@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

$3

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

$3

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

Quick Start

`typescript
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

$3

Manage dynamic working memory slots for complex reasoning tasks.

`typescript
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.

$3

Control cognitive attention and information filtering.

`typescript
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

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

`typescript
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.

$3

Provide cognitive scaffolding for complex reasoning tasks.

`typescript
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.

$3

Monitor and balance cognitive load during reasoning.

`typescript
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

`typescript
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

$3

| 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 |

$3

| 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 |

$3

| 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