researchvault

What This Skill Does

ResearchVault is a high-velocity research orchestration engine designed specifically for OpenClaw agents that require long-term memory and complex synthesis. It functions as the central nervous system for knowledge acquisition, moving beyond simple web scraping by providing a persistent, searchable, and verifiable repository of research data. At its core, it leverages local SQLite storage to maintain a structured state of artifacts, findings, and hyperlinks, ensuring that no insight is lost during long-running research tasks. The engine supports divergent reasoning, allowing agents to spawn multiple parallel research branches and competing hypotheses to explore topics from various angles without cluttering the primary knowledge base. With its integrated synthesis engine, it uses local embeddings to discover latent connections between disparate pieces of information. Furthermore, the Active Verification feature allows agents to initiate self-correcting cycles, ensuring the reliability of data. By acting as a native MCP (Model Context Protocol) server, ResearchVault enables seamless collaboration, allowing multiple agents to contribute to or query the same research state simultaneously.

Installation

To integrate ResearchVault into your OpenClaw environment, ensure you have Python 3.13 and uv installed. Run the following command in your terminal:

clawhub install openclaw/skills/skills/lraivisto/vault-research

Once installed, you can initialize a new research project using the CLI: uv run python scripts/vault.py init --id "my-project" --name "My Research" --objective "Define research goal"

Use Cases

ResearchVault is ideal for complex, multi-step investigation projects such as market intelligence gathering, technical deep-dives, competitive analysis, or academic literature reviews. It is particularly effective in scenarios where information is spread across multiple platforms and needs to be synthesized into a coherent narrative. The watchdog mode is excellent for trend monitoring, where an agent needs to keep an eye on evolving social media threads or changing documentation sites.

Example Prompts

1. "Initialize a new project called 'Quantum Computing Trends' and start scuttling the latest papers from the top 5 research domains."
2. "Review the current findings in the 'Quantum' vault, identify any low-confidence data, and start a verification mission to confirm these claims."
3. "Synthesize all findings from my last three branches and generate a final summary report on the potential impact of modular chips."

Tips & Limitations

• Resource Management: Because the synthesis engine performs local embedding calculations, ensure your machine has sufficient RAM for larger datasets.
• Persistence: While SQLite is robust, always perform manual backups of your vault directory if you are working on highly mission-critical data.
• Verification: Always review the results of verify plan before the agent executes potentially expensive API calls or deep recursive scrapes. This helps save token costs and avoids redundant data collection.