Imported from Zpankz/mcp-skillset (infranodus-skills/infranodus-reasoning/SKILL.md). Install upstream with npx skills add Zpankz/mcp-skillset --skill infranodus-reasoning. Copyright stays with the author.

InfraNodus Reasoning Engine

Programmatic cognitive reasoning system integrating:

• Temporal state tracking (BIASED/FOCUSED/DIVERSIFIED/DISPERSED with energy economics)
• Writing pattern analysis (grammatical signals of cognitive states)
• Critical perspective generation (state-aware questioning)
• Ontology validation (anti-hierarchy enforcement)
• Gap interpretation (contextual InfraNodus integration)
• Intelligent routing (context detection and pipeline selection)

Architecture

Programmatic Layer (Python modules in scripts/):

• router.py - Context detection and route selection
• coordinator.py - Pipeline orchestration
• state_manager.py - Temporal state persistence
• pattern_detector.py - Writing pattern analysis
• question_engine.py - Critical question generation
• gap_analyzer.py - Gap interpretation with state context
• ontology_validator.py - Anti-hierarchy validation
• infranodus_bridge.py - MCP tool integration interface
• utils.py - Shared data structures and utilities

Interpretive Layer (Claude):

• SKILL.md (this file) - Orchestration and natural language synthesis
• components/guidance.md - Philosophical context for interpretation
• components/*.md - Reference component skills

Usage Workflow

Step 1: Route Detection

Invoke the router to analyze user intent and select appropriate pipeline:

python3 scripts/router.py "user message here"

Router output (JSON):

{
  "route": "text_analysis",
  "confidence": 0.85,
  "reason": "Substantial text provided for comprehensive analysis",
  "components": ["pattern_detector", "gap_analyzer", "infranodus_bridge"],
  "options": null
}

Step 2: Pipeline Execution

Invoke coordinator with the selected route:

python3 scripts/coordinator.py <route> "user message" "text to analyze"

Coordinator output (JSON):

{
  "route": "text_analysis",
  "state_before": {...},
  "state_after": {...},
  "patterns": {...},
  "questions": [...],
  "gaps": [...],
  "recommendations": [...],
  "requires_mcp": true,
  "mcp_requests": [...],
  "errors": []
}

Step 3: MCP Tool Invocation (if required)

If requires_mcp = true, invoke InfraNodus MCP tools using mcp_requests data:

// Each request contains:
{
  "tool": "generate_knowledge_graph",
  "parameters": {...},
  "parser": "parse_graph_response"
}

Invoke the tool, then parse response using the specified parser from infranodus_bridge.py.

Step 4: Result Interpretation

Combine programmatic output with MCP data and interpret using components/guidance.md context:

1. Pattern → State correlation: Reference guidance.md cognitive states
2. Questions → Priority: Use state-aware question interpretation
3. Gaps → Strategies: Apply state-dependent gap interpretation
4. Recommendations → Natural language: Synthesize into user-facing guidance

Routes and Pipelines

pattern_detection_only

When: Text provided without specific request Components: [pattern_detector] Output: Patterns, state detection MCP: No

Usage:

python3 scripts/coordinator.py pattern_detection_only "analyze" "text here"

Interpret: Report patterns detected and any cognitive state shifts.

text_analysis

When: Grammar fixes, text analysis, "analyze" keyword + text Components: [pattern_detector, gap_analyzer, infranodus_bridge] Output: Patterns, gap analysis request MCP: Yes (generate_content_gaps)

Usage:

python3 scripts/coordinator.py text_analysis "fix grammar" "text here"

Interpret:

1. Report pattern findings
2. Invoke InfraNodus MCP tool with mcp_requests
3. Present grammar-corrected text with pattern-based insights
4. Suggest gap development if relevant

cognitive_diagnosis

When: "stuck", "cognitive", "state", "thinking" keywords Components: [state_manager, pattern_detector, question_engine] Output: State analysis, diagnostic questions MCP: No

