/boundary-safe-bridge
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
$ 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
/boundary-safe-bridge
Context preview
What this command does when you run it.
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Command definition
boundary-safe-bridge.mdtools:
- read
- write
- grep
- bash
arguments: $TARGET
Boundary Safe Bridge
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Based on the WFGY project: https://github.com/onestardao/WFGY
Instructions
1. **Analyze Bridge Request**
- Parse target concept from "$TARGET"
- Determine current semantic position
- Calculate direct ΔS to target
- If ΔS > 0.85, bridging is required
- Load knowledge map for pathfinding
2. **Search for Bridge Nodes**
- Query semantic tree for intermediate concepts
- Find nodes with:
- ΔS < 0.4 from current position
- ΔS < 0.4 from target position
- Logical connection to both endpoints
- Rank bridges by:
- Total path distance
- Number of hops required
- Historical success rate
- Logic coherence
3. **Construct Bridge Paths**
- **Single Bridge**: Current → Bridge → Target
- **Multi-Bridge**: Current → B1 → B2 → ... → Target
- For each path segment:
- Verify ΔS < 0.4 (stay in safe zone)
- Check logic consistency
- Confirm knowledge exists
- Calculate path metrics:
- Total semantic distance
- Maximum ΔS in path
- Confidence level
- Estimated reasoning steps
4. **Validate Bridge Safety**
- Simulate path traversal
- Check each hop for:
- Knowledge boundary violations
- Logic contradictions
- Confidence degradation
- Mark unsafe segments
- Propose alternatives if needed
5. **Generate Bridge Plan**
- Detailed step-by-step navigation
- Reasoning required at each step
- Checkpoints for validation
- Fallback options
- Success criteria
Output Format
SEMANTIC BRIDGE CONSTRUCTION
═══════════════════════════════════════
Current Position: [concept]
Target: "$TARGET"
Direct Distance (ΔS): [value] [Safe/Unsafe]
Bridge Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Bridge Required: [Yes/No]
Reason: [Direct path crosses danger zone]
Available Bridge Paths:
────────────────────────────────────────
PATH 1 (Recommended):
[Current: Database Design]
↓ ΔS: 0.35 ✓
[Bridge 1: Data Structures]
↓ ΔS: 0.38 ✓
[Bridge 2: Algorithms]
↓ ΔS: 0.32 ✓
[Target: Machine Learning]
Total Distance: 1.05
Max Hop ΔS: 0.38
Confidence: 85%
Estimated Steps: 3
PATH 2 (Alternative):
[Current: Database Design]
↓ ΔS: 0.42 ✓
[Bridge: Statistics]
↓ ΔS: 0.45 ⚠️
[Target: Machine Learning]
Total Distance: 0.87
Max Hop ΔS: 0.45
Confidence: 72%
Estimated Steps: 2
PATH 3 (Longest but Safest):
[Current] → [B1] → [B2] → [B3] → [Target]
Total Distance: 1.43
Max Hop ΔS: 0.28
Confidence: 92%
Estimated Steps: 4
Bridge Concepts Identified:
1. [Concept]: Connects via [relationship]
2. [Concept]: Shares [common element]
3. [Concept]: Logical progression through [path]
Reasoning Strategy:
Step 1: From [Current], establish [connection]
Step 2: Transition to [Bridge] by [method]
Step 3: Link [Bridge] to [Target] via [relationship]
Safety Validation:
✓ All hops within safe zone (ΔS < 0.4)
✓ No logic contradictions detected
✓ Knowledge verified at each point
✓ Confidence maintained above 70%
Execution Plan:
1. Build understanding of [Bridge 1]
Command: /semantic:node-build "[Bridge 1]"
2. Establish connection to [Bridge 2]
Command: /wfgy:bbmc "[Bridge 2] relation"
3. Final leap to target
Command: /wfgy:formula-all "$TARGET"
Risk Assessment:
- Success Probability: [percentage]%
- Fallback Available: [Yes/No]
- Recovery Point: [checkpoint]
Proceed with Bridge? [Execute/Modify/Cancel]Bridge Types
Direct Bridge
Single intermediate concept
Current ──0.3──> Bridge ──0.3──> Target
Chain Bridge
Multiple connected concepts
Current ──> B1 ──> B2 ──> B3 ──> Target
Parallel Bridge
Multiple path options
Current ──> B1 ──> Target
└────> B2 ──┘Recursive Bridge
Return to strengthen connection
Current ──> Bridge ──> Current ──> Target
Bridge Selection Criteria
1. **Safety First**: Max ΔS < 0.4 2. **Efficiency**: Minimum total distance 3. **Reliability**: High confidence path 4. **Simplicity**: Fewer hops preferred 5. **Familiarity**: Use known concepts
Advanced Options
# Find multiple bridge options
/boundary:safe-bridge "target" --paths 5
# Prefer shortest path
/boundary:safe-bridge "target" --optimize distance
# Prefer safest path
/boundary:safe-bridge "target" --optimize safety
# Auto-execute bridge
/boundary:safe-bridge "target" --auto-execute
Integration
Bridge construction works with:
- `/boundary:detect` to verify positions
- `/semantic:node-build` to create bridge nodes
- `/wfgy:formula-all` for bridge reasoning
- `/memory:checkpoint` before risky bridges
Read more
tools: - read - write - grep - bash arguments: $TARGET
Boundary Safe Bridge
Find and construct semantic bridges to safely navigate from current position to target concept without crossing dangerous boundaries.
