@claude-flow/plugin-cognitive-kernel

A cutting-edge cognitive augmentation plugin combining the Cognitum Gate Kernel with SONA self-optimizing architecture to provide LLMs with enhanced cognitive capabilities. The plugin enables dynamic working memory, attention control mechanisms, meta-cognitive self-monitoring, and cognitive scaffolding while maintaining low latency through WASM acceleration.

Installation

npm

npm install @claude-flow/plugin-cognitive-kernel

CLI

npx claude-flow plugins install --name @claude-flow/plugin-cognitive-kernel

Quick Start

import { CognitiveKernelPlugin } from '@claude-flow/plugin-cognitive-kernel';

// Initialize the plugin
const plugin = new CognitiveKernelPlugin();
await plugin.initialize();

// Allocate working memory for a complex reasoning task
const memorySlot = await plugin.workingMemory({
  action: 'allocate',
  slot: {
    id: 'current-problem',
    content: { problem: 'Design authentication system', context: {...} },
    priority: 0.9,
    decay: 0.05
  },
  capacity: 7  // Miller's number
});

// Control attention for focused analysis
await plugin.attentionControl({
  mode: 'focus',
  targets: [
    { entity: 'security-requirements', weight: 0.8, duration: 300 },
    { entity: 'user-experience', weight: 0.6, duration: 300 }
  ],
  filters: {
    includePatterns: ['auth*', 'security*', 'token*'],
    noveltyBias: 0.3
  }
});

console.log('Cognitive context established');

Available MCP Tools

1. cognition/working-memory

Manage dynamic working memory slots for complex reasoning tasks.

const result = await mcp.call('cognition/working-memory', {
  action: 'allocate',
  slot: {
    id: 'task-context',
    content: {
      goal: 'Refactor authentication module',
      constraints: ['maintain backward compatibility', 'improve security'],
      progress: []
    },
    priority: 0.8,
    decay: 0.1
  },
  capacity: 7,
  consolidationTarget: 'episodic'
});

Actions: allocate, update, retrieve, clear, consolidate

Returns: Memory slot state with current contents and decay status.

2. cognition/attention-control

Control cognitive attention and information filtering.

const result = await mcp.call('cognition/attention-control', {
  mode: 'selective',
  targets: [
    { entity: 'error-handling', weight: 0.9, duration: 600 },
    { entity: 'input-validation', weight: 0.7, duration: 600 }
  ],
  filters: {
    includePatterns: ['error*', 'exception*', 'validation*'],
    excludePatterns: ['deprecated*', 'legacy*'],
    noveltyBias: 0.5
  }
});

Modes: focus, diffuse, selective, divided, sustained

Returns: Attention state with active targets and filter configuration.

3. cognition/meta-monitor

Meta-cognitive monitoring of reasoning quality and self-reflection.

const result = await mcp.call('cognition/meta-monitor', {
  monitoring: [
    'confidence_calibration',
    'reasoning_coherence',
    'goal_tracking',
    'error_detection'
  ],
  reflection: {
    trigger: 'on_uncertainty',
    depth: 'medium'
  },
  interventions: true
});

Returns: Meta-cognitive assessment with confidence scores, detected issues, and suggested interventions.

4. cognition/scaffold

Provide cognitive scaffolding for complex reasoning tasks.

const result = await mcp.call('cognition/scaffold', {
  task: {
    description: 'Design a distributed caching system',
    complexity: 'complex',
    domain: 'distributed-systems'
  },
  scaffoldType: 'decomposition',
  adaptivity: {
    fading: true,
    monitoring: true
  }
});

Scaffold Types: decomposition, analogy, worked_example, socratic, metacognitive_prompting, chain_of_thought

Returns: Structured scaffolding with step-by-step guidance adapted to task complexity.

5. cognition/cognitive-load

Monitor and balance cognitive load during reasoning.

const result = await mcp.call('cognition/cognitive-load', {
  assessment: {
    intrinsic: 0.7,    // Task complexity
    extraneous: 0.3,   // Presentation complexity
    germane: 0.5       // Learning investment
  },
  optimization: 'reduce_extraneous',
  threshold: 0.8
});

Optimizations: reduce_extraneous, chunk_intrinsic, maximize_germane, balanced

Returns: Load assessment with optimization recommendations and intervention triggers.

