Cognitive Mode Management

SKILL-SPECIFIC GUIDANCE

When to Use This Skill

• Configuring cognitive modes for different task types (research, coding, security)
• Optimizing prompt engineering through GlobalMOO optimization
• Ensuring epistemic consistency with VERIX notation
• Selecting appropriate VERILINGUA cognitive frames
• Running multi-objective optimization on prompt configurations
• Meta-loop recursive improvement on foundry skills

When NOT to Use This Skill

• Simple one-off tasks that don't require specialized configuration
• Tasks where speed is paramount and optimization overhead is unacceptable
• When default balanced mode is sufficient
• Non-technical conversational interactions

Success Criteria

• Appropriate mode selected for task domain and complexity
• VERIX epistemic notation applied correctly to claims
• Cognitive frames activated match task requirements
• GlobalMOO optimization produces Pareto-optimal configurations
• Cross-model consistency maintained (Claude + Gemini + Codex)

Edge Cases & Limitations

• Mode selection may be uncertain for novel task types
• VERIX parsing may miss nuanced epistemic markers
• GlobalMOO optimization requires multiple iterations
• Some cognitive frames may conflict (e.g., minimal vs comprehensive)

Critical Guardrails

• NEVER skip VERIX grounding for high-confidence claims
• ALWAYS validate mode selection for security-sensitive tasks
• NEVER use minimal mode for compliance/​audit tasks
• ALWAYS include confidence levels for factual assertions
• NEVER modify holdout corpus (never_optimize: true)

Evidence-Based Validation

• Mode selection validated against expected_metrics
• VERIX consistency checked via ConsistencyChecker
• Optimization results compared to Pareto frontier
• Cross-model evaluation via 3-model council

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Cognitive Mode Management

A comprehensive skill for managing cognitive modes, VERILINGUA frames, VERIX epistemic notation, and GlobalMOO optimization in the Context Cascade plugin system.

Overview

This skill integrates four major systems:

1. VERILINGUA: 7 cognitive frames from diverse linguistic traditions
2. VERIX: Epistemic notation for claim validation
3. DSPy: Two-layer prompt optimization (Level 2 caching + Level 1 evolution)
4. GlobalMOO: Multi-objective optimization for configuration tuning

Slash Commands

/​mode - Mode Selection

Select and configure cognitive modes:

/​mode                    # List available modes
/​mode <name>             # Select mode by name
/​mode auto "<task>"      # Auto-select based on task
/​mode info <name>        # Show mode details
/​mode recommend "<task>" # Get top-3 recommendations

Available Modes:

• strict: Maximum epistemic consistency (research, legal, medical)
• balanced: Good tradeoff for general use (default)
• efficient: Optimized for token efficiency (high-volume APIs)
• robust: Edge case handling (security, adversarial)
• minimal: Lightweight with no frames (simple Q&A)

/​eval - Evaluation

Evaluate tasks against cognitive architecture metrics:

/​eval "<task>" "<response>"  # Evaluate response
/​eval --corpus <path>        # Run corpus evaluation
/​eval --metrics              # Show metric definitions
/​eval --graders              # List available graders

Metrics:

• task_accuracy: Correctness (0.0 - 1.0)
• token_efficiency: Tokens vs target (0.0 - 1.0)
• edge_robustness: Adversarial handling (0.0 - 1.0)
• epistemic_consistency: VERIX compliance (0.0 - 1.0)

/​optimize - GlobalMOO Optimization

Run multi-objective optimization:

/​optimize               # Show optimization status
/​optimize start         # Start optimization run
/​optimize suggest       # Get configuration suggestions
/​optimize report        # Get optimization report
/​optimize phase <A|B|C> # Run specific cascade phase

Three-MOO Cascade:

• Phase A: Framework structure optimization
• Phase B: Edge case discovery
• Phase C: Production frontier refinement

/​pareto - Pareto Frontier

Explore the Pareto frontier:

