Files
blackroad-operating-system/cognitive/quickstart.py
Claude 9ec18608fd Add Cognitive Layer - The missing OS layer for AI-human collaboration
This is what AI collaboration should have been from day one. A comprehensive
cognitive layer that solves the fundamental problems of context loss,
information silos, and coordination chaos.

## Core Components

**Intent Graph** - Tracks WHY things happen
- Every goal, task, and decision has a rationale
- Relationships between objectives are explicit
- Context is never lost

**Semantic File System** - Files that know what they ARE
- Auto-classification based on content and purpose
- Semantic search (find by meaning, not just name)
- Auto-organization (no more downloads folder chaos)
- Files suggest where they belong

**Living Documents** - Self-updating documentation
- Code-aware: understands what code it documents
- Detects when code changes and docs are stale
- Can auto-generate from code
- Always in sync

**Context Engine** - Right information at the right time
- Provides relevant context based on current task
- Integrates intent, code, docs, and decisions
- Proactive intelligence (suggests next actions)
- Answers: "Why does this exist?" "What's related?"

**Agent Coordination Protocol** - Multi-agent collaboration that works
- Shared context via cognitive layer
- Clear task ownership and handoffs
- No duplicate work
- Conflict resolution
- Progress tracking

**Smart Documents** - OCR, templates, auto-formatting
- Extract text from PDFs and images
- Identify document types automatically
- ATS-friendly resume formatting
- Business plan templates
- Auto-filing based on content
- Template matching and application

## What This Solves

Traditional problems:
 Files in arbitrary folders
 Context lives in people's heads
 Docs get out of sync
 Multi-agent chaos
 Downloads folder anarchy
 Lost decisions and rationale

Cognitive OS solutions:
 Files organize by meaning and purpose
 Context is captured and connected
 Docs update themselves
 Agents coordinate cleanly
 Everything auto-organizes
 Every decision is recorded with WHY

## Architecture

cognitive/
├── __init__.py           # Main CognitiveOS integration
├── intent_graph.py       # Goals, tasks, decisions, relationships
├── semantic_fs.py        # Content-aware file organization
├── living_docs.py        # Self-updating documentation
├── context_engine.py     # Intelligent context retrieval
├── agent_coordination.py # Multi-agent collaboration
├── smart_documents.py    # OCR, templates, auto-format
├── README.md            # Vision and philosophy
├── USAGE.md             # Complete usage guide
├── quickstart.py        # Interactive demo
└── requirements.txt     # Optional dependencies

## Quick Start

```python
from cognitive import CognitiveOS

# Initialize
cog = CognitiveOS()

# Create a goal with rationale
goal = cog.create_goal(
    "Build user authentication",
    rationale="Users need secure access"
)

# Process a document (auto-classify, auto-organize)
cog.process_new_file("~/Downloads/resume.pdf")

# Get context for what you're working on
context = cog.get_context(task_id="current-task")
```

## Philosophy

This is how AI and data should have been handled from the start:
- **Semantic over Hierarchical**: Organize by meaning, not folders
- **Intent-Preserving**: Capture WHY, not just WHAT
- **Auto-Linking**: Related things connect automatically
- **Context-Aware**: System knows what you're trying to do
- **Agent-First**: Designed for AI-human collaboration

Combines the best of Notion + Asana + actual code awareness +
auto-organization + OCR + business planning + ATS-friendly formatting.

No more hoping the world doesn't catch on fire.
No more downloads folder chaos.
No more lost context.