Configuration Options

interface CognitiveKernelConfig {
  // Maximum working memory slots (default: 7, Miller's number)
  maxWorkingMemorySlots: number;

  // Memory limit in MB (default: 256)
  memoryLimit: number;

  // CPU time limit per operation in seconds (default: 10)
  cpuTimeLimit: number;

  // Enable session isolation (default: true)
  sessionIsolation: boolean;

  // Scaffold fading configuration
  scaffolding: {
    enableFading: boolean;
    fadingRate: number;
  };

  // Meta-cognitive intervention thresholds
  metaCognition: {
    confidenceThreshold: number;
    coherenceThreshold: number;
    autoIntervene: boolean;
  };
}

Performance Targets

| Metric | Target | Notes | |--------|--------|-------| | Working memory operations | <1ms per slot | 10x faster than naive cache | | Attention steering | <5ms for reallocation | 10x faster than context rebuild | | Meta-cognitive check | <10ms per assessment | Novel capability | | Memory consolidation | <100ms batch | 10x faster than full reindex | | Scaffold generation | <50ms per step | Novel capability |

Cognitive Theories Implemented

| Theory | Implementation | |--------|----------------| | Baddeley's Working Memory | Multi-component memory system with phonological loop, visuospatial sketchpad, and episodic buffer | | Cognitive Load Theory | Intrinsic/extraneous/germane load management | | Metacognition | Self-monitoring, error detection, and regulation | | Zone of Proximal Development | Adaptive scaffolding with gradual fading | | Dual Process Theory | Fast/slow thinking modes |

Security Considerations

• Session Isolation: Each cognitive session has isolated working memory with session-specific encryption keys (AES-256-GCM)
• Secure Clearing: Working memory is securely cleared and overwritten (zero-fill) at session end
• Prompt Injection Prevention: Scaffold content is sanitized to remove potential prompt injection patterns (special tokens, control sequences)
• Input Validation: All inputs validated with Zod schemas with strict limits
• Rate Limiting: Prevents abuse of cognitive resources
• Content Filtering: Memory content scanned for sensitive data patterns before storage

WASM Security Constraints

| Constraint | Value | Rationale | |------------|-------|-----------| | Memory Limit | 256MB | Sufficient for cognitive operations | | CPU Time per Operation | 10 seconds | Prevent runaway processing | | No Network Access | Enforced | Prevent data exfiltration | | Session Isolation | Enforced | Per-session WASM instances | | Secure Memory Clear | Zero-fill on exit | Prevent memory forensics |

Input Limits

| Constraint | Limit | |------------|-------| | Working memory slots | 20 max | | Memory limit | 256MB | | CPU time per operation | 10 seconds | | Attention targets | 50 max | | Scaffold description | 5,000 characters |

Rate Limits

| Tool | Requests/Minute | Max Concurrent | |------|-----------------|----------------| | working-memory | 120 | 10 | | attention-control | 60 | 5 | | meta-monitor | 60 | 5 | | scaffold | 30 | 3 | | cognitive-load | 60 | 5 |

Dependencies

• cognitum-gate-kernel - Core cognitive kernel for memory gating and attention control
• sona - Self-Optimizing Neural Architecture for adaptive cognition
• ruvector-attention-wasm - Multi-head attention for cognitive focus
• ruvector-nervous-system-wasm - Coordination between cognitive subsystems
• micro-hnsw-wasm - Fast retrieval for episodic memory

Use Cases

1. Complex Reasoning: Support multi-step reasoning with working memory persistence
2. Research Synthesis: Maintain focus across long document analysis sessions
3. Learning Enhancement: Adaptive scaffolding for skill acquisition
4. Error Prevention: Meta-cognitive monitoring catches reasoning errors before output
5. Context Management: Intelligent attention control for managing long contexts

Related Plugins

| Plugin | Description | Synergy | |--------|-------------|---------| | @claude-flow/plugin-neural-coordination | Multi-agent coordination | Cognitive kernel provides enhanced reasoning for coordinated agents | | @claude-flow/plugin-hyperbolic-reasoning | Hierarchical reasoning | Combines hierarchical structure with cognitive scaffolding | | @claude-flow/plugin-quantum-optimizer | Quantum-inspired optimization | Optimizes cognitive resource allocation and attention scheduling |

Architecture

+------------------+     +----------------------+     +------------------+
|    LLM Input     |---->|  Cognitive Kernel    |---->|  Enhanced Output |
|   (Prompts)      |     |  (WASM Accelerated)  |     |  (Augmented)     |
+------------------+     +----------------------+     +------------------+
                                   |
              +--------------------+--------------------+
              |                    |                    |
       +------+------+     +-------+-------+    +------+------+
       | Cognitum    |     |    SONA       |    | Attention   |
       | Gate Kernel |     | Self-Optimize |    | Control     |
       +-------------+     +---------------+    +-------------+
              |                    |                    |
              +--------------------+--------------------+
                                   |
                           +-------+-------+
                           | Working Memory |
                           | (HNSW Index)   |
                           +---------------+

License

MIT