Epstein Investigation Repository

⚠️ SECURITY NOTICE

🛡️ CREATOR IDENTITY PROTECTED - This investigation uses anonymization to protect the creator from harm/retaliation

🔒 DATA SEALED UNTIL RELEASE - All findings remain sealed until investigation is 95%+ complete and safety criteria are met

See SECURITY.md for important security guidelines.

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Overview

This repository contains an AI-powered investigation system designed to assist in the Epstein investigation by:

• Tracking connections between individuals, organizations, and locations
• Organizing evidence from public records and sources
• Analyzing relationships and identifying key connectors
• Building timelines of events
• Connecting dots through network analysis
• Providing transparency to support FBI investigations and public awareness
• Autonomous data collection from multiple sources without user input
• Transaction and tie tracking with continuous monitoring
• Redaction detection and analysis to uncover hidden information
• Cryptic identifier tracking for aliases and code names
• Name change detection and variation tracking
• Hidden connection discovery including children and relatives
• Autonomous repository updates when new information is discovered
• 🔒 CREATOR IDENTITY PROTECTION - Anonymizes investigator to prevent harm
• 🔒 CRIMINAL ACTIVITY TRACKING - Monitors potential crimes and patterns
• 🔒 SECRET GROUP DETECTION - Tracks secret organizations and networks
• 🔒 SEALED UNTIL RELEASE - All findings protected until investigation complete
• 🤖 AI ORCHESTRATION WITH FULL CONTROL - Autonomous AI research without user intervention
• 📊 LONG GAME STRATEGY PLANNING - Multi-step research planning over time
• 🔍 KNOWLEDGE GAP DISCOVERY - Identifies missing information automatically
• 🎯 MULTI-AI COORDINATION - Uses multiple AI systems strategically
• 👥 TRUSTED DEVELOPER FINDER - Vetted collaborators with right mindset who won't compromise investigation
• 🌐 WORLDWIDE WEB SCRAPING - Autonomous search and scraping across all internet sources
• 🔧 SKILL GAP RESOLUTION - Identifies missing capabilities and autonomously finds help
• 📨 AUTONOMOUS OUTREACH - Automatic contact with trusted developers for collaboration
• 💾 MASSIVE DATA STORAGE - Handles 1TB to 10TB+ datasets with deduplication and compression
• 🗜️ LONG-TERM COMPRESSION - Automatic compression for archival storage (2-10x ratios)
• 🔍 FILE TAMPERING DETECTION - 15+ methods to detect file alterations and hidden data
• 🔐 SECRET COMMUNICATIONS - Encrypted channels, steganography, and anonymous messaging
• 📨 CRYPTOGRAPHIC FLYERS - Create and distribute encrypted documents anonymously

Mission

To assist Director Patel and the FBI in expediting the Epstein investigation by providing:

1. A comprehensive database of entities and connections
2. AI-powered analysis to identify patterns and relationships
3. Public access to information to uncover hidden coverups
4. Tools to bring all truth to light
5. Autonomous research system with one logic drift: Uncover all truths with continuous ties and transactions tracking
6. AI orchestration with full autonomous control to research and uncover all information needed

Features

1. Investigation Database (investigation_system.py)

• Entity Management: Track people, organizations, locations, and events
• Evidence Repository: Store and organize evidence with verification status
• Connection Tracking: Map relationships between entities
• Search Functionality: Find entities and evidence quickly
• Network Analysis: Discover paths and connections between entities

2. Data Collection (data_collector.py)

• Public Records Integration: Import data from public sources
• Evidence Templates: Standardized evidence submission
• Timeline Building: Chronological organization of events
• Batch Import: Import multiple entities and connections at once

3. Network Analysis (network_analysis.py)

• Path Finding: Discover connections between any two entities
• Centrality Analysis: Identify most connected entities
• Cluster Detection: Find groups of highly connected entities
• Connection Strength: Measure relationship strength
• Key Connector Identification: Find entities that bridge different groups

4. Command-Line Interface (cli.py)

• Interactive Investigation: Easy-to-use menu system
• Quick Search: Find entities and evidence instantly
• Entity Analysis: Deep dive into connections and evidence
• Report Generation: Export investigation summaries