This is the cognitive layer every OS should have had.
2025-11-17 05:34:57 +00:00

186 lines
5.7 KiB
Python
Executable File

#!/usr/bin/env python3
"""
Cognitive OS - Quick Start Script
This demonstrates the Cognitive OS in action.
Run this to see what it can do!
"""
from cognitive import CognitiveOS, AgentRole, DocumentTemplate
def main():
print("\n" + "=" * 70)
print(" " * 20 + "COGNITIVE OS - QUICK START")
print("=" * 70 + "\n")
print("Initializing Cognitive Operating System...")
cog = CognitiveOS(workspace_path=".")
print("✓ Cognitive OS initialized\n")
# Demo 1: Intent Graph
print("-" * 70)
print("DEMO 1: Intent Graph - Tracking Goals, Tasks, and WHY")
print("-" * 70)
goal = cog.create_goal(
title="Build a smart document management system",
description="Create a system that understands documents and organizes them automatically",
rationale="Current file management is chaos. Downloads folder anarchy. Need semantic organization."
)
task1 = cog.create_task(
"Implement OCR for document scanning",
goal_id=goal.id,
rationale="Need to extract structured data from PDFs and images"
)
task2 = cog.create_task(
"Build auto-filing system",
goal_id=goal.id,
rationale="Documents should organize themselves based on content"
)
decision = cog.intent_graph.create_decision(
title="Use Tesseract for OCR",
rationale="Open source, well-maintained, good accuracy, supports multiple languages",
alternatives_considered=[
"Google Cloud Vision API (too expensive for local-first OS)",
"AWS Textract (vendor lock-in)",
"Azure Computer Vision (vendor lock-in)"
]
)
cog.intent_graph.link_nodes(decision.id, task1.id, "related")
print()
# Demo 2: Semantic File System
print("-" * 70)
print("DEMO 2: Semantic File System - Files That Know What They Are")
print("-" * 70)
print("\nExample: If you had a resume in downloads...")
print(" Traditional: ~/Downloads/john_resume_final_v2_FINAL.pdf")
print(" Cognitive OS suggests: documents/career/resumes/john_resume_final_v2_FINAL.pdf")
print("\nBased on content analysis, not filename!")
print()
# Demo 3: Context Engine
print("-" * 70)
print("DEMO 3: Context Engine - Right Info at Right Time")
print("-" * 70)
print(f"\nGetting context for task: '{task1.title}'")
context = cog.get_context(task_id=task1.id)
print("\nRelevant context:")
for item in context.get_top_items(5):
print(f" [{item.type:15s}] {item.title}")
if item.metadata.get('rationale'):
print(f" → Why: {item.metadata['rationale']}")
print()
# Demo 4: Agent Coordination
print("-" * 70)
print("DEMO 4: Agent Coordination - Multi-Agent Collaboration")
print("-" * 70)
from cognitive.agent_coordination import AgentInfo, HandoffType
# Register agents
coder = AgentInfo(name="CodeWriter", role=AgentRole.CODER)
reviewer = AgentInfo(name="CodeReviewer", role=AgentRole.REVIEWER)
cog.agent_coordinator.register_agent(coder)
cog.agent_coordinator.register_agent(reviewer)
print(f"\n✓ Registered agent: {coder.name} ({coder.role.value})")
print(f"✓ Registered agent: {reviewer.name} ({reviewer.role.value})")
# Create collaboration session
session = cog.agent_coordinator.create_session(
goal="Implement OCR feature",
description="Add OCR capability to document processor"
)
print(f"\n✓ Created collaboration session: {session.goal}")
# Assign task
cog.agent_coordinator.assign_task(task1.id, coder.id)
print(f"✓ Assigned task to {coder.name}")
# Create handoff
handoff = cog.agent_coordinator.create_handoff(
from_agent_id=coder.id,
to_agent_id=reviewer.id,
task_id=task1.id,
handoff_type=HandoffType.REVIEW,
message="OCR implementation complete, ready for review"
)
print(f"✓ Created handoff: {coder.name}{reviewer.name} (for review)")
print()
# Demo 5: Smart Documents
print("-" * 70)
print("DEMO 5: Smart Documents - Templates and Auto-Formatting")
print("-" * 70)
print("\nAvailable templates:")
print(" ✓ ATS-friendly Resume (beats applicant tracking systems)")
print(" ✓ Business Plan (executive summary, financials, market analysis)")
print(" ✓ Meeting Notes (structured with action items)")
print(" ✓ Technical Spec (architecture, requirements, API docs)")
print(" ✓ And more...")
print("\nDocuments can:")
print(" • Extract text via OCR from images/PDFs")
print(" • Identify what type of document they are")
print(" • Auto-format for specific purposes (ATS, business, etc.)")
print(" • Organize themselves into correct folders")
print()
# Show overall state
print("-" * 70)
print("CURRENT STATE")
print("-" * 70)
print(cog.intent_graph.get_summary())
# Next steps
print("\n" + "=" * 70)
print("NEXT STEPS")
print("=" * 70)
print("""
1. Try it yourself:
from cognitive import CognitiveOS
cog = CognitiveOS()
goal = cog.create_goal("Your goal here", rationale="Why you're doing this")
2. Process a document:
cog.process_new_file("path/to/your/document.pdf")
3. Get context for your work:
context = cog.get_context(query="What am I working on?")
4. Check the full usage guide:
See cognitive/USAGE.md for complete examples
5. Integrate with your workflow:
- Connect to your IDE
- Watch your downloads folder
- Link to git commits
- Coordinate multiple agents
This is what AI collaboration should have been from day one.
No more context loss. No more file chaos. No more docs out of sync.
Welcome to the Cognitive OS.
""")
print("=" * 70 + "\n")
if __name__ == "__main__":
main()