claude-flow
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Ruflo - Enterprise AI agent orchestration for Claude Code. Deploy 60+ specialized agents in coordinated swarms with self-learning, fault-tolerant consensus, vector memory, and MCP integration
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# @claude-flow/plugin-cognitive-kernel
[](https://www.npmjs.com/package/@claude-flow/plugin-cognitive-kernel)
[](https://github.com/ruvnet/claude-flow/blob/main/LICENSE)
[](https://www.npmjs.com/package/@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
```bash
npm install @claude-flow/plugin-cognitive-kernel
```
### CLI
```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
### 1. `cognition/working-memory`
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.
### 2. `cognition/attention-control`
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. `cognition/meta-monitor`
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.
### 4. `cognition/scaffold`
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.
### 5. `cognition/cognitive-load`
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
### 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](https://www.npmjs.com/package/@claude-flow/plugin-neural-coordination) | Multi-agent coordination | Cognitive kernel provides enhanced reasoning for coordinated agents |
| [@claude-flow/plugin-hyperbolic-reasoning](https://www.npmjs.com/package/@claude-flow/plugin-hyperbolic-reasoning) | Hierarchical reasoning | Combines hierarchical structure with cognitive scaffolding |
| [@claude-flow/plugin-quantum-optimizer](https://www.npmjs.com/package/@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