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Diagnose why an AI feature is underperforming, hallucinating, or behaving inconsistently. Uses 4D audit to work backwards from symptoms to root cause.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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| name | ai-debug |
| description | Diagnose why an AI feature is underperforming, hallucinating, or behaving inconsistently. Uses 4D audit to work backwards from symptoms to root cause. |
Figure out why an existing AI feature is broken.
Works with:
When this skill is invoked, start with:
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AI DEBUG
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When AI fails, teams blame the model.
But 90% of failures are context failures.
What's going wrong?
1. Provide a Linear issue ID
2. Describe the symptoms
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/ai-debug # Describe symptoms manually
/ai-debug LIN-123 # Start from Linear bug/issue
Works backwards from symptoms to root cause using the 4D audit:
| Symptom | Likely Root Cause | Focus Area |
|---|---|---|
| Hallucinations | Missing domain context, no grounding | D2, D4 |
| Inconsistency | Vague job definition, missing rules | D1, D4 |
| Generic outputs | Missing user/environment context | D2 |
| Wrong tone/format | Missing constraints, no examples | D1, D4 |
| Slow responses | Too much context, bad discovery | D2, D3 |
| High costs | Dumping everything in prompt | D2, D3 |
| Demo vs prod mismatch | Discovery strategy broken | D3, D4 |
Key insight: When AI fails, teams blame the model. But 90% of failures are context failures.
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CONTEXT AUDIT COMPLETE
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Feature: [Name]
Symptoms: [What was reported]
D1 Demand: [CLEAR / GAP / CRITICAL]
D2 Data: [CLEAR / GAP / CRITICAL]
D3 Discovery: [CLEAR / GAP / CRITICAL]
D4 Defense: [CLEAR / GAP / CRITICAL]
Primary Issue: [Root cause summary]
RECOMMENDED FIXES (prioritized):
1. [Highest impact fix]
2. [Second fix]
3. [Third fix]
Quick Win: [Smallest change that would help]
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Questions to ask at each step:
Framework: 4D Context Canvas (Aakash Gupta & Miqdad Jaffer) Best for: Debugging hallucinations, inconsistency, performance issues in AI features