boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Analyze the logic flow direction (λ_observe) to understand reasoning patterns and identify potential issues.
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
/reasoning-logic-vectorContext preview
What this command does when you run it.
Analyze the logic flow direction (λ_observe) to understand reasoning patterns and identify potential issues.
tools: - read - write - grep arguments: $ANALYSIS_CONTEXT
Analyze the logic flow direction (λ_observe) to understand reasoning patterns and identify potential issues.
Based on the WFGY project: https://github.com/onestardao/WFGY
1. **Load Reasoning Context**
2. **Classify Logic Vectors**
3. **Analyze Flow Patterns**
4. **Calculate Flow Metrics**
Coherence = 1 - (changes / total_vectors) Stability = 1 - (chaos_count / total_vectors) Direction_strength = max(vector_count) / total
5. **Identify Logic Health**
LOGIC VECTOR ANALYSIS ═══════════════════════════════════════ Context: "$ANALYSIS_CONTEXT" Nodes Analyzed: [count] Time Range: [start] to [end] Vector Distribution: ┌─────────────────────────────────────┐ │ → Convergent ████████████ 45% │ │ ← Divergent ████████ 30% │ │ <> Recursive ████ 15% │ │ × Chaotic ██ 10% │ └─────────────────────────────────────┘ Logic Flow Sequence (Recent): ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ [→→→←→←→<>→→×→←→→<>→→→] ↑ ↑ Start Current Pattern Analysis: • Dominant Direction: [Convergent/Divergent/Mixed] • Pattern Type: [Linear/Cyclic/Chaotic/Exploratory] • Stability: [High/Medium/Low] • Coherence: [percentage]% Flow Metrics: ┌─────────────────────────────────────┐ │ Metric │ Value │ Status │ ├─────────────────────────────────────┤ │ Coherence │ 0.75 │ ✓ Good │ │ Stability │ 0.85 │ ✓ Good │ │ Direction │ 0.45 │ ⚠️ Mixed │ │ Progression │ 0.62 │ ✓ Good │ └─────────────────────────────────────┘ Identified Patterns: 1. Exploration Phase (nodes 1-5): [←←←→←] - Broad exploration 2. Convergence Phase (nodes 6-10): [→→→→→] - Focusing on solution 3. Refinement Loop (nodes 11-15): [<>→<>→<>] - Iterative improvement 4. Instability (nodes 16-17): [××] - Logic breakdown detected Warning Indicators: ⚠️ Chaos spike at node 16-17 ⚠️ Recursive loop at nodes 11-15 ✓ Recovery achieved at node 18 Logic Health Assessment: ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ Overall Status: [HEALTHY/FOCUSED/EXPLORATORY/UNSTABLE] Strengths: • Good convergence rate • Effective exploration phases • Quick recovery from chaos Weaknesses: • Tendency toward loops • Occasional instability • Mixed direction focus Recommendations: 1. [Increase convergent reasoning] 2. [Break recursive patterns] 3. [Stabilize chaotic transitions] Predicted Next Vector: [→/←/<>/×] Confidence: [percentage]%
[←←→→→] Explore then converge
[<><><><>] Repetitive loop
[×××××] Logic failure
[→←→←→] Alternating focus
# Analyze specific time range /reasoning:logic-vector --from "node_10" --to "node_30" # Focus on specific pattern /reasoning:logic-vector --pattern "recursive" # Predict future vectors /reasoning:logic-vector --predict 5 # Compare with ideal pattern /reasoning:logic-vector --compare "optimal"
Logic vector analysis supports:
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.
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
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:…