Usage:

python3 scripts/coordinator.py cognitive_diagnosis "I feel stuck" "user text"

Interpret:

1. Report current cognitive state, dwelling time, energy level
2. Present diagnostic questions generated by question_engine
3. Explain state dynamics using guidance.md
4. Recommend state transition if needed

critical_intervention

When: Energy <0.2, dwelling exceeded, "challenge" keyword Components: [question_engine, gap_analyzer] Output: Maximum challenge questions, state recommendations MCP: No

Usage:

python3 scripts/coordinator.py critical_intervention "challenge assumptions" "user text"

Interpret:

1. Present challenging questions (8+ questions)
2. Explain intervention reason (energy/dwelling)
3. Recommend state transition
4. Provide blind spot analysis

ontology_generation

When: "ontology", "knowledge graph" keywords Components: [ontology_validator, infranodus_bridge] Output: Validation results, graph creation request MCP: Yes (create_knowledge_graph) if valid

Usage:

python3 scripts/coordinator.py ontology_generation "create ontology" "ontology text"

Interpret:

1. Report validation results (errors, warnings, metrics)
2. If invalid: Explain anti-hierarchy or relation code violations
3. If valid: Invoke create_knowledge_graph MCP tool
4. Provide improvement recommendations

full_pipeline

When: Substantial text (>200 words) + "develop"/"strategic" keywords Components: [pattern_detector, gap_analyzer, infranodus_bridge, question_engine] Output: Comprehensive analysis MCP: Yes (develop_text_tool, generate_content_gaps)

Usage:

python3 scripts/coordinator.py full_pipeline "develop this" "long text"

Interpret:

1. Report pattern analysis
2. Invoke multiple InfraNodus MCP tools (develop_text_tool, generate_content_gaps)
3. Parse and contextualize gap data with gap_analyzer
4. Present research questions
5. Provide development strategy recommendations
6. Generate follow-up questions

clarify

When: Ambiguous or very short messages Components: [] Output: Clarification request MCP: No

Interpret: Ask user to specify intent (grammar? analysis? ontology? diagnosis?)

State-Aware Interpretation

Always check current conversation state before interpreting results:

python3 -c "from scripts.state_manager import load_state; import json; print(json.dumps(load_state(), indent=2))"

Key state factors:

• current_state: BIASED/FOCUSED/DIVERSIFIED/DISPERSED
• dwelling_time: Exchanges in current state
• energy_level: 0.0 to 1.0
• state_history: Transition record

State affects:

• Question intensity and type
• Gap interpretation strategy
• Intervention priority
• Recommendation tone

Reference components/guidance.md for state-specific interpretation guidelines.

Examples

Example 1: Grammar Correction with Pattern Analysis

User: "Fix grammar: Machine learning help us understand patterns. Its about connections not just data itself."

Workflow:

# Route detection
python3 scripts/router.py "Fix grammar: Machine learning..."
# Output: route="text_analysis", confidence=0.85

# Execute pipeline
python3 scripts/coordinator.py text_analysis "Fix grammar" "Machine learning help us..."
# Output: patterns detected, gap analysis request

Interpret:

1. Correct grammar: "Machine learning helps us understand patterns. It's about connections, not just the data itself."
2. Report patterns: repetitive_structures=false, punctuation_rhythm=mixed
3. No significant cognitive state concerns
4. Skip MCP gap analysis (text too short)

Example 2: Cognitive Diagnosis

User: "I keep thinking about the same problem over and over. Can't move forward."

Workflow:

# Route
python3 scripts/router.py "I keep thinking..."
# Output: route="cognitive_diagnosis"

# Execute
python3 scripts/coordinator.py cognitive_diagnosis "I keep thinking..." "same problem over and over"
# Output: state=BIASED, dwelling=4, energy=0.65, questions=[8 challenging questions]

Interpret:

1. Current state: BIASED (dwelling 4 exchanges, threshold 3)
2. Energy level: 65% (sustainable but declining)
3. Present diagnostic questions from question_engine
4. Recommend transition to FOCUSED state
5. Explain BIASED state dynamics from guidance.md

Example 3: Ontology Validation

User: "Validate this ontology: [[ML]] uses [[data]] [relatedTo]\n[[ML]] has [[accuracy]] [hasAttribute]..."

