ResearchClaw is an end-to-end autonomous research pipeline that orchestrates 23 stages from hypothesis generation to conference-ready LaTeX paper export.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install researchclaw
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 researchclaw 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 ResearchClaw?
ResearchClaw is a sophisticated autonomous research engine designed to handle the entire lifecycle of scientific inquiry without manual oversight. As a standout among Openclaw Skills, it automates complex workflows including literature reviews, problem decomposition, and experiment design. It doesn't just theorize; it generates actual code, executes it in secure sandboxes, and performs statistical analysis on the resulting data.
The skill is built for developers and researchers who need to move from a vague topic to a structured, data-backed paper draft in record time. By utilizing an agentic pipeline, it ensures that every step—from the initial hypothesis to the final multi-agent peer review—is executed with technical rigor and academic formatting standards, including LaTeX support for tables and charts.
ResearchClaw Use Cases
- Creating full academic paper drafts from a single research prompt.
- Validating scientific hypotheses through autonomous code generation and sandbox execution.
- Streamlining the literature review and problem decomposition phase for new technical projects.
- Generating reproducible experiment results and data visualizations for conference submissions.
- Leveraging Openclaw Skills to conduct automated peer reviews on technical documentation or research.
How ResearchClaw Works
- The pipeline begins by initializing the research topic and decomposing it into specific, solvable sub-problems.
- It performs a literature review and generates testable hypotheses based on the identified gaps.
- ResearchClaw designs an experiment and writes the Python code necessary to test the hypotheses.
- The generated code is executed in a controlled environment (local sandbox or remote SSH) to collect data.
- Statistical analysis is performed on the raw results, aggregating data into tables and generating visual charts.
- The skill drafts a comprehensive paper, passes it through an internal peer-review process, and exports it as a LaTeX-ready document.
ResearchClaw Setup
To integrate ResearchClaw into your Openclaw Skills workflow, follow these configuration steps:
- Check for the existence of the configuration file:
ls config.yaml || ls config.researchclaw.example.yaml
- Create your local configuration file:
cp config.researchclaw.example.yaml config.yaml
- Input your LLM API credentials into the
config.yamlfile under thellmsection. - Launch the autonomous pipeline via the command line:
researchclaw run --topic "Your specific research topic" --auto-approve
ResearchClaw Data Schema & Taxonomy
ResearchClaw generates a structured hierarchy of artifacts for every run to ensure full reproducibility and transparency:
| Directory/File | Description |
|---|---|
stage-1/ to stage-9/ |
Planning, decomposition, and literature review artifacts. |
stage-10/experiment.py |
The autonomous Python code generated to test the hypothesis. |
stage-12/runs/ |
Raw JSON output and execution logs from the experiment runs. |
stage-14/results_table.tex |
Aggregated data formatted specifically for LaTeX documents. |
stage-17/paper_draft.md |
The full Markdown draft of the research paper. |
stage-22/charts/ |
High-resolution PNG visualizations of experiment trajectories and comparisons. |
pipeline_summary.json |
A high-level overview of the pipeline's status and results. |
ResearchClaw Advanced Features
- Iterative pipeline mode that runs multiple rounds of experimentation to achieve statistical convergence.
- Support for remote execution on GPU servers via SSH for compute-intensive research tasks.
- Resumable workflow architecture allowing users to restart the process from any of the 23 specific stages.
- Flexible execution modes including simulated results, local subprocess sandboxing, and remote clusters.
- Integrated multi-agent peer review system that provides critical feedback on the generated paper before finalization.
SKILL.md
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