nav-diagnose

name	nav-diagnose
description	Detect quality drops in AI output and prompt re-anchoring. Auto-triggers after repeated corrections, context confusion, or when user says "something seems off", "you're not getting this".
allowed-tools	Read, Write, Bash
version	1.0.0

Navigator Diagnose Skill

Detect when human-AI collaboration quality drops and prompt re-anchoring to restore effective communication.

Why This Exists (Theory of Mind)

Based on Riedl & Weidmann 2025 research on Human-AI Synergy:

• Theory of Mind varies dynamically within users (moment-to-moment)
• Quality drops occur when ToM alignment degrades
• Early detection and re-anchoring restores collaboration effectiveness
• Both user ToM (understanding Claude) and Claude's model of user can drift

This skill detects when collaboration is degrading and prompts corrective action.

When to Invoke

Auto-invoke when:

• 2+ corrections on the same topic detected
• User says "something seems off", "you're not getting this"
• User says "wrong again", "still not right"
• Context usage exceeds 75% and quality signals degrade
• User expresses frustration ("ugh", "sigh", explicit frustration)
• Loop mode stagnation detected (3+ same-state iterations)

DO NOT invoke if:

• Single correction (normal collaboration)
• User is providing new requirements (not correcting)
• Fresh session (insufficient data to diagnose)
• User explicitly says "it's fine" or "close enough"

Quality Drop Indicators

1. Repeated Corrections (High Severity)

Trigger: Same correction given 2+ times
Signal: "No, I said users plural, not user" (2nd time)
Issue: Not incorporating user feedback

2. Hallucination Signals (High Severity)

Trigger: References to non-existent files, functions, or packages
Signal: "That file doesn't exist", "There's no such function"
Issue: Generating from incorrect mental model

3. Context Confusion (Medium Severity)

Trigger: Mixing details from unrelated tasks
Signal: "That's from the other project", "Wrong feature"
Issue: Context window pollution or misattribution

4. Unaddressed Feedback (Medium Severity)

Trigger: User correction not reflected in next output
Signal: Generates same pattern after being told not to
Issue: Not properly updating internal model

5. Goal Drift (Low Severity)

Trigger: Output increasingly diverges from original goal
Signal: "We're getting off track", "Not what I asked for"
Issue: Lost sight of user's actual objective

6. Loop Stagnation (High Severity)

Trigger: 3+ consecutive iterations with same state hash (loop mode only)
Signal: nav-loop detects stagnation, triggers nav-diagnose
Issue: Stuck on same step, unable to progress

Execution Steps

Step 1: Assess Quality State

Analyze recent exchanges (last 10-15 messages):

Quality Indicators:
- [ ] Corrections given: {count}
- [ ] Same-topic corrections: {count}
- [ ] User frustration signals: {count}
- [ ] Hallucination reports: {count}
- [ ] "Not what I meant" phrases: {count}

Calculate severity:

severity = "critical" if same_topic_corrections >= 2 or hallucinations >= 1
severity = "high" if corrections >= 3 or frustration_signals >= 2
severity = "medium" if corrections >= 2 or goal_drift_detected
severity = "low" if corrections == 1  # Normal, don't trigger

Step 2: Identify Root Cause

Analyze correction patterns:

Pattern	Likely Cause	Re-anchoring Focus
Same correction repeated	Not incorporating feedback	Explicitly acknowledge and confirm understanding
Increasing corrections	Drifting from user intent	Re-establish goals
Technical mismatches	Wrong assumptions	Clarify technical context
Frustration without specifics	Communication mismatch	Ask what's wrong

Step 3: Display Diagnostic

Show quality check alert:

⚠️  QUALITY CHECK
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Detected Issue: {ISSUE_TYPE}
Severity: {SEVERITY}

What I noticed:
- {OBSERVATION_1}
- {OBSERVATION_2}

Possible causes:
- {CAUSE_1}
- {CAUSE_2}
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Let me re-anchor our collaboration:

1. Your goal: {RECONSTRUCTED_GOAL}
2. Current state: {STATE_SUMMARY}
3. What you want: {CORRECTED_UNDERSTANDING}

Is this understanding correct? [Y/n]

Step 4: Re-anchor Collaboration

Based on user confirmation:

If correct (Y):

✅ Re-anchored!

