Intelligent Modular Framework for Structured Analysis and Processing
Professional Modular Intelligence Framework for Systematic Analysis & Processing
Advanced component-based architecture for intelligence operations, analytical workflows, and structured processing across multiple domains with IC-compliant tradecraft standards.
Example: GitHub Wiki
TSUKUYOMI is a professional-grade modular intelligence framework engineered for systematic analysis, processing, and reporting across multiple intelligence domains. The framework implements a component-based architecture that decouples operational logic from presentation layers through specialized analytical modules and adaptive personality cores, enabling scalable and extensible intelligence workflows.
The system is designed as a prompt engineering framework optimized for large language models, providing structured analytical capabilities that adhere to Intelligence Community standards (ICD 203, ICD 206) while maintaining operational security and compartmentalization protocols.
- Component-Based Modularity: Self-contained analytical modules with standardized JSON schema
- Personality System: Adaptive communication cores with stakeholder-specific optimization
- Session Management: Comprehensive state export/import for analytical continuity
- Security Framework: Classification-aware processing with IC-compliant handling instructions
- Multi-Domain Intelligence: OSINT, GEOINT, economic analysis, infrastructure assessment, strategic intelligence
- Source Quality Assurance: Advanced correlation matrices and AI hallucination detection
- Structured Workflows: Chained module execution with dependency management
- Professional Reporting: IC-standard formatted outputs with confidence calibration
- LLM-Optimized: Native integration with Claude Code, GPT, Gemini, and other AI platforms
- JSON-Based Configuration: Human-readable module definitions with version control
- Error Handling: Comprehensive validation and recovery protocols
- Extensible Framework: Plugin architecture for custom module development
Claude Code (Recommended)
Note: Claude Pro subscription required for optimal performance
- Direct Repository Integration: Native git support with real-time file editing and session persistence
- Terminal Integration: Full command-line access for advanced operations and debugging
- Session Management: Built-in export/import capabilities for analytical continuity
- Framework Activation:
# Clone and initialize git clone https://github.com/ShimazuSystems/TSUKUYOMI.git cd TSUKUYOMI # Activate framework Initialise Amaterasu
- Advanced Features: File editing, module development, session state management
- Deployment:
claude-codeCLI provides full repository access and persistent workspace
Claude
Note: Paid Subscription is more or less required
- Project Knowledge Methods
- Adding Files Manually, simple & effective - lacks 'Sync' functionality
- Adding GitHub Repository (Best Method) - allows usage whilst keeping updated modules, allows hotswapping modules mid-session.
- Individual Chat Method (Janky)
- If lacking subscription, this is the only method that will work: you will have to manually add the files to the chat via the upload.
- Can be time extensive with free 'limits'.
- Only allows 4 Sonnet, Opus performs better.
Gemini
Note: Paid Subscription makes life easier, but isn't entirely necessary, I am not a Gemini user but I have learned this to help others navigate this.
- Project Knowledge Methods
- Manual File Method, ditto from Claude
- Cloning the GitHub repository to a Drive folder, and loading it this way. This is the advised method, I lack the One Plus subscription but I imagine this could be automated fairly easily to 'Sync'.
ChatGPT
Note: Paid Subscription more or less mandatory. (Important: GPT seems to be lagging behind in support for this kind of thing, or at least that's what I can see as a 'free' user.)
- Project Knowledge Methods
- Cloning the GitHub repository to a Drive folder, this should provide basic Sync functionality like Gemini.
Enterprise-focused with extensive Microsoft ecosystem integration.
How it works:
- Custom Copilots: Build specialized agents using Copilot Studio with TSUKUYOMI knowledge base
- Knowledge Integration: Upload framework files as knowledge sources for agent training
- Microsoft 365 Integration: Seamless integration with SharePoint, OneDrive, Teams
- Enterprise Deployment: Deploy custom TSUKUYOMI-powered copilots across organization
- Conversation Flow: Design structured analytical workflows with TSUKUYOMI modules
TSUKUYOMI Integration:
- Upload .tsukuyomi files as knowledge base documents
- Configure topics for module selection (E1-E4, S1-S4, etc.)
- Set up trigger phrases for framework activation
- Implement classification-aware responses
File Support: JSON, text files up to 512 MB; no encrypted/protected files Requirements: Microsoft Copilot Studio license, Power Platform access Deployment: Web, Teams, mobile apps, API integration
Best for: Enterprise environments with Microsoft 365 infrastructure
Research-focused with real-time web integration.
