Use python3 as the canonical command name course-wide (#104)

Most current systems (default Debian/Ubuntu, recent macOS) install Python
only as `python3`, with no bare `python` on PATH, so learners who copied
`python cli.py ...` into their host shell hit "command not found".

Convert host-shell `python <cmd>` -> `python3 <cmd>` across module/lab
READMEs, lab `.py` docstrings & usage strings, blog posts, lab prompt and
instruction files, the M04 verify.sh message, and the M10/M24 lab patches.
Module 01's convention note (and its blog/02 mirror) is rewritten so
`python3` is canonical and `python` is the documented fallback.

Stop-lines respected: Docker image tags (`python:3.12-slim`), `.venv/.../python`
and `...\.venv\Scripts\python.exe` paths, the M20 `"command": "python"`
teaching example and surrounding venv prose, container-internal invocations
(M16/M18 Dockerfiles, M16 README `docker run` examples), and CI-workflow
`run:` steps fed by `actions/setup-python` / `image: python:3.12` are left
as `python` on purpose.

pip was left out of scope: most occurrences are prose or CI/container-internal,
and `pip3` does not fix the PEP 668 externally-managed-environment refusal that
the course already addresses with venvs. The M01 note is worded to stay
consistent with bare `pip` (use whichever pip pairs with your Python).

Build (tools/build_wiki.py) and tools/check.sh both pass.

Closes #104

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01GAEzanEoGJT5o1VizQar47

modules/24-assistive-agents/lab/feature.patch

@@ -6,8 +6,8 @@ index 91e9276..b2c4f1a 100644
  def main(argv: list[str]) -> int:
      tlist = load()
      if not argv:
--        print("usage: python cli.py [add <title> | list | done <index>]")
-+        print("usage: python cli.py [add <title> | list | done <index> | clear]")
+-        print("usage: python3 cli.py [add <title> | list | done <index>]")
++        print("usage: python3 cli.py [add <title> | list | done <index> | clear]")
          return 1
 
      command = argv[0]

modules/24-assistive-agents/lab/reviewer.py

@@ -4,8 +4,8 @@ This stands in for a forge-native reviewer (an app/bot triggered when a PR opens
 runner from Module 19) without needing any hosted account. It does the two deterministic halves of
 the job and leaves the one judgment call (what actually happens to the PR) to you.
 
-    python reviewer.py prompt              # assemble the prompt: rubric + diff, for the agent to review
-    python reviewer.py apply ai-review.sample.json   # ingest the agent's JSON, render it, gate it
+    python3 reviewer.py prompt              # assemble the prompt: rubric + diff, for the agent to review
+    python3 reviewer.py apply ai-review.sample.json   # ingest the agent's JSON, render it, gate it
 
 The point of this module: the agent produces comments and a recommendation. It never approves,
 never requests-changes-as-a-gate, never merges. The `apply` step ends at a HUMAN DECISION, every

modules/24-assistive-agents/lab/sample-issue.md

@@ -1,12 +1,12 @@
 Title: `done` command crashes on an empty list
 
-When I run `python cli.py done 0` right after a fresh checkout, before adding any tasks, it throws
+When I run `python3 cli.py done 0` right after a fresh checkout, before adding any tasks, it throws
 an IndexError and dumps a stack trace instead of a friendly message. Every other command handles the
 empty-list case fine, so this one feels like an oversight.
 
 Steps to reproduce:
 1. Delete tasks.json (or clone fresh).
-2. Run `python cli.py done 0`.
+2. Run `python3 cli.py done 0`.
 3. See the traceback.
 
 Expected: a clear message like "no task at index 0", exit non-zero, no traceback.

modules/24-assistive-agents/lab/triage.py

@@ -4,8 +4,8 @@ Stands in for a forge-native triage agent (triggered when an issue opens) withou
 It assembles the prompt, then validates and renders the AI's suggestion, and stops at a human
 confirm. The agent proposes labels and a route; it does not apply them.
 
-    python triage.py prompt                          # taxonomy + issue -> prompt for the agent
-    python triage.py apply ai-triage.sample.json     # validate + render + confirm gate
+    python3 triage.py prompt                          # taxonomy + issue -> prompt for the agent
+    python3 triage.py apply ai-triage.sample.json     # validate + render + confirm gate
 
 The validation step matters: the agent may only use labels that exist in label-taxonomy.md. A
 hallucinated label is rejected. Stdlib only, no pip install.