A sophisticated state management and orchestration engine designed to empower autonomous agents with persistent memory, synthesis capabilities, and verification workflows.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-vault
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install research-vault using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is ResearchVault?
ResearchVault is a high-velocity research orchestration engine built to manage the complex lifecycle of agentic data gathering. It provides a robust local infrastructure, often referred to as The Vault, which utilizes SQLite persistence to store artifacts, findings, and semantic links. As a standout entry among Openclaw Skills, it enables agents to move beyond simple search tasks into deep, structured knowledge discovery.
The skill excels at divergent reasoning, allowing developers to spawn parallel research branches and hypotheses. By integrating a synthesis engine that uses local embeddings for automated link discovery, ResearchVault ensures that disparate pieces of information are woven into a cohesive knowledge graph. This is further enhanced by active verification missions, where agents self-correct and validate low-confidence data points autonomously.
ResearchVault Use Cases
- Orchestrating multi-source data ingestion for complex market or academic research projects.
- Managing persistent state for long-running research agents that need to resume tasks across sessions.
- Exploring parallel research paths and divergent hypotheses to ensure comprehensive topic coverage.
- Automating the validation of gathered facts through self-correcting verification missions.
- Providing a unified context layer for multiple AI agents via the built-in MCP server support.
How ResearchVault Works
- Project Initialization: The process begins by defining a project ID and objective, which initializes the local SQLite persistence layer.
- Multi-Source Ingestion: Agents use the scuttle command to ingest data from URLs or social platforms, populating the vault with raw artifacts.
- Semantic Synthesis: The synthesis engine analyzes the vault using embeddings to discover and record semantic links between findings.
- Active Verification: The system identifies low-confidence data and generates verification plans for agents to validate or correct facts.
- MCP Integration: The state is exposed via a Model Context Protocol server, allowing other tools within the Openclaw Skills ecosystem to interact with the research data.
ResearchVault Setup
To get started with ResearchVault, ensure you have Python 3.13+ and uv installed. This skill is designed to integrate seamlessly with other Openclaw Skills.
# Initialize the environment
uv venv
uv pip install -e .
# Initialize a new research project
uv run python scripts/vault.py init --id "project-v1" --name "My Research" --objective "Specific Goal"
# Start data ingestion
uv run python scripts/vault.py scuttle "https://example.com" --id "project-v1"
ResearchVault Data Schema & Taxonomy
ResearchVault maintains a structured SQLite database to ensure high-performance access to research data. The schema includes the following core components:
| Component | Purpose |
|---|---|
| Artifacts | Stores raw data and source content from ingested URLs. |
| Findings | Contains specific facts and insights extracted by agents. |
| Links | Maps relationships and semantic connections between findings. |
| Hypotheses | Tracks divergent reasoning paths and parallel research branches. |
| Missions | Manages the status and results of active verification tasks. |
ResearchVault Advanced Features
- Divergent Reasoning: Support for branching research paths to explore multiple hypotheses simultaneously.
- Synthesis Engine: Automated discovery of relationships between findings using local vector embeddings.
- Watchdog Mode: Continuous background monitoring of specific URLs and search queries for real-time updates.
- Native MCP Server: Standardized protocol support for cross-agent communication and data sharing.
- Self-Correcting Verification: Autonomous planning and execution of data validation missions to improve information accuracy.
SKILL.md
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