by ShimazuSystems | 2025-06-05 09:43:00+00:00
TSUKUYOMI - Modular Intelligence Analysis Framework (40+ modules for structured analysis)

I've been working on TSUKUYOMI, a modular framework that implements intelligence community analytical standards in AI systems. It's essentially a collection of 40+ specialized modules that can be orchestrated to perform structured analysis on complex data.

GitHub: https://github.com/ShimazuSystems/TSUKUYOMI/

What it does

Instead of relying on black-box AI responses, TSUKUYOMI enforces structured analytical methodologies:

- Analysis of Competing Hypotheses (ACH) - Systematically evaluates multiple explanations - Source reliability scoring - A-F reliability, 1-6 credibility (IC standards) - Multi-source correlation - Temporal, spatial, semantic, network analysis - Confidence quantification - Bayesian fusion with uncertainty propagation

Technical approach

modules/ ├── data_recognition_ingestion.tsukuyomi # Multi-INT data processing ├── correlation_analysis.tsukuyomi # Cross-source validation ├── strategic_scenario_modeling.tsukuyomi # Probabilistic forecasting ├── flight_data_analysis.tsukuyomi # ADS-B/MLAT processing └── web_search_osint.tsukuyomi # OSINT collection

Each module follows a structured execution sequence with explicit reasoning chains. The orchestration core dynamically selects and chains modules based on requirements.

Example: OSINT Analysis

{ "execution_sequence": [ { "operation": "Source Reliability Assessment", "actions": ["evaluate_source_access", "check_corroboration", "assign_confidence"], "output": "//SOURCE: Reliability B / Credibility 2" }, { "operation": "Entity Extraction", "actions": ["identify_persons", "map_relationships", "extract_technical_indicators"], "output": "//RESULT: 15 entities identified with confidence scores" } ] }

Why I built this

Current AI systems are great at pattern matching but poor at structured reasoning. In intelligence analysis, you need: - Explicit evidence chains - Alternative hypothesis consideration - Quantified uncertainty - Source attribution - Audit trails

TSUKUYOMI provides these through modular components that enforce analytical rigor.

Current implementation

- Runs in Claude AI environment (upload the .tsukuyomi files) - Modules are JSON-based configurations defining analytical workflows - Includes specialized processors for HUMINT, SIGINT, GEOINT, OSINT, CYBINT - Outputs professional intelligence products with proper confidence levels

Interesting technical bits

1. Multi-source information fusion (MSIF) at three levels: data, feature, and decision 2. Bayesian confidence integration across heterogeneous sources 3. Dynamic module orchestration based on information requirements 4. Classification-aware processing (handles UNCLASSIFIED through TOP SECRET)

Use cases beyond intelligence

- Research: Systematic literature review with source credibility scoring - Business: Competitive analysis with structured methodology - Engineering: Failure analysis using ACH framework - Security: Threat correlation across multiple data sources

Future development

- Standalone Python implementation - REST API for integration - Custom module development SDK - Visualization components for link analysis

Looking for feedback on: 1. The module architecture approach 2. Other domains where structured analysis would be valuable 3. Integration ideas with existing analytical workflows

The code is MIT licensed. Contributions welcome, especially domain-specific modules.