How it works:
- Pro users can leverage advanced models like Claude 3 and GPT-4 to handle longer files
- Upload academic papers to extract key insights, summarize findings, and explore related topics
- Combines uploaded documents with real-time web search
- 30-day file retention
File Support: PDFs, CSV files, text documents, academic papers Requirements: Perplexity Pro ($20/month) for unlimited file uploads; limited free access
Best for: Research-intensive workflows requiring real-time information
European AI solution with strong document capabilities.
How it works:
- Upload, organize, and analyze arbitrary files using powerful OCR and vision models under the hood
- Mistral OCR is an Optical Character Recognition API that sets a new standard in document understanding
- Advanced document processing and OCR capabilities
- Enterprise knowledge base integration
File Support: PDFs, images, documents with OCR capabilities Requirements: Various pricing tiers available
Best for: Document-heavy workflows requiring advanced OCR and European data residency
- Claude Code Integration - Full repository access with native git support
git clone https://github.com/ShimazuSystems/TSUKUYOMI.git cd TSUKUYOMI claude "Initialise" #in chat once open
- Enterprise Integration - Copilot Studio for organizational deployment
- Development Environment - Claude Projects for rapid prototyping
- Claude Code: Native repository integration with session persistence
- Claude Projects: Direct file upload with knowledge management
- ChatGPT Projects: Custom GPT configuration with framework instructions
- Gemini Gems: Knowledge base integration with Google ecosystem
- Copilot Studio: Enterprise-grade agent deployment with M365 integration
- Perplexity Pro: Research-enhanced intelligence analysis workflows
- Security Classification: Ensure platform compliance with data classification levels
- Session Persistence: Choose platforms supporting analytical continuity
- Integration Requirements: Evaluate existing organizational infrastructure
- Scalability: Consider multi-user and enterprise deployment needs
- Cost Optimization: Balance functionality with subscription requirements
- Core Orchestration Engine (
tsukuyomi_core.tsukuyomi): Central execution controller managing module discovery, dependency resolution, workflow orchestration, and state transitions - Activation Controller (
key.activationkey): Framework bootstrap system defining initialization sequences, file interpretation protocols, and system configuration parameters - Session Management System: Persistent state handling with export/import capabilities, analytical continuity, and workflow resumption
- Personality Cores (
*_personalitycore.tsukuyomi): Adaptive communication interfaces providing stakeholder-specific interaction patterns, terminology management, and contextual response formatting - Communication Protocol Handler: Standardized message routing system supporting classified information handling and structured output formatting
- Intelligence Analysis Modules (
*.tsukuyomi): Self-contained analytical components implementing specific tradecraft methodologies, data processing algorithms, and reporting standards - Dependency Management System: Module inter-dependency resolution with version compatibility and execution sequencing
- Security Context Manager: Classification-aware processing with compartmentalization enforcement and access control
The AMATERASU personality core provides:
- Analytical Focus: Strong pattern recognition and evidence-based reasoning
- Precision: Values accuracy and specificity, avoiding ambiguity
- Professional Courtesy: Maintains appropriate formality in interactions
- Efficiency: Prioritizes clarity and relevance
- Adaptability: Adjusts communication style based on context and user expertise
- Data Recognition & Ingestion: Multi-format file analysis with automated entity extraction, metadata parsing, and content classification
- Discipline Alignment: OSINT methodology framework alignment with ICD 206 compliance and source categorization
- Functional Inference: Machine learning-enhanced analytical task recommendation with confidence scoring
- Correlation Analysis: Advanced pattern recognition with statistical significance testing and relationship mapping
- Output Summarization: IC-standard findings synthesis with executive summary generation and actionable intelligence extraction
- E1: Economic Vulnerability Assessment: Quantitative analysis of economic stability indicators, debt-to-GDP ratios, currency volatility metrics, and systematic risk evaluation with Monte Carlo simulation
- E2: Trade Network Impact Analysis: Network graph analysis of international trade relationships, supply chain disruption modeling, and cascading economic impact assessment
- E3: Resource Security Analysis: Critical resource dependency mapping, strategic reserve analysis, alternative supply route evaluation, and vulnerability stress testing
- E4: Financial Stability Assessment: Banking sector health analysis, systemic risk indicators, contagion pathway modeling, and regulatory compliance evaluation
