style(no-slop): remove every em-dash + banned words across all modules + capstone
Apply the no-ai-slop standard (now binding in AGENTS.md): the em-dash character is banned outright (restructured, not blind-replaced), plus the banned word/phrase list (delve, leverage, robust, seamless, truly, unlock, etc.). 0 em-dashes remain in modules + capstone; the only "robust" left is the planted M10 ai-change.patch trap. Module H1 titles use a colon separator. All deliberate teaching devices preserved; labs compile/parse (py/sh/yaml/json); no junk. AGENTS.md updated with the hard no-slop rules. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01TfzV5QvtPDz8LJS3Pu5VLT
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@@ -1,15 +1,15 @@
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"""Assistive AI reviewer — local simulation of a PR-reviewer bot.
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"""Assistive AI reviewer: local simulation of a PR-reviewer bot.
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This stands in for a forge-native reviewer (an app/bot triggered when a PR opens, running on a
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runner from Module 19) without needing any hosted account. It does the two deterministic halves of
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the job and leaves the one judgment call — what actually happens to the PR — to you.
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the job and leaves the one judgment call (what actually happens to the PR) to you.
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python reviewer.py prompt # assemble the prompt: rubric + diff, for the agent to review
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python reviewer.py apply ai-review.sample.json # ingest the agent's JSON, render it, gate it
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The point of this module: the agent produces comments and a recommendation. It never approves,
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never requests-changes-as-a-gate, never merges. The `apply` step ends at a HUMAN DECISION, every
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time. Stdlib only — no pip install.
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time. Stdlib only, no pip install.
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"""
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import argparse
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@@ -68,7 +68,7 @@ def cmd_apply(args: argparse.Namespace) -> int:
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comments = review.get("comments", [])
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print("=" * 70)
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print("AI REVIEWER — first pass (advisory only)")
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print("AI REVIEWER: first pass (advisory only)")
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print("=" * 70)
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print(f"\nSummary: {summary}\n")
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