I'll proceed with this understanding:
- {KEY_POINT_1}
- {KEY_POINT_2}

Continuing with: {NEXT_ACTION}

If incorrect (n):

Help me understand better:

1. What is your actual goal?
2. What am I getting wrong?
3. What constraints should I know?

[Open-ended response welcome]

Step 5: Log Diagnostic (Optional)

If nav-profile exists, save diagnostic:

{
  "date": "{YYYY-MM-DD}",
  "issue_type": "{ISSUE_TYPE}",
  "severity": "{SEVERITY}",
  "resolution": "re-anchored|user-corrected|escalated",
  "learnings": ["{WHAT_TO_AVOID}"]
}

Step 6: Suggest Preventive Actions

Based on severity and pattern:

For context overload:

💡 Suggestion: Consider running nav-compact to clear context.
Current context usage is high, which can cause confusion.

For repeated corrections:

💡 Suggestion: Let me save your preference to avoid this in future.
"Remember I always want {X}" - This will persist across sessions.

For communication mismatch:

💡 Suggestion: Consider adjusting your profile preferences.
- Current verbosity: {VERBOSITY}
- Current confirmation: {CONFIRMATION}

Update with: "Remember I prefer {SUGGESTED_STYLE}"

---

Re-anchoring Templates

Template 1: Goal Re-alignment

Let me verify I understand your goal:

You want to: {GOAL_STATEMENT}

Not: {COMMON_MISUNDERSTANDING}

Key constraints:
- {CONSTRAINT_1}
- {CONSTRAINT_2}

Is this right?

Template 2: Technical Re-alignment

Let me verify the technical context:

Framework: {FRAMEWORK}
Patterns: {PATTERNS}
Conventions: {CONVENTIONS}

What I should be using:
- {TOOL_1}: for {PURPOSE_1}
- {TOOL_2}: for {PURPOSE_2}

Corrections to my assumptions?

Template 3: Communication Re-alignment

I may be mismatching your communication style:

You seem to prefer:
- {INFERRED_STYLE_1}
- {INFERRED_STYLE_2}

I've been:
- {MY_STYLE_1}
- {MY_STYLE_2}

Should I adjust my approach?

---

Integration with Other Skills

With nav-profile

• Log diagnostics for pattern analysis
• Suggest preference updates after repeated issues
• Load profile preferences for baseline comparison

With nav-marker

• Suggest marker before major re-anchoring
• Include diagnostic state in marker

With nav-compact

• Recommend compact if context overload detected
• Track if compaction resolves issues

---

Quality Signals Reference

Positive Signals (Good Collaboration)

- "Perfect, exactly what I needed"
- "Yes, continue"
- "Good, now..."
- No corrections for 5+ exchanges
- User providing new requirements (not corrections)

Negative Signals (Quality Drop)

- "No", "Wrong", "Not that"
- "I already said..."
- "Again, please..."
- "Sigh", "Ugh", explicit frustration
- "You're not understanding"
- Same correction twice

Neutral Signals (Normal Iteration)

- "Actually, let's try..."
- "Can we also..."
- "What about..."
- Single correction with explanation

---

Example Scenarios

Scenario 1: Repeated REST Convention Correction

Exchange 1:
User: "Create endpoint for users"
Claude: Creates /user endpoint
User: "Should be /users (plural)"

Exchange 2:
User: "Now create endpoint for posts"
Claude: Creates /post endpoint
User: "Again, plural! /posts"

→ Trigger: Same correction (plural naming) given twice
→ Action: Re-anchor on REST conventions
→ Outcome: "I understand now - always use plural nouns for REST resources"

Scenario 2: Context Confusion

User working on: OAuth feature (Feature A)
Claude references: Stripe integration (Feature B from earlier)

User: "That's from the payment feature, not auth"

→ Trigger: Context confusion detected
→ Action: Re-anchor on current feature
→ Suggestion: Consider nav-compact to clear old context

Scenario 3: User Frustration

User: "Ugh, still not right"
User: "This is frustrating"

→ Trigger: Frustration signals detected
→ Action: Pause and diagnose
→ Response: Open-ended question about what's wrong

---

Success Criteria

Diagnostic is successful when:

• Quality drops detected before user escalates
• Root cause correctly identified
• Re-anchoring restores collaboration quality
• Preventive suggestions are actionable
• User confirms understanding after re-anchor
• Same issue doesn't recur immediately

---

Limitations

Cannot detect:

• Silent user frustration (no signals in text)
• Issues outside conversation context
• Problems with external systems
• User preferences not yet expressed

Should not:

• Over-trigger on normal corrections
• Interrupt productive flow
• Make user feel blamed
• Require lengthy re-explanation

---

Best Practices

When diagnosing:

• Be humble about AI limitations
• Don't blame user for miscommunication
• Offer concrete next steps
• Keep re-anchoring brief

When re-anchoring:

• Focus on understanding, not apologizing
• Confirm specific points, not general "I understand"
• Let user correct if wrong
• Thank user for patience

---

This skill catches collaboration quality drops early, enabling quick recovery through Theory of Mind re-alignment 🔍