/​pareto                  # Display frontier
/​pareto filter <metric>  # Filter by metric
/​pareto export           # Export as JSON
/​pareto distill          # Distill into named modes
/​pareto visualize        # ASCII visualization

/​frame - VERILINGUA Frames

Configure cognitive frames:

/​frame                    # List all frames
/​frame <name>             # Show frame details
/​frame enable <names>     # Enable frames
/​frame disable <names>    # Disable frames
/​frame preset <name>      # Apply preset

Frames:

• evidential: Turkish -mis/​-di ("How do you know?")
• aspectual: Russian pfv/​ipfv ("Complete or ongoing?")
• morphological: Arabic trilateral roots (semantic decomposition)
• compositional: German compounding (primitives to compounds)
• honorific: Japanese keigo (audience calibration)
• classifier: Chinese measure words (object comparison)
• spatial: Guugu Yimithirr (absolute positioning)

Presets:

• all: All 7 frames
• minimal: No frames
• research: evidential + aspectual
• coding: compositional + spatial
• documentation: honorific + compositional
• analysis: evidential + aspectual + morphological
• security: evidential + spatial + classifier

/​verix - Epistemic Notation

Apply VERIX notation:

/​verix                     # Show VERIX guide
/​verix parse "<text>"      # Parse for VERIX elements
/​verix validate "<claim>"  # Validate epistemic consistency
/​verix annotate "<text>"   # Add VERIX annotations
/​verix level <0|1|2>       # Set compression level

VERIX Structure:

STATEMENT := ILLOCUTION + AFFECT + CONTENT + GROUND + CONFIDENCE + STATE

Compression Levels:

• L0: AI-AI (Emoji shorthand, maximum compression)
• L1: AI+Human (Full annotation, balanced)
• L2: Human (Natural language, lossy)

Thin Waist Architecture

Two contracts that NEVER change:

Contract 1 - PromptBuilder:

def build(task: str, task_type: str) -> Tuple[str, str]:
    """Returns (system_prompt, user_prompt)"""

Contract 2 - Evaluate:

def evaluate(config_vector: List[float]) -> OutcomesVector:
    """config_vector -> outcomes_vector"""

Configuration Vector

14-dimensional vector for GlobalMOO:

• Dimensions 0-6: Framework flags (7 cognitive frames)
• Dimension 7: VERIX strictness (0-2)
• Dimension 8: Compression level (0-2)
• Dimensions 9-13: Reserved for future use

Integration with Meta-Loop

This skill integrates with the recursive improvement system:

1. Prompt Forge: Optimizes prompts (including skill prompts)
2. Skill Forge: Applies improvements (including to itself)
3. Agent Creator: Creates auditor agents
4. Eval Harness: Gates ALL changes (FROZEN - never self-improves)

3-Model Council

For cross-model compatibility, evaluation uses a 3-model council:

• Claude (primary)
• Gemini (validation)
• Codex (technical verification)

All three must agree for high-confidence claims.

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Core Principles

1. Mode-First Thinking

Always select the appropriate cognitive mode before executing tasks. Modes encode domain-specific optimizations that dramatically improve outcomes. Don't default to balanced - consciously choose based on task requirements.

2. Epistemic Hygiene

Every high-confidence claim requires grounding. Use VERIX notation to make epistemic status explicit. Ungrounded certainty is a red flag - always provide evidence basis for strong assertions.

3. Multi-Objective Optimization

Recognize that accuracy, efficiency, robustness, and consistency often trade off against each other. Use GlobalMOO to find Pareto-optimal configurations rather than optimizing a single metric.

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Anti-Patterns

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Conclusion

The cognitive-mode skill provides a unified interface to VERILINGUA, VERIX, DSPy, and GlobalMOO. By selecting appropriate modes, applying epistemic notation, and running multi-objective optimization, you can dramatically improve AI task performance across diverse domains.

Key insight: The cognitive architecture is itself subject to recursive improvement. The prompt-architect, skill-forge, and agent-creator form a foundry triangle that continuously optimizes the system - but always gated by the frozen eval harness to prevent Goodhart's Law.