ai-debug

AI Debug

Figure out why an existing AI feature is broken.

Works with:

• Linear MCP - Pull issue/bug details
• Manual - Describe the symptoms

Entry Point

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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Usage

/ai-debug                 # Describe symptoms manually
/ai-debug LIN-123         # Start from Linear bug/issue

What It Does

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.

The 4D Audit

D1: Was the Job Defined?

• Can you articulate exactly what the model should produce?
• Is there a written spec for inputs, outputs, constraints?
• Do engineers and PMs agree on what "good" looks like?

D2: Is Context Right?

• What context is the model actually receiving?
• Walk through the 6 layers: Intent, User, Domain, Rules, Environment, Exposition
• Is context structured or dumped as raw text?
• Is there too much context (token bloat)?

D3: Is Context Fetched Reliably?

• How is each piece of context being fetched at runtime?
• What happens when a data source is unavailable?
• Is there visibility into what context is used per request?

D4: Are Failures Being Caught?

• Are there pre-checks before calling the model?
• Are there post-checks validating output?
• What's the fallback UX when things break?
• Is there a feedback loop capturing failures?

Output

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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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Workflow

1. Collect symptoms (what's going wrong)
2. Map symptoms to likely causes using the table above
3. Audit each D dimension with diagnostic questions
4. Identify root cause and prioritize fixes
5. Offer to add findings to Linear or export

Questions to ask at each step:

• "What specific behavior are you seeing?"
• "What should it be doing instead?"
• "When did this start happening?"
• "Does it happen every time or intermittently?"

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Framework: 4D Context Canvas (Aakash Gupta & Miqdad Jaffer) Best for: Debugging hallucinations, inconsistency, performance issues in AI features