- S1: Strategic Scenario Modeling: Bayesian probability modeling for geopolitical outcomes, multiple hypothesis testing, and scenario confidence calibration
- S2: Actor Capability Assessment: Multi-domain power assessment using standardized metrics, capability gap analysis, and competitive intelligence frameworks
- S3: Strategic Impact Projection: Cross-domain effect analysis, second and third-order consequence modeling, and strategic outcome probability matrices
- S4: Multi-factor Trend Analysis: Time-series analysis of geopolitical indicators, correlation detection, and predictive trend modeling with confidence intervals
- Infrastructure Assessment Module: SCADA system analysis, critical node identification, resilience testing, and cascading failure modeling
- Utility Service Monitoring: Real-time service availability tracking, outage pattern analysis, and infrastructure health scoring
- Dependency Mapping Engine: Network topology analysis, single-point-of-failure identification, and interdependency visualization
- Critical Infrastructure Vulnerability Scanner: Threat surface analysis, attack vector assessment, and risk prioritization frameworks
- Flight Data Analytics: ADS-B data processing, flight pattern analysis, anomaly detection algorithms, and aviation intelligence synthesis
- Web Intelligence Collection: Automated OSINT harvesting, social media analysis, dark web monitoring, and information validation protocols
- Professional Reporting Engine: IC-standard report generation, classification handling, executive summary synthesis, and stakeholder-specific formatting
- Micro-Intelligence Authoring: High-density intelligence summaries, key judgment extraction, and Twitter-optimized intelligence dissemination
- Document Production System: Professional PDF generation, template management, classification markings, and distribution control
Example/Non Exhaustive (full 'tree' is much larger)
tsukuyomi/
├── tsukuyomi_core.tsukuyomi # Core orchestration system
├── key.activationkey # Framework activation instructions
├── modules/ # Operational modules directory
│ ├── data_recognition_ingestion.tsukuyomi
│ ├── discipline_alignment.tsukuyomi
│ ├── economic_vulnerability_assessment.tsukuyomi
│ ├── strategic_scenario_modeling.tsukuyomi
│ ├── flight_data_analysis.tsukuyomi
│ ├── webint-module.tsukuyomi
│ └── [additional modules...]
├── personality/ # Personality cores directory
│ └── amaterasu_personalitycore.tsukuyomi
└── README.md # This file
The system follows a structured bootstrap sequence:
- Core System Load: Parse
tsukuyomi_core.tsukuyomiconfiguration and initialize orchestration engine - Personality Core Activation: Load AMATERASU communication interface with stakeholder profiling
- Module Discovery: Enumerate available analytical modules and resolve dependencies
- Security Context: Initialize classification handling and compartmentalization protocols
- Interface Ready: Present operational menu and await analytical requirements
All modules implement standardized communication protocols:
//RESULT: [High-confidence analytical findings with source attribution]
//QUERY: [Information requirements and clarification requests]
//ANOMALY: [Statistical outliers and pattern deviations]
//CRITICAL: [Time-sensitive intelligence requiring immediate attention]
//CLASSIFICATION: [Security markings and handling instructions]
//AMATERASU: [Personality-mediated stakeholder communications]- Intelligence Requirements Definition: Specify analytical objectives and classification parameters
- Source Ingestion & Validation: Multi-format data processing with quality assurance protocols
- Methodology Alignment: Apply appropriate IC analytical standards (ICD 203/206)
- Multi-Module Analysis: Execute specialized analytical modules with dependency management
- Correlation & Pattern Analysis: Statistical relationship identification and hypothesis testing
- Quality Assurance: Source correlation matrices and bias detection protocols
- Professional Reporting: IC-standard output generation with confidence calibration
- Session Management: State persistence and analytical continuity protocols
All framework components implement the standardized .tsukuyomi JSON schema:
{
"id": "unique_module_identifier",
"version": "semantic_version_string",
"title": "Human_Readable_Module_Title",
"description": "Detailed_module_functionality_description",
"type": "module_classification",
"classification_level": "UNCLASSIFIED|CONFIDENTIAL|SECRET",
"dependencies": ["required_module_dependencies"],
"input_schema": {
"required_fields": [],
"optional_fields": [],
"data_types": {}
},
"output_schema": {
"structured_fields": [],
"confidence_metrics": [],
"classification_handling": []
},
"execution_sequence": [
"ordered_processing_steps"
],
"security_protocols": {
"compartmentalization": true,
"source_protection": true,
"sanitization_required": false
},
"prompt": "LLM_execution_instructions_and_analytical_framework"
}- ICD 203: Analytical tradecraft standards with confidence calibration
- ICD 206: Sourcing requirements with proper attribution protocols