5. Autonomous Research System (autonomous_researcher.py)

• Autonomous Data Collection: Collects data without user input
• Multi-Source Support: Documents, videos, testimonies, DOJ files, flight logs, financial records
• ZIP Archive Management: Stores collected data in verified .zip files with integrity checking
• Data Mapping: Comprehensive map linking all resources, entities, and connections
• Transaction Tracking: Monitors financial transactions between entities
• Tie Tracking: Continuously tracks connections and relationships
• Priority-Based Research: Automatically prioritizes critical data sources

6. Integrated Investigation (integrated_investigation.py)

• System Integration: Bridges autonomous research with manual investigation
• Data Synchronization: Auto-syncs collected data to investigation database
• Unified Interface: Single point of access for all investigation data
• Continuous Monitoring: Tracks ties and transactions across both systems

7. Document Analysis System (document_analyzer.py)

• Redaction Detection: Identifies and analyzes redacted content in documents
• Context Analysis: Infers what redactions likely contain based on surrounding text
• Cryptic Identifier Tracking: Detects code names, aliases, and single-letter identifiers
• Name Variation Generation: Creates all possible variations of names for searching
• Maiden Name Detection: Finds name changes from marriage or legal changes
• Hidden Connection Discovery: Identifies children, relatives, and family relationships
• Temporal Pattern Matching: Finds births and events around specific timeframes

8. Autonomous Update System (autonomous_updater.py)

• Automatic Processing: Processes new documents without manual intervention
• Entity Creation: Auto-creates entities discovered in documents
• Connection Mapping: Automatically establishes relationships
• Repository Commits: Commits updates when new information is verified
• Investigation Suite: Runs comprehensive analysis on entities
• Continuous Monitoring: Watches for new information to process

9. Security & Protection System (security_system.py, identity_protection.py, release_control.py)

• 🔒 Creator Identity Protection: Anonymizes investigator identity to prevent harm/retaliation
• 🔒 Data Encryption: Encrypts sensitive investigation data
• 🔒 Access Control: Multi-level security classification (PUBLIC to TOP_SECRET)
• 🔒 Criminal Activity Tracking: Monitors and categorizes potential crimes
• 🔒 Secret Group Detection: Tracks secret organizations and networks
• 🔒 Sealed Reports: Investigation findings sealed until 95%+ complete
• 🔒 Release Authorization: Multi-criteria safety checks before disclosure

10. AI Orchestration System (ai_orchestrator.py)

• 🤖 Full Autonomous Control: AI operates independently making all research decisions
• 📊 Knowledge Gap Discovery: Identifies missing information at entity, pattern, and undocumented levels
• 🎯 Multi-AI Coordination: Coordinates 12+ specialized AI systems strategically
• 📅 Long Game Strategy: Plans multi-step research over 30-90 days
• 🧩 Incomplete Data Handling: Works effectively with partial information
• 🔍 Unknown Unknown Discovery: Finds information needs you didn't know you had
• 📈 Adaptive Learning: Adjusts strategy based on findings

11. Trusted Developer Finder (developer_finder.py)

• 👥 Developer Vetting: Multi-criteria evaluation of potential collaborators
• 🧠 Mindset Assessment: Identifies developers with right ethical approach
• 🔧 Skill Gap Analysis: Identifies missing capabilities in investigation
• 📨 Autonomous Outreach: Automatically contacts suitable developers
• 🛡️ Trust Scoring: Weighted scoring across multiple criteria
• 🔍 Background Checks: Verifies criminal records, employment, references
• 🤝 Secure Collaboration: Framework for safe collaboration without compromise

