eleutherios

What This Skill Does

Eleutherios is an advanced epistemic analysis infrastructure designed to help users decode complex information environments by querying local knowledge graphs. Unlike traditional search engines that rely on keyword indexing, Eleutherios treats document collections as interconnected networks, allowing it to perform deep-structural analysis on how information is shared, suppressed, or coordinated across diverse sources.

The tool utilizes claim-level extraction to identify subtle patterns in text, including:

• Suppression Detection: It tracks evidence of career obstacles, funding irregularities, and institutional marginalization.
• Coordination Signatures: It identifies non-random timing patterns and shared linguistic markers that often signal coordinated messaging campaigns.
• Multi-Perspective Clustering: It organizes conflicting viewpoints into clusters, providing a neutral overview of contested debates.
• Source Topology Analysis: It maps citation networks to reveal the 'trust' architecture behind your documents.

Installation

Eleutherios is designed for local-first operations and requires a Docker environment.

1. Begin by cloning the repository from the project GitHub: git clone https://github.com/Eleutherios-project/Eleutherios-docker.git.
2. Navigate to the folder and run docker-compose up -d to launch the core services.
3. Verify the MCP server is active by checking the health endpoint: curl http://localhost:8100/health.
4. Install the OpenClaw skill using the terminal command: clawhub install openclaw/skills/skills/eleutherios-project/eleutherios.
5. Import your data using the CLI tool: docker exec -it eleutherios-api python aegis_import_wizard.py /path/to/your/documents.

Use Cases

• Researching Contested Debates: Use Eleutherios to compare competing scientific papers and see how different institutions frame controversial theories.
• Auditing Institutional Transparency: Analyze declassified materials or congressional records to identify suppressed research threads or 'dead-end' citations.
• Media Literacy: Detect coordination patterns in news reporting to understand if disparate sources are part of a unified narrative strategy.
• Academic Investigation: Map the citation topology of a specific field to identify key influencers and potential information echo chambers.

Example Prompts

1. "Analyze the topic of electrogravitics in my collection - what sources exist and what patterns emerge regarding suppression?"
2. "Get perspectives on plasma propulsion research from my knowledge graph and cluster the conflicting viewpoints."
3. "What does Eleutherios show about coordination signatures within the latest batch of congressional testimony documents?"

Tips & Limitations

• Data Quality Matters: The accuracy of the analysis is directly dependent on the quality and diversity of your ingested documents.
• Hardware Requirements: Large document collections require significant RAM for the knowledge graph construction phase; ensure your Docker environment has at least 8GB of memory allocated.
• Local-First Privacy: Because all analysis occurs locally, your sensitive document collections are never uploaded to the cloud, ensuring total epistemic privacy. However, this means you are responsible for maintaining your own document ingestion pipeline.