctrl-C to stop):
+    python tasks_mcp_server.py
+
+You don't normally run it by hand, though. Your agentic tool launches it for you — see the lab.
+"""
+
+import json
+from pathlib import Path
+
+from mcp.server.fastmcp import FastMCP
+
+from tasks import Task, TaskList
+
+STATE = Path(__file__).parent / "tasks.json"
+
+# The name is how the server identifies itself to the client.
+mcp = FastMCP("tasks")
+
+
+def _load() -> TaskList:
+    if not STATE.exists():
+        return TaskList()
+    raw = json.loads(STATE.read_text())
+    return TaskList(tasks=[Task(**t) for t in raw])
+
+
+def _save(tlist: TaskList) -> None:
+    STATE.write_text(json.dumps([t.__dict__ for t in tlist.tasks], indent=2))
+
+
+@mcp.tool()
+def list_tasks() -> str:
+    """List every task in the tasks-app, with its index and whether it's done."""
+    return _load().render()
+
+
+@mcp.tool()
+def add_task(title: str) -> str:
+    """Add a new task to the tasks-app. `title` is the text of the task to add."""
+    tlist = _load()
+    tlist.add(title)
+    _save(tlist)
+    return f"added: {title}"
+
+
+if __name__ == "__main__":
+    # stdio transport by default: the client launches this process and talks to it over
+    # stdin/stdout. That's why the server "just sits there" when you run it by hand — it's
+    # waiting for a client on the other end of the pipe.
+    mcp.run()