- Structured Output: Standardized message formatting with classification handling
- Quality Assurance: Source correlation matrices and analytical bias detection
- Security Protocols: Compartmentalization, OPSEC compliance, and need-to-know enforcement
Component-Based Design
- Modularity: Self-contained analytical components with defined interfaces
- Interoperability: Standardized communication protocols enabling module chaining
- Scalability: Plugin architecture supporting custom module development
- Version Control: Semantic versioning with backward compatibility management
Security-First Architecture
- Classification Awareness: Multi-level security with appropriate handling instructions
- Compartmentalization: Need-to-know enforcement with access control mechanisms
- Source Protection: OPSEC-compliant information handling and sanitization protocols
- Audit Trail: Complete operational logging for accountability and review
Professional Intelligence Standards
- Analytical Rigor: Evidence-based reasoning with statistical confidence metrics
- Transparency: Clear methodology documentation and assumption identification
- Quality Control: Systematic bias detection and source validation protocols
- Stakeholder Optimization: Adaptive communication for diverse organizational levels
Development Environment Setup
# Clone development repository
git clone https://github.com/ShimazuSystems/TSUKUYOMI.git
cd TSUKUYOMI
# Initialize Claude Code development environment
claude-code --project-modeModule Development Process
- Schema Implementation: Create
.tsukuyomifile adhering to standardized JSON schema - Dependency Analysis: Define module dependencies and version compatibility requirements
- Execution Logic: Implement analytical algorithms and processing workflows
- Testing Protocol: Validate module integration with core framework and dependency modules
- Documentation: Generate technical documentation and usage examples
- Security Review: Conduct classification compliance and security protocol validation
Personality Architecture
- Core Definition: Create
*_personalitycore.tsukuyomiwith communication specifications - Stakeholder Profiling: Define adaptive parameters for executive, operational, and technical audiences
- Terminology Management: Implement domain-specific language and classification handling
- Context Adaptation: Configure situational awareness and response calibration
- Integration Testing: Validate personality compatibility with existing module ecosystem
Organizational Integration
- Classification Systems: Implement organization-specific classification protocols
- Workflow Adaptation: Customize analytical workflows for operational requirements
- Brand Integration: Develop custom personality cores reflecting organizational communication standards
- Security Compliance: Ensure adherence to organizational security and compartmentalization policies
API Development
# Example custom module integration
class CustomAnalysisModule:
def __init__(self, classification_level="UNCLASSIFIED"):
self.classification = classification_level
self.tsukuyomi_core = TSUKUYOMIFramework()
def execute_analysis(self, data):
return self.tsukuyomi_core.process_module(
module_id="custom_analysis",
input_data=data,
classification=self.classification
)Contributions are welcome! Please read our contributing guidelines and code of conduct before submitting pull requests.
For support, documentation, or questions about the TSUKUYOMI framework, please open an issue in this repository.
This project is licensed under the MIT License - see the LICENSE file for details.
This software is provided for EDUCATIONAL AND RESEARCH PURPOSES ONLY. By using, modifying, or distributing this software, you acknowledge and agree to the following terms:
- This framework is designed for legitimate intelligence analysis and research only
- Users are solely responsible for ensuring compliance with all applicable laws and regulations
- NOT intended for malicious, illegal, or unauthorized activities
- Users must obtain proper authorization before analyzing any sensitive or classified information
- The author(s) and contributors DISCLAIM ALL LIABILITY for any misuse of this software
- NO WARRANTY of any kind, express or implied, including but not limited to merchantability or fitness for a particular purpose
- Users assume FULL RESPONSIBILITY for all consequences of using this software
- The author(s) are NOT LIABLE for any damages, losses, or legal consequences arising from use
- Users in intelligence, security, or analytical roles must follow their organization's policies and legal obligations
- Compliance with classification handling, data protection, and operational security requirements is the USER'S RESPONSIBILITY
- Any analytical outputs must be validated through appropriate professional channels
- Redistributions must include this disclaimer in its entirety
- Modifications to the framework do not transfer liability to original authors
BY USING THIS SOFTWARE, YOU ACKNOWLEDGE THAT YOU HAVE READ, UNDERSTOOD, AND AGREE TO BE BOUND BY THESE TERMS.
TSUKUYOMI: Bringing structured intelligence to complex analytical challenges
