boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/boundary-risk-assessContext preview
What this command does when you run it.
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
tools: - read - write - grep arguments: $CONTEXT
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Based on the WFGY project: https://github.com/onestardao/WFGY
1. **Gather Risk Indicators**
2. **Analyze Risk Factors**
3. **Calculate Risk Scores**
Overall Risk = W₁*Semantic + W₂*Logic + W₃*Confidence + W₄*Systemic Where weights: W₁ = 0.35 (semantic weight) W₂ = 0.30 (logic weight) W₃ = 0.25 (confidence weight) W₄ = 0.10 (systemic weight)
4. **Identify Risk Patterns**
5. **Generate Risk Mitigation Plan**
BOUNDARY RISK ASSESSMENT ═══════════════════════════════════════ Context: "$CONTEXT" Assessment Time: [timestamp] RISK LEVEL: [LOW|MODERATE|HIGH|CRITICAL] ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [▓▓▓▓▓▓▓▓░░░░░░░░░░░░] 40% Risk Risk Breakdown: ┌─────────────────────────────────────┐ │ Category │ Score │ Status │ ├─────────────────────────────────────┤ │ Semantic │ 0.45 │ ⚠️ Moderate │ │ Logic │ 0.22 │ ✓ Low │ │ Confidence │ 0.38 │ ⚠️ Moderate │ │ Systemic │ 0.15 │ ✓ Low │ │─────────────────────────────────────│ │ Overall │ 0.35 │ ⚠️ MODERATE │ └─────────────────────────────────────┘ Risk Indicators Detected: ⚠️ Semantic tension increasing (ΔS: 0.42 → 0.61) ⚠️ Approaching knowledge boundary ✓ Logic coherence maintained ⚠️ Confidence degrading (82% → 67%) ✓ System modules stable Trend Analysis: - Risk Direction: [Increasing/Stable/Decreasing] - Rate of Change: [value]/hour - Projected Risk (1hr): [value]% - Time to Danger Zone: [time estimate] Historical Patterns: • Similar risk at: [previous occurrence] • Previous outcome: [success/failure] • Recovery method: [what worked] Mitigation Strategies: Priority 1: [Immediate action] - Execute: /boundary:safe-bridge - Reduce semantic tension - Estimated risk reduction: -15% Priority 2: [Preventive measure] - Apply: /wfgy:bbcr - Reset to stable state - Estimated risk reduction: -25% Priority 3: [Fallback option] - Return to checkpoint - Abandon current path - Estimated risk reduction: -40% Safe Zones Nearby: 1. [Concept/Topic] - ΔS: 0.3 from current 2. [Concept/Topic] - ΔS: 0.4 from current 3. [Concept/Topic] - ΔS: 0.5 from current Recommendation: [CONTINUE with caution|PAUSE for bridging|RETREAT to safety] Auto-triggers: □ BBCR: Armed (triggers at risk > 0.75) □ Checkpoint: Ready (auto-save at risk > 0.6) □ Alert: Set (warning at risk > 0.5)
Continuous risk monitoring includes:
Risk assessment works with:
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Analyze semantic position relative to knowledge boundaries to prevent hallucination and identify uncertainty zones.
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles:…