fix(M7-27+capstone): apply AI-drives-git reframe, lesson=theory, de-slop course-wide

Phase 2 sweep — all modules are post-pivot, so the learner directs the AI agent
(Claude Code as the worked example) to do the git/setup work and verifies, instead
of typing commands by hand; no re-teaching basics. Lesson sections are theory with
example output; all execution lives in the labs. De-slopped ("prose" etc. gone
course-wide, em-dash density thinned). /path/to placeholders -> ~/ai-workflow-course.

Every deliberate teaching device verified intact: M10 ai-change.patch trap,
M12 bad-clear-snippet, M13/M27 planted pending_count bug, M15 secret+typosquat+MD5,
M18 BREAK=1, M21 absent-.gitignore, M22 poisoned skill, M24 no-op patch, M25 --simulate.
Labs compile/parse (py/sh/yaml/json); no junk.

Closes #83
Closes #86
Closes #89

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
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2026-06-22 21:58:17 -04:00
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"""Cloud-sync config for tasks-app — a realistic snapshot of what an AI hands you.
Asked to "sync tasks to a cloud service," a model will cheerfully produce something like this: it
works, it reads naturally, it passes lint and tests... and it carries two planted flaws a live
credential baked straight into the source (caught by Gate 2, secret scanning) and a weak-crypto
"signature" using MD5 (caught by Gate 3, SAST). Two different gates, two different blind spots.
Asked to "sync tasks to a cloud service," a model will produce something like this: it works, it
reads naturally, it passes lint and tests... and it carries two planted flaws: a live credential
baked straight into the source (caught by Gate 2, secret scanning) and a weak-crypto "signature"
using MD5 (caught by Gate 3, SAST). Two different gates, two different blind spots.
DO NOT copy these patterns. The point of this file is to be caught by a scanner, not imitated.
The fix (read from the environment) is shown at the bottom, commented out, so you can see the