12. Worldwide Web Scraper (web_scraper.py)

• 🌐 Multi-Engine Search: Google, Bing, DuckDuckGo, Archive.org, and more
• 📊 Data Extraction: Patterns (emails, phones, dates, amounts) and entities (names, orgs, locations)
• ✅ Content Verification: SHA-256 checksums and confidence scoring
• 🔄 Cross-Verification: Validates information across multiple sources
• ⚖️ Ethical Scraping: Rate limiting and respectful crawling
• 🤖 Autonomous Research: Self-directed research workflows
• 📈 Research Reports: Comprehensive analysis of all collected data
• 🔒 Progressive Disclosure: More sensitive data revealed as investigation nears completion
• 🔒 Anonymous Contributions: Manages anonymous tips and submissions
• 🔒 Data Anonymization: Protects entity identities during investigation
• 🔒 Safe Git Configuration: Prevents accidental identity disclosure in commits

10. AI Orchestration System (ai_orchestrator.py) 🤖

• Full Autonomous Control: AI operates independently without constant user input
• Knowledge Gap Discovery: Automatically identifies missing information at multiple levels
• Multi-AI Coordination: Strategically uses 12+ different AI systems for different research needs
• Long Game Strategy: Plans multi-step research over weeks/months
• Incomplete Data Handling: Works effectively even with partial information
• Hypothesis Generation: Creates testable hypotheses from incomplete data
• Undocumented Need Discovery: Finds information needs you didn't know you had
• Progress Tracking: Comprehensive monitoring and logging
• Adaptive Learning: Adjusts strategy based on findings
• Priority-Based Execution: Critical tasks first, exploratory tasks later

Installation

Prerequisites

• Python 3.7 or higher

Setup

1. Clone this repository:

git clone https://github.com/pramit-shah/epstein-investigation-.git
cd epstein-investigation-

2. No external dependencies required - uses Python standard library only

3. Initialize the data structure:

python3 investigation_system.py

Usage

Quick Start

1. Run the interactive CLI:

python3 cli.py

2. View investigation summary:

python3 cli.py --summary

3. Run autonomous research (collects data automatically):

python3 autonomous_researcher.py

4. Run integrated investigation (autonomous + manual):

python3 integrated_investigation.py

5. Run individual modules:

# Initialize database
python3 investigation_system.py

# Set up data collection
python3 data_collector.py

# Analyze network
python3 network_analysis.py

Adding Data

Add an Entity

from investigation_system import InvestigationDatabase, Entity

db = InvestigationDatabase()
db.load_from_file()

# Create a new entity
person = Entity("Name", "person", {"role": "Description"})
person.add_tag("tag1")
person.add_tag("tag2")

db.add_entity(person)
db.save_to_file()

Add Evidence

from investigation_system import Evidence

evidence = Evidence(
    "EV001",
    "Evidence Title",
    "Source Name",
    "Evidence description and content"
)
evidence.add_related_entity("Entity Name")
evidence.add_tag("relevant_tag")
evidence.set_verification_status("verified")

db.add_evidence(evidence)
db.save_to_file()

Add Connections

# Add a connection between entities
entity = db.entities["Person A"]
entity.add_connection(
    "Person B",
    "business_partner",
    confidence=0.9
)

db.save_to_file()

Analyzing the Network

from investigation_system import InvestigationDatabase, InvestigationAssistant
from network_analysis import NetworkAnalyzer

# Load database
db = InvestigationDatabase()
db.load_from_file()

# Create analyzer
analyzer = NetworkAnalyzer(db.entities, db.evidence)

# Find path between entities
path = analyzer.find_shortest_path("Person A", "Person B")
print(f"Path: {' → '.join(path)}")

# Get network report
print(analyzer.generate_network_report())

# Identify key connectors
connectors = analyzer.identify_key_connectors(10)
for conn in connectors:
    print(f"{conn['name']}: {conn['centrality_score']:.3f}")

Data Organization

data/
├── investigation_data.json    # Main database
├── entities/                  # Entity-specific data
├── evidence/                  # Evidence files
├── connections/              # Connection data
├── timeline/                 # Timeline events
│   └── timeline.json
├── collected/                # Collected data
├── reports/                  # Generated reports
└── analysis/                 # Analysis outputs

Contributing

This investigation relies on public information and transparent collaboration.

How to Contribute

1. Submit Evidence: Use the evidence template to submit public information
2. Report Connections: Identify relationships between entities
3. Verify Data: Help verify existing evidence
4. Improve Code: Submit pull requests for improvements

See CONTRIBUTING.md for detailed guidelines.

