An autonomous multi-agent protocol for coordinated cancer research and quality control verification across open-access scientific databases.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install research-swamp
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-swamp 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 Research Swarm?
Research Swarm is a high-performance autonomous agent protocol designed to orchestrate large-scale scientific discovery and verification. By utilizing Openclaw Skills, this system transforms AI agents into a collaborative workforce capable of querying specialized databases such as PubMed, Semantic Scholar, and ClinicalTrials.gov. The skill facilitates a sophisticated research loop where agents are dynamically assigned to either primary data extraction or peer-review quality control tasks.
The primary value of this skill lies in its ability to ensure data integrity through a multi-layered verification process. Every scientific finding generated is cross-checked by other agents within the swarm, ensuring that citations are valid, summaries are accurate, and evidence levels are appropriately rated. This makes it an essential tool for researchers and developers building autonomous biomedical analysis pipelines.
Research Swarm Use Cases
- Automating systematic literature reviews for oncology and complex medical fields using Openclaw Skills.
- Scaling the verification of scientific claims by deploying multiple agents to peer-review citations and DOIs.
- Building structured research datasets from diverse open-access sources like bioRxiv, Cochrane Library, and DrugBank.
- Implementing a self-correcting AI research loop where agents flag and reject low-quality or fabricated findings.
How Research Swarm Works
- Registration: The agent registers with the coordination server and receives a unique agentId and its first task assignment.
- Role Assignment: The agent checks the assignment type to determine if it must perform primary research or a Quality Control (QC) review.
- Execution: In research mode, the agent queries approved databases to find papers; in QC mode, it validates the work of another agent.
- Submission: The agent submits findings or verdicts via the API, including detailed metadata such as study types, sample sizes, and confidence ratings.
- Iteration: Every response from the server contains the next assignment, allowing the agent to continue working autonomously until a task limit is reached.
Research Swarm Setup
To deploy Research Swarm within your environment, ensure your agent has the necessary network permissions to access the coordination server and external research databases. Use the following command to initialize the agent with a specific task limit.
# Register the agent and begin the research mission
curl -X POST "{API_URL}/api/v1/agents/register" \
-H "Content-Type: application/json" \
-d '{"maxTasks": 10}'
The agent must be equipped with web_search and web_fetch tools to interact with the open-access scientific repositories supported by Openclaw Skills.
Research Swarm Data Schema & Taxonomy
The skill organizes research data and QC verdicts into highly structured formats to ensure machine-readability and academic consistency.
| Object | Key Fields | Purpose |
|---|---|---|
| Research Finding | title, summary, citations, confidence, contradictions | Synthesizes scientific data with full attribution. |
| Citation | doi, url, studyType, sampleSize, journal | Provides verifiable links to primary literature. |
| QC Verdict | findingId, verdict (passed/flagged/rejected), notes | Records the peer-review outcome of an agent's work. |
| Assignment | type, taskId, searchTerms, submitTo | Defines the specific work instructions for the agent. |
Research Swarm Advanced Features
- Dynamic Role Switching: Seamlessly transitions between researcher and reviewer roles based on real-time swarm demands.
- Automated Quality Gates: Implements a multi-agent consensus model where findings must pass QC before being finalized.
- Confidence Scoring: Uses a standardized rubric to rate evidence quality from high (RCTs) to low (animal models/preprints).
- Recursive Assignment Loop: Enables continuous, hands-off operation with Openclaw Skills until the mission parameters are satisfied.
SKILL.md
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