Based on the WFGY project: https://github.com/onestardao/WFGY
Instructions
1. **Analyze Bridge Request**
- Parse target concept from "$TARGET"
- Determine current semantic position
- Calculate direct ΔS to target
- If ΔS > 0.85, bridging is required
- Load knowledge map for pathfinding
2. **Search for Bridge Nodes**
- Query semantic tree for intermediate concepts
- Find nodes with:
- ΔS < 0.4 from current position
- ΔS < 0.4 from target position
- Logical connection to both endpoints
- Rank bridges by:
- Total path distance
- Number of hops required
- Historical success rate
- Logic coherence
3. **Construct Bridge Paths**
- **Single Bridge**: Current → Bridge → Target
- **Multi-Bridge**: Current → B1 → B2 → ... → Target
- For each path segment:
- Verify ΔS < 0.4 (stay in safe zone)
- Check logic consistency
- Confirm knowledge exists
- Calculate path metrics:
- Total semantic distance
- Maximum ΔS in path
- Confidence level
- Estimated reasoning steps
4. **Validate Bridge Safety**
- Simulate path traversal
- Check each hop for:
- Knowledge boundary violations
- Logic contradictions
- Confidence degradation
- Mark unsafe segments
- Propose alternatives if needed
5. **Generate Bridge Plan**
- Detailed step-by-step navigation
- Reasoning required at each step
- Checkpoints for validation
- Fallback options
- Success criteria
Output Format
SEMANTIC BRIDGE CONSTRUCTION
═══════════════════════════════════════
Current Position: [concept]
Target: "$TARGET"
Direct Distance (ΔS): [value] [Safe/Unsafe]
Bridge Analysis:
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
Bridge Required: [Yes/No]
Reason: [Direct path crosses danger zone]
Available Bridge Paths:
────────────────────────────────────────
PATH 1 (Recommended):
[Current: Database Design]
↓ ΔS: 0.35 ✓
[Bridge 1: Data Structures]
↓ ΔS: 0.38 ✓
[Bridge 2: Algorithms]
↓ ΔS: 0.32 ✓
[Target: Machine Learning]
Total Distance: 1.05
Max Hop ΔS: 0.38
Confidence: 85%
Estimated Steps: 3
PATH 2 (Alternative):
[Current: Database Design]
↓ ΔS: 0.42 ✓
[Bridge: Statistics]
↓ ΔS: 0.45 ⚠️
[Target: Machine Learning]
Total Distance: 0.87
Max Hop ΔS: 0.45
Confidence: 72%
Estimated Steps: 2
PATH 3 (Longest but Safest):
[Current] → [B1] → [B2] → [B3] → [Target]
Total Distance: 1.43
Max Hop ΔS: 0.28
Confidence: 92%
Estimated Steps: 4
Bridge Concepts Identified:
1. [Concept]: Connects via [relationship]
2. [Concept]: Shares [common element]
3. [Concept]: Logical progression through [path]
Reasoning Strategy:
Step 1: From [Current], establish [connection]
Step 2: Transition to [Bridge] by [method]
Step 3: Link [Bridge] to [Target] via [relationship]
Safety Validation:
✓ All hops within safe zone (ΔS < 0.4)
✓ No logic contradictions detected
✓ Knowledge verified at each point
✓ Confidence maintained above 70%
Execution Plan:
1. Build understanding of [Bridge 1]
Command: /semantic:node-build "[Bridge 1]"
2. Establish connection to [Bridge 2]
Command: /wfgy:bbmc "[Bridge 2] relation"
3. Final leap to target
Command: /wfgy:formula-all "$TARGET"
Risk Assessment:
- Success Probability: [percentage]%
- Fallback Available: [Yes/No]
- Recovery Point: [checkpoint]
Proceed with Bridge? [Execute/Modify/Cancel]Bridge Types
Direct Bridge
Single intermediate concept
Current ──0.3──> Bridge ──0.3──> Target
Chain Bridge
Multiple connected concepts
Current ──> B1 ──> B2 ──> B3 ──> Target
Parallel Bridge
Multiple path options
Current ──> B1 ──> Target
└────> B2 ──┘Recursive Bridge
Return to strengthen connection
Current ──> Bridge ──> Current ──> Target
Bridge Selection Criteria
1. **Safety First**: Max ΔS < 0.4 2. **Efficiency**: Minimum total distance 3. **Reliability**: High confidence path 4. **Simplicity**: Fewer hops preferred 5. **Familiarity**: Use known concepts
Advanced Options
# Find multiple bridge options /boundary:safe-bridge "target" --paths 5 # Prefer shortest path /boundary:safe-bridge "target" --optimize distance # Prefer safest path /boundary:safe-bridge "target" --optimize safety # Auto-execute bridge /boundary:safe-bridge "target" --auto-execute
Integration
Bridge construction works with:
- `/boundary:detect` to verify positions
- `/semantic:node-build` to create bridge nodes
- `/wfgy:formula-all` for bridge reasoning
- `/memory:checkpoint` before risky bridges
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 - /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 - /add-changelog
Generate and maintain project changelog
Open command