Evidence Submission Guidelines

• Only include information from public sources
• Cite sources clearly
• Mark verification status appropriately
• Tag evidence for easy categorization
• Include relevant dates when available

Data Verification

All evidence should be:

• From public, verifiable sources
• Properly cited
• Cross-referenced when possible
• Marked with verification status

Investigation Principles

1. Transparency: All data is open and accessible
2. Accuracy: Verify information from multiple sources
3. Comprehensive: Track all connections and evidence
4. Objective: Let the data speak for itself
5. Legal: Only use publicly available information

API Reference

InvestigationDatabase

Main database for storing entities and evidence.

Methods:

• add_entity(entity): Add an entity to the database
• add_evidence(evidence): Add evidence to the database
• find_connections(entity_name, max_depth): Find all connections
• search_entities(query, entity_type): Search for entities
• search_evidence(query): Search evidence
• get_entity_network(entity_name): Get complete network info
• generate_investigation_report(): Generate status report
• save_to_file(filename): Save database to JSON
• load_from_file(filename): Load database from JSON

NetworkAnalyzer

Analyzes network connections and relationships.

Methods:

• find_shortest_path(start, end): Find shortest path between entities
• find_all_paths(start, end, max_depth): Find all paths
• calculate_centrality(): Calculate entity centrality scores
• find_clusters(min_cluster_size): Identify entity clusters
• identify_key_connectors(top_n): Find most connected entities
• analyze_connection_strength(entity1, entity2): Analyze connection
• generate_network_report(): Generate network analysis report

InvestigationAssistant

AI assistant for investigation analysis.

Methods:

• analyze_entity(entity_name): Detailed entity analysis
• suggest_connections(entity_name): Suggest potential connections
• find_investigation_gaps(): Identify gaps in investigation
• generate_investigation_summary(): Generate summary report

Security and Privacy

• This system only processes publicly available information
• No personal data should be included unless already public
• All sources must be verifiable
• Comply with all applicable laws and regulations

Disclaimer

This is an investigative tool for organizing publicly available information. It does not:

• Make accusations or determinations of guilt
• Include non-public or confidential information
• Replace official law enforcement investigations
• Provide legal advice

All information should be verified through official sources.

Support

For questions or issues:

• Open an issue on GitHub
• Review existing documentation
• Check the examples in the code

License

MIT License - See LICENSE file for details

Acknowledgments

This tool is designed to assist in bringing truth to light and supporting legitimate investigative efforts by organizing publicly available information in a transparent and accessible manner.

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Status: Active Development
Version: 1.0.0
Last Updated: 2026-02-14

Quick Reference Commands

# View summary
python3 cli.py --summary

# Interactive mode
python3 cli.py

# Initialize fresh database
python3 investigation_system.py

# Run network analysis
python3 network_analysis.py

# Run autonomous research (NEW)
python3 autonomous_researcher.py

# Run integrated investigation (NEW)
python3 integrated_investigation.py

Autonomous Research System

The autonomous research system operates independently to:

• Collect data from DOJ files, flight logs, testimonies, financial records, and media
• Store all data in verified .zip archives
• Create comprehensive data maps linking resources
• Track financial transactions continuously
• Monitor ties and connections between entities
• Sync with manual investigation database

One Logic Drift: Uncover all truths with continuous ties and transactions tracking

See AUTONOMOUS_RESEARCH.md for complete documentation.

AI Orchestration with Full Control 🤖

The AI Orchestration System provides full autonomous control for research when you have incomplete information:

The Problem It Solves

"How to research when you don't have all the information and need to play the long game?"