Workflow:

# Route
python3 scripts/router.py "Validate this ontology..."
# Output: route="ontology_generation"

# Execute
python3 scripts/coordinator.py ontology_generation "validate" "[[ML]] uses [[data]]..."
# Output: validation results with errors/warnings

Interpret:

1. Report validation status
2. If errors: Explain anti-hierarchy violations ("ML dominates with 80% of statements")
3. Provide correction strategy: "Distribute relationships across multiple entity pairs"
4. If warnings: Note relation code imbalance
5. If valid: Offer to save to InfraNodus via create_knowledge_graph

Example 4: Full Strategic Development

User: "Help me develop this 800-word article about heart rate variability for SEO."

Workflow:

# Route
python3 scripts/router.py "Help me develop..."
# Output: route="full_pipeline"

# Execute
python3 scripts/coordinator.py full_pipeline "develop article" "[800-word HRV article]"
# Output: patterns, mcp_requests=[develop_text_tool, generate_content_gaps]

# Invoke MCP tools
# 1. develop_text_tool → research questions, latent topics
# 2. generate_content_gaps → structural gaps

# Re-run coordinator with MCP data for gap interpretation

Interpret:

1. Present pattern analysis
2. Invoke InfraNodus MCP tools
3. Interpret gaps contextually (current state: FOCUSED → "productive expansion opportunities")
4. Present research questions
5. Recommend specific topic development
6. Provide SEO alignment suggestions (if generate_seo_report used)

Error Handling

If router errors: Default to "clarify" route If coordinator errors: Check errors array in output, report to user If MCP tools unavailable: Skip MCP-dependent routes, use pattern-only analysis If state file corrupt: State manager auto-initializes fresh state

Component Skill Reference

When additional context needed beyond programmatic output:

Writing philosophy: components/writing-assistant.md Ontology syntax: components/ontology-creator.md Question templates: components/critical-perspective.md State dynamics: components/cognitive-variability.md Interpretive guidance: components/guidance.md

Security and State Management

State persistence: conversation_state.json in skill directory State reset: Delete conversation_state.json to start fresh Module safety: All modules validate inputs before processing MCP validation: infranodus_bridge validates all parameters before tool invocation

Performance Notes

Programmatic advantages:

• ~10x faster pattern detection vs manual analysis
• Deterministic state tracking across sessions
• Consistent validation (no human variability in ontology checking)
• Precise energy/dwelling calculations

Claude advantages:

• Natural language synthesis and explanation
• Contextual recommendation tailoring
• Creative examples and analogies
• Emotional intelligence in delivery
• MCP tool invocation and integration

When NOT to Use This Skill

Skip if:

• Simple question answering (no reasoning/analysis needed)
• No text analysis, pattern detection, ontology, or cognitive diagnosis requested
• User explicitly requests different skill or approach

Prefer this skill if:

• User provides text for analysis/correction
• Cognitive state concerns ("stuck", "obsessing", "scattered")
• Ontology/knowledge graph generation requested
• Strategic content development needed
• InfraNodus integration relevant

Quick Reference

# Route detection
python3 scripts/router.py "message"

# Pipeline execution
python3 scripts/coordinator.py <route> "message" "text"

# Check current state
python3 -c "from scripts.state_manager import load_state; print(load_state()['current_state'])"

# Test pattern detection
python3 scripts/pattern_detector.py

# Test ontology validation
python3 scripts/ontology_validator.py

# View module documentation
cat components/guidance.md

Remember: You (Claude) are the interpretive layer. The Python modules provide algorithmic precision; you provide contextual wisdom, natural language synthesis, and user-facing intelligence. Use components/guidance.md to ground your interpretations in the philosophical framework.