openclaw-auto-researchOpenClaw 原生的自动化研究 pipeline。从一个研究 topic 出发，经过 23 个 stage 产出完整论文。 每个 Phase 由独立 sub-agent 执行（context 隔离），Phase 间通过文件系统传递产出。 触发词：Research X、跑研究、文献调研、写论文、研究 pipel...
Install via ClawdBot CLI:
clawdbot install lixiang1076/openclaw-auto-researchGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://export.arxiv.org/api/queryUses known external API (expected, informational)
arxiv.orgAudited Apr 18, 2026 · audit v1.0
Generated May 6, 2026
A researcher provides a research topic, and the pipeline automatically performs literature search, experiment design, code execution, and full paper drafting across 23 stages, delivering a complete manuscript ready for submission to a target venue.
A PhD student uses the pipeline to conduct a comprehensive literature survey on a specific research area. The system searches arXiv and Semantic Scholar, filters relevant papers, extracts key findings, and generates a structured literature review section for their thesis or paper.
An R&D team member inputs a research hypothesis related to a product improvement. The pipeline scopes the problem, searches prior art, designs experiments, runs code, and produces a technical report with insights, accelerating the innovation cycle.
A grant writer specifies a research topic. The pipeline generates a comprehensive background section including state-of-the-art analysis, gaps identification, and proposed methodology outline, saving weeks of manual effort.
In a graduate-level research methods class, students use the pipeline to practice the full research workflow from topic scoping to paper writing. The system provides automated feedback at each phase, helping students learn the process.
Offer the auto-research pipeline as