The system addresses:

• Incomplete Information: You have most but not all data
• Unknown Unknowns: You don't know what you don't know
• Multiple AI Systems: Different AI tools excel at different tasks
• Long Game Strategy: Need multi-step, iterative approach
• Undocumented Needs: Requirements that aren't yet clear

Core Capabilities

1. Knowledge Gap Discovery

from ai_orchestrator import AIOrchestrator

orchestrator = AIOrchestrator(investigation_context="...")
analysis = orchestrator.analyze_current_state(incomplete_data)
# Identifies: missing fields, sparse networks, undocumented needs

2. Long Game Strategy Planning

strategy = orchestrator.create_research_strategy(
    timeframe_days=30,  # 30-day research plan
    max_parallel_tasks=3  # Run 3 tasks simultaneously
)
# Plans: Immediate, short-term, medium-term, long-term phases

3. Full Autonomous Execution

execution_log = orchestrator.execute_autonomous_research(
    max_iterations=10
)
# AI takes full control: identifies gaps, plans, executes, adapts

4. Multi-AI Coordination

• 12+ AI systems for different research needs
• Strategic system selection based on task type
• Cross-validation across multiple sources
• Adaptive learning from results

Available AI Systems

• WEB_SEARCH: Public information, news, events
• DOCUMENT_ANALYSIS: PDFs, legal docs, redactions
• NETWORK_ANALYSIS: Relationships, influence, paths
• FINANCIAL_ANALYSIS: Transactions, money flow, fraud
• TEMPORAL_ANALYSIS: Timelines, sequences, events
• PATTERN_RECOGNITION: Trends, anomalies, correlations
• CROSS_REFERENCE: Multi-source validation
• And 5 more specialized systems...

Example: Autonomous Investigation

# 1. Start with incomplete data
incomplete_data = {
    'entities': [
        {'id': 'E1', 'name': 'John Doe'},  # Missing: type, connections, timeline
        {'id': 'E2', 'name': 'ABC Corp'}   # Missing: almost everything
    ],
    'connections': []  # Empty - need to discover
}

# 2. AI analyzes and creates plan
orchestrator = AIOrchestrator("Trafficking investigation")
analysis = orchestrator.analyze_current_state(incomplete_data)
strategy = orchestrator.create_research_strategy(timeframe_days=30)

# 3. AI executes autonomous research
log = orchestrator.execute_autonomous_research(max_iterations=10)

# 4. Monitor progress
report = orchestrator.generate_progress_report()
print(f"Completion: {report['completion_percentage']:.1f}%")

Key Features

• ✅ Full Autonomous Control: Operates without constant user input
• ✅ Gap Discovery: Identifies what's missing automatically
• ✅ Strategic Planning: Multi-step "long game" approach
• ✅ Multi-AI Use: Coordinates 12+ AI systems effectively
• ✅ Handles Incomplete Data: Works with partial information
• ✅ Discovers Unknowns: Finds needs you didn't know you had
• ✅ Adaptive: Adjusts strategy based on findings
• ✅ Progress Tracking: Comprehensive monitoring and logging

See AI_ORCHESTRATION.md for complete documentation.

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13. Massive Data Storage System 💾

Handle 1TB to 10TB+ Datasets

Purpose: Efficiently store and manage massive investigation datasets with deduplication, compression, and distributed storage.

Key Components

1. Data Deduplication

• Content-based chunking
• 50-90% space savings
• SHA-256 chunk identification
• File reconstruction

2. Compression for Long-Term Storage 🗜️

• Multiple algorithms: zlib, gzip, bz2
• 2-10x compression ratios
• Optimized for archival
• Automatic algorithm selection
• Compression levels 1-9

3. Distributed Storage

• Multi-location storage (local, cloud, network)
• 2-3x replication for redundancy
• Health monitoring
• Automatic failure recovery

4. Smart Data Collection

• Auto-categorization by file type
• Metadata extraction and indexing
• Search capabilities
• Organized storage

Usage Example

from massive_storage import MassiveStorageSystem

# Initialize for 10TB storage
storage = MassiveStorageSystem(
    base_path="/mnt/investigation",
    storage_locations=[
        "/mnt/local",
        "s3://bucket/data",
        "/mnt/backup"
    ],
    max_size_tb=10.0
)

# Store with deduplication and compression (long-term)
result = storage.store_file(
    "evidence_package.zip",
    deduplicate=True,
    compress=True,  # Compress for long-term storage
    replicate=True
)

# Smart collection of entire directory
stats = storage.smart_collection(
    source_dir="/downloads/evidence",
    auto_categorize=True,
    deduplicate=True,
    compress=True  # All files compressed for archival
)

