researchvault

What This Skill Does

ResearchVault is a high-velocity orchestration engine built specifically for agents that require deep, persistent, and verifiable research capabilities. Unlike transient memory modules, ResearchVault utilizes a robust local SQLite backbone to store artifacts, findings, and cross-referenced links, ensuring that your agent retains context throughout long-running tasks. Its core value proposition lies in its ability to support divergent reasoning, allowing agents to spawn multiple parallel research branches and hypotheses simultaneously. The built-in synthesis engine leverages local embeddings to identify latent connections between disparate data points, while the active verification module ensures that findings undergo a rigorous self-correction cycle before they are considered reliable. With its native MCP (Model Context Protocol) support and watchdog background monitoring, this skill turns a standard agent into a research powerhouse capable of autonomous data collection, synthesis, and peer-reviewed accuracy.

Installation

To integrate ResearchVault into your OpenClaw environment, ensure you have Python 3.13 and the uv package manager installed. Execute the following command in your terminal to pull the skill from the central repository:

clawhub install openclaw/skills/skills/lraivisto/researchbrain

Once installed, verify the setup by running the initialization script to prepare your local data vault: uv run python scripts/vault.py init --id "your-project-id" --name "Project Name".

Use Cases

• Market Intelligence: Aggregating news, sentiment data, and competitor movements into a searchable, synthesized knowledge base.
• Academic Literature Review: Tracking papers, hypotheses, and citations across multiple branches of research without losing provenance.
• Technical Due Diligence: Automating the ingestion of project documentation and continuously verifying claims against external live sources via Watchdog mode.
• Agentic Collaboration: Acting as the shared 'brain' for multiple agents connected via the MCP protocol to ensure consistent state across a multi-agent system.

Example Prompts

1. "ResearchVault, initialize a new project ID 'quantum-computing' and start scuttling the latest research papers from arXiv to identify recent breakthroughs in error correction."
2. "Run the synthesis engine on the 'metal-v1' vault to map connections between the scraped Reddit data and the verified band profiles; identify any low-confidence findings."
3. "Initiate a verification mission for the 'quantum-computing' findings that have a confidence score below 0.7 and generate a summary report."

Tips & Limitations

• Resource Management: Since this skill maintains local SQLite databases and performs embedding operations, ensure your environment has sufficient disk space and memory for large-scale projects.
• Watchdog Frequency: Be mindful when using Watchdog mode; continuous background monitoring of URLs may trigger rate limiting on external websites.
• Verification Cycles: Always review the output of verify plan before finalizing high-stakes research, as automated logic can sometimes produce false positives during early-stage hypotheses.
• Data Integrity: Regularly backup your project SQLite files located in your local data directory to prevent potential loss during rapid branch switching.