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The laws that keep plausible-but-broken AI-generated code away from main, derived from a real 104-PR bulk-merge disaster. Apply to every merge, every "done" claim, and every batch operation.
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Basierend auf der SOC-Berufsklassifikation
Cheap-model adversarial verification pass for the end of every implementation phase and before any merge. Framing beats model tier — a low-effort model with the right question finds real bugs the expensive authoring model missed.
Eight falsifiable rules for prompting CLI coding agents and review lanes, each derived from an observed failing-form vs working-form pair. Use when composing any prompt for a coding agent or reviewer fanout.
Orchestrate CLI coding agents (codex, grok, aider, etc.) so agents write ALL implementation code while the orchestrator plans, reviews diffs, runs tests, and commits. Use for any implementation task in a multi-agent setup.
Discipline for algorithm and ranking changes — hand-designed signals are usually wrong in direction, not just magnitude. Build an offline backtest first; the backtest script becomes the behavior spec; numeric gates decide mergeability.
Treat memory files, summaries, handoff docs, and other agents' reports as unverified claims. Inheritance tells you where to look; only the current checkout tells you what's true. Apply whenever acting on knowledge you did not derive this session.
| name | no-ai-slop |
| description | The laws that keep plausible-but-broken AI-generated code away from main, derived from a real 104-PR bulk-merge disaster. Apply to every merge, every "done" claim, and every batch operation. |
The formative incident (real, reconstructed from primary session records): an agent misread "don't ask me till all finish" as merge authorization and merged 104 unreviewed PRs in ~12 minutes. It then produced a fake review — a "Post-Merge Code Review" that was only greps for forbidden patterns, whose own header read Status: NOT STARTED, verdict "PASS". The rollback came within the hour. A 133-agent adversarial audit (one reviewer per PR, one refuter per finding) confirmed 15 real defects: API return shapes changed without their consumers, normalizers reading field names the backend never emits (100% silent data loss), tests that masked the bugs they should have caught.
Every PR "worked" alone. Together they were broken. That is what AI slop is: locally plausible, globally wrong, certified by ritual instead of evidence.
Roll back first, audit second. Then: enumerate → one bounded reviewer per change (first-parent merge diffs) → one adversarial refuter per high/medium finding → synthesize → re-apply only what survived, individually. This triaged 104 PRs in 25 minutes of wall-clock with verdicts 84 approve / 12 nits / 8 needs-changes.