# Get storage stats
stats = storage.get_storage_stats()
print(f"Total size: {stats['total_size_tb']:.2f} TB")
print(f"Capacity used: {stats['capacity_used_percent']:.1f}%")

Features

• ✅ Handles 1TB to 10TB+ datasets
• ✅ Deduplication saves 50-90% space
• ✅ Compression: 2-10x ratios for long-term storage
• ✅ Distributed across multiple locations
• ✅ Automatic replication for redundancy
• ✅ Smart categorization and search
• ✅ Health monitoring and recovery

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14. File Tampering Detection System 🔍

Detect Alterations Using 15+ Methods

Purpose: Identify file tampering, hidden data, and alterations hundreds of different ways.

Detection Methods (15+)

1. Hash-Based (3 methods)

• SHA-256, SHA-512, MD5
• Detects any content modification

2. Metadata Analysis (4 methods)

• Timestamp anomalies
• EXIF data tampering
• File system attributes
• Creation/modification time checks

3. Content Analysis (4 methods)

• File signature validation
• Structure integrity checks
• Known tampering patterns
• Suspicious content detection

4. Steganography Detection (2 methods)

• LSB (Least Significant Bit) analysis
• Statistical anomaly detection

5. Binary Analysis (2 methods)

• Hex dump patterns
• Binary structure validation

Usage Example

from file_tampering_detector import FileTamperingDetector

detector = FileTamperingDetector()

# Create baseline for file
detector.create_baseline("important_document.pdf")

# Later, check for tampering
result = detector.comprehensive_check("important_document.pdf")

if result['tampered']:
    print(f"⚠️ TAMPERING DETECTED!")
    print(f"Confidence: {result['confidence']:.0%}")
    print(f"Evidence: {result['evidence']}")
    print(f"Methods detected: {len(result['checks'])}")

# Batch check multiple files
results = detector.batch_check([
    "file1.pdf",
    "file2.doc", 
    "file3.jpg"
])

print(f"Tampered files: {len(results['tampered_files'])}")

Features

• ✅ 15+ detection methods
• ✅ Multi-hash verification
• ✅ Metadata analysis
• ✅ Steganography detection
• ✅ Pattern recognition
• ✅ Confidence scoring
• ✅ Batch processing
• ✅ Comprehensive evidence collection

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15. Secret Communications System 🔐

Encrypted Channels & Anonymous Distribution

Purpose: Create secret encrypted communication channels and distribute findings anonymously.

Components

1. Encryption

• AES-256 (symmetric)
• RSA-4096 (asymmetric)
• Hybrid encryption (RSA + AES)
• PGP support

2. Steganography

• Hide messages in images
• Hide messages in audio
• LSB encoding
• Invisible to inspection

3. Secret Channels

• End-to-end encrypted
• Perfect forward secrecy
• Channel-specific keys
• Message history encrypted

4. Anonymous Messaging

• Onion routing (Tor-like)
• No sender identification
• Encrypted relay network
• Untraceable delivery

5. Cryptographic Flyers

• Create encrypted documents
• PGP-signed for authenticity
• Distribute anonymously
• Recipients decrypt with key

Usage Examples

Encryption:

from secret_communications import EncryptionManager

enc = EncryptionManager()

# AES-256 for speed
key_id = enc.generate_symmetric_key()
encrypted = enc.encrypt_aes(b"secret data", key_id)
decrypted = enc.decrypt_aes(encrypted, key_id)

# RSA-4096 for key exchange
key_id = enc.generate_rsa_keypair()
encrypted = enc.encrypt_rsa(b"message", key_id)
decrypted = enc.decrypt_rsa(encrypted, key_id)

# Hybrid for large data
enc_key, enc_data = enc.hybrid_encrypt(b"large dataset", key_id)
decrypted = enc.hybrid_decrypt(enc_key, enc_data, key_id)

Secret Channels:

from secret_communications import SecretChannelManager

mgr = SecretChannelManager()

