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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@@ -1,9 +1,9 @@
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"""Cloud-sync config for tasks-app — a realistic snapshot of what an AI hands you.
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Asked to "sync tasks to a cloud service," a model will cheerfully produce something like this: it
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works, it reads naturally, it passes lint and tests... and it carries two planted flaws — a live
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credential baked straight into the source (caught by Gate 2, secret scanning) and a weak-crypto
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"signature" using MD5 (caught by Gate 3, SAST). Two different gates, two different blind spots.
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Asked to "sync tasks to a cloud service," a model will produce something like this: it works, it
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reads naturally, it passes lint and tests... and it carries two planted flaws: a live credential
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baked straight into the source (caught by Gate 2, secret scanning) and a weak-crypto "signature"
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using MD5 (caught by Gate 3, SAST). Two different gates, two different blind spots.
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DO NOT copy these patterns. The point of this file is to be caught by a scanner, not imitated.
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The fix (read from the environment) is shown at the bottom, commented out, so you can see the
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