/reasoning-multi-path
Execute parallel reasoning exploration across multiple solution paths to find optimal approaches.
$ npx -y skills add qdhenry/Claude-Command-Suite --agent claude-codeHow it fires
How this command gets triggered: by you, by Claude, or both.
- Fires itselfClaude auto-loads it when your prompt matches the work.
- You can call itInvoke it directly when you want it.
- Slash command
/reasoning-multi-path
Context preview
What this command does when you run it.
Execute parallel reasoning exploration across multiple solution paths to find optimal approaches.
Command definition
reasoning-multi-path.mdtools:
- read
- write
- grep
- bash
arguments: $PROBLEM
Multi-Path Reasoning
Execute parallel reasoning exploration across multiple solution paths to find optimal approaches.
Based on the WFGY project: https://github.com/onestardao/WFGY
Instructions
1. **Initialize Multi-Path Setup**
- Parse problem from "$PROBLEM"
- Set number of parallel paths (default: 5)
- Load current context and constraints
- Define exploration parameters:
- Divergence factor: 0.3
- Pruning threshold: 0.1
- Max iterations: 10
2. **Generate Reasoning Paths**
- For each path i (1 to N):
- Apply unique perturbation V_i
- Vary approach angle:
- Path 1: Direct/logical
- Path 2: Creative/lateral
- Path 3: Systematic/methodical
- Path 4: Analogical/comparative
- Path 5: Contrarian/inverse
- Maintain problem constraints
- Track path evolution
3. **Evolve Paths Iteratively**
- For each iteration:
- Advance each path independently
- Calculate path metrics:
- Progress score
- Confidence level
- Semantic coherence
- Logic consistency
- Apply selection pressure:
- Amplify successful paths
- Diminish failing paths
- Cross-pollinate insights
4. **Weight and Rank Paths**
- Calculate path probabilities:
- P_i = exp(-ΔS_i) / Σexp(-ΔS_j)
- Compute dynamic weights:
- W_i based on progress rate
- Adjust for confidence
- Rank by composite score:
- Score = P_i * W_i * Confidence_i
5. **Synthesize Solutions**
- Combine top paths weighted by score
- Extract unique insights from each
- Identify convergent conclusions
- Note divergent possibilities
- Create unified solution
Output Format
MULTI-PATH REASONING ANALYSIS
═══════════════════════════════════════
Problem: "$PROBLEM"
Paths Explored: 5
Iterations: [count]
Path Evolution:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Iteration
Path 1 2 3 4 5 6 7 8 9 10
────────────────────────────────────────
Path 1 ▪──●──●──●──●──●──●──●──●──● 85%
Path 2 ▪──○──●──●──●──●──●──●──○──○ 72%
Path 3 ▪──○──○──●──●──●──○──○──×──× Failed
Path 4 ▪──○──○──○──●──●──●──●──●──● 78%
Path 5 ▪──●──●──●──●──●──●──●──●──● 91%
Legend: ▪ Start ○ Exploring ● Promising × Failed
Top 3 Solution Paths:
────────────────────────────────────────
PATH 5 (Best) - Score: 0.91
Approach: [Contrarian/Inverse thinking]
Key Insight: [Main discovery]
Reasoning: [Brief explanation]
Confidence: 91%
PATH 1 - Score: 0.85
Approach: [Direct logical analysis]
Key Insight: [Main discovery]
Reasoning: [Brief explanation]
Confidence: 85%
PATH 4 - Score: 0.78
Approach: [Analogical comparison]
Key Insight: [Main discovery]
Reasoning: [Brief explanation]
Confidence: 78%
Convergent Findings:
• All paths agree: [Common conclusion 1]
• 4/5 paths suggest: [Common conclusion 2]
• Majority indicates: [Common conclusion 3]
Divergent Possibilities:
• Path 2 uniquely suggests: [Alternative]
• Path 5 contrarian view: [Opposite angle]
Synthesized Solution:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[Weighted combination of successful paths]
[Incorporating insights from all approaches]
[Final recommended solution]
Confidence: [weighted average]%
Risk Assessment: [Low/Medium/High]
Alternative Approaches:
1. If [condition], consider Path 2
2. If [constraint], prefer Path 4
3. For [scenario], Path 1 is optimal
Path Statistics:
- Average Convergence: [value]
- Cross-Path Correlation: [value]
- Solution Stability: [value]Path Types
Logical Path
- Step-by-step deduction
- Rule-based reasoning
- Systematic progression
Creative Path
- Lateral thinking
- Unexpected connections
- Novel approaches
Empirical Path
- Evidence-based
- Data-driven
- Experimental validation
Analogical Path
- Pattern matching
- Similar problem spaces
- Transfer learning
Contrarian Path
- Inverse thinking
- Challenge assumptions
- Devil's advocate
Configuration
{
"num_paths": 5,
"max_iterations": 10,
"divergence_factor": 0.3,
"pruning_threshold": 0.1,
"selection_pressure": 0.2,
"crossover_rate": 0.15
}Advanced Usage
# Explore more paths
/reasoning:multi-path "problem" --paths 10
# Focus on creative solutions
/reasoning:multi-path "problem" --emphasize creative
# Quick exploration
/reasoning:multi-path "problem" --iterations 5
# Deep exploration
/reasoning:multi-path "problem" --iterations 20
Integration
Multi-path reasoning works with:
- `/wfgy:bbpf` for path generation
- `/semantic:node-build` to record paths
- `/boundary:detect` to check path safety
- `/reasoning:chain-validate` to verify logic
Read more
tools: - read - write - grep - bash arguments: $PROBLEM
Multi-Path Reasoning
Execute parallel reasoning exploration across multiple solution paths to find optimal approaches.
Based on the WFGY project: https://github.com/onestardao/WFGY
Instructions
1. **Initialize Multi-Path Setup**
- Parse problem from "$PROBLEM"
- Set number of parallel paths (default: 5)
- Load current context and constraints
- Define exploration parameters:
- Divergence factor: 0.3
- Pruning threshold: 0.1
- Max iterations: 10
2. **Generate Reasoning Paths**
- For each path i (1 to N):
- Apply unique perturbation V_i
- Vary approach angle:
- Path 1: Direct/logical
- Path 2: Creative/lateral
- Path 3: Systematic/methodical
- Path 4: Analogical/comparative
- Path 5: Contrarian/inverse
- Maintain problem constraints
- Track path evolution
3. **Evolve Paths Iteratively**
- For each iteration:
- Advance each path independently
- Calculate path metrics:
- Progress score
- Confidence level
- Semantic coherence
- Logic consistency
- Apply selection pressure:
- Amplify successful paths
- Diminish failing paths
- Cross-pollinate insights
4. **Weight and Rank Paths**
- Calculate path probabilities:
- P_i = exp(-ΔS_i) / Σexp(-ΔS_j)
- Compute dynamic weights:
- W_i based on progress rate
- Adjust for confidence
- Rank by composite score:
- Score = P_i * W_i * Confidence_i
5. **Synthesize Solutions**
- Combine top paths weighted by score
- Extract unique insights from each
- Identify convergent conclusions
- Note divergent possibilities
- Create unified solution
Output Format
MULTI-PATH REASONING ANALYSIS
═══════════════════════════════════════
Problem: "$PROBLEM"
Paths Explored: 5
Iterations: [count]
Path Evolution:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Iteration
Path 1 2 3 4 5 6 7 8 9 10
────────────────────────────────────────
Path 1 ▪──●──●──●──●──●──●──●──●──● 85%
Path 2 ▪──○──●──●──●──●──●──●──○──○ 72%
Path 3 ▪──○──○──●──●──●──○──○──×──× Failed
Path 4 ▪──○──○──○──●──●──●──●──●──● 78%
Path 5 ▪──●──●──●──●──●──●──●──●──● 91%
Legend: ▪ Start ○ Exploring ● Promising × Failed
Top 3 Solution Paths:
────────────────────────────────────────
PATH 5 (Best) - Score: 0.91
Approach: [Contrarian/Inverse thinking]
Key Insight: [Main discovery]
Reasoning: [Brief explanation]
Confidence: 91%
PATH 1 - Score: 0.85
Approach: [Direct logical analysis]
Key Insight: [Main discovery]
Reasoning: [Brief explanation]
Confidence: 85%
PATH 4 - Score: 0.78
Approach: [Analogical comparison]
Key Insight: [Main discovery]
Reasoning: [Brief explanation]
Confidence: 78%
Convergent Findings:
• All paths agree: [Common conclusion 1]
• 4/5 paths suggest: [Common conclusion 2]
• Majority indicates: [Common conclusion 3]
Divergent Possibilities:
• Path 2 uniquely suggests: [Alternative]
• Path 5 contrarian view: [Opposite angle]
Synthesized Solution:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
[Weighted combination of successful paths]
[Incorporating insights from all approaches]
[Final recommended solution]
Confidence: [weighted average]%
Risk Assessment: [Low/Medium/High]
Alternative Approaches:
1. If [condition], consider Path 2
2. If [constraint], prefer Path 4
3. For [scenario], Path 1 is optimal
Path Statistics:
- Average Convergence: [value]
- Cross-Path Correlation: [value]
- Solution Stability: [value]Path Types
Logical Path
- Step-by-step deduction
- Rule-based reasoning
- Systematic progression
Creative Path
- Lateral thinking
- Unexpected connections
- Novel approaches
Empirical Path
- Evidence-based
- Data-driven
- Experimental validation
Analogical Path
- Pattern matching
- Similar problem spaces
- Transfer learning
Contrarian Path
- Inverse thinking
- Challenge assumptions
- Devil's advocate
Configuration
{
"num_paths": 5,
"max_iterations": 10,
"divergence_factor": 0.3,
"pruning_threshold": 0.1,
"selection_pressure": 0.2,
"crossover_rate": 0.15
}Advanced Usage
# Explore more paths /reasoning:multi-path "problem" --paths 10 # Focus on creative solutions /reasoning:multi-path "problem" --emphasize creative # Quick exploration /reasoning:multi-path "problem" --iterations 5 # Deep exploration /reasoning:multi-path "problem" --iterations 20
Integration
Multi-path reasoning works with:
- `/wfgy:bbpf` for path generation
- `/semantic:node-build` to record paths
- `/boundary:detect` to check path safety
- `/reasoning:chain-validate` to verify logic
A comprehensive development toolkit designed following Anthropic's Claude Code Best Practices for AI-assisted software development.
Repo: qdhenry/Claude-Command-Suite
Other commands on claude-command-suite.
- /boundary-bbcr-fallback
Execute automatic BBCR (Collapse-Rebirth Correction) when knowledge boundaries are exceeded or reasoning fails.
Open command - /boundary-detect
Analyze semantic position relative to knowledge boundaries to prevent hallucination and identify uncertainty zones.
Open command - /boundary-heatmap
Generate a visual heatmap of knowledge boundaries showing safe zones, risk areas, and semantic coverage.
Open command - /boundary-risk-assess
Evaluate the current risk level and provide detailed analysis of potential hallucination or reasoning failure.
Open command - /boundary-safe-bridge
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Open command - /optimize-prompt
Takes an input prompt and returns ONLY a token-optimized version that preserves meaning while minimizing token count. Based on LLM tokenization principles: common words tokenize more efficiently, unusual words break into more tokens, and conciseness reduces cost.
Open command