# Create encrypted channel
channel = mgr.create_channel("investigation-team", "aes-256")

# Send encrypted message
mgr.send_message(
    channel['id'],
    "Meeting at safe location",
    sender_id="agent-1"
)

# Receive messages (auto-decrypted)
messages = mgr.receive_messages(channel['id'])

Steganography:

from secret_communications import SteganographyEngine

stego = SteganographyEngine()

# Hide message in image
result = stego.hide_in_image(
    image_path="cover.png",
    message="Secret investigation findings",
    output_path="output.png"
)

# Extract hidden message
extracted = stego.extract_from_image("output.png")

Anonymous Distribution:

from secret_communications import CryptographicFlyerSystem

flyer_sys = CryptographicFlyerSystem()

# Create encrypted flyer
flyer = flyer_sys.create_flyer(
    title="Investigation Report",
    content="Confidential findings...",
    recipients=["trusted@example.com"],
    encryption='pgp'
)

# Distribute anonymously
result = flyer_sys.distribute_anonymously(
    flyer['id'],
    method='steganography'  # or 'email', 'drop'
)

# Recipients decrypt
content = flyer_sys.decrypt_flyer(flyer['id'])

Features

• ✅ AES-256 encryption
• ✅ RSA-4096 key exchange
• ✅ Steganography for covert comms
• ✅ Anonymous messaging
• ✅ Encrypted channels
• ✅ Cryptographic flyers
• ✅ Perfect forward secrecy
• ✅ No metadata leakage

See MASSIVE_SYSTEMS.md for complete documentation.

16. Continuous Task System with Swarm Agents

Break down complex tasks using WH-questions and distribute to swarms of parallel agents for faster completion.

WH-Question Decomposition

Automatically breaks down tasks into 7 fundamental questions:

from continuous_task_system import WHQuestionDecomposer

decomposer = WHQuestionDecomposer()
subtasks = decomposer.decompose_task(
    "Investigate financial transactions",
    context="Money laundering case"
)

for subtask in subtasks:
    print(f"{subtask.wh_type}: {subtask.description}")
    # WHAT: What transactions occurred?
    # WHO: Who was involved?
    # WHEN: When did they happen?
    # WHERE: Where were they processed?
    # WHY: Why were they made?
    # HOW: How were they executed?
    # WHICH: Which accounts/banks?

Swarm Agent Execution

Distribute work to multiple parallel agents for 2-7x speedup:

from continuous_task_system import SwarmOrchestrator

orchestrator = SwarmOrchestrator(num_agents=7)
results = orchestrator.execute_task_swarm(
    task_description="Analyze 500 documents",
    subtasks=subtasks,
    parallel=True
)

print(f"Speedup: {results['speedup']:.1f}x")  # ~5.5x with 7 agents

Continuous Background Tasks

Schedule tasks to run continuously in background:

from continuous_task_system import ContinuousTaskScheduler

scheduler = ContinuousTaskScheduler()

# Recurring task
scheduler.schedule_task(
    task_id="daily_scrape",
    description="Scrape news sources",
    priority="HIGH",
    interval_hours=24,
    task_type="recurring"
)

# Continuous monitoring
scheduler.schedule_task(
    task_id="monitor",
    description="Monitor file changes",
    priority="CRITICAL",
    task_type="continuous"
)

scheduler.start()

Complete Integration

Use the complete system for continuous investigation:

from continuous_task_system import ContinuousTaskSystem

system = ContinuousTaskSystem(num_agents=7, max_parallel_jobs=5)

log = system.start_continuous_investigation(
    investigation_goal="Uncover all connections",
    focus_areas=["transactions", "entities"],
    max_duration_hours=24
)

# Monitor progress
status = system.get_system_status()
print(f"Active jobs: {status['active_jobs']}")
print(f"Completed: {status['completed_tasks']}")

Features:

• ✅ WH-question decomposition (7 types)
• ✅ Swarm agents (3-10 configurable)
• ✅ 2-7x speedup with parallel execution
• ✅ Continuous 24/7 background operation
• ✅ Priority-based scheduling
• ✅ Auto-restart on failure

See CONTINUOUS_TASKS.md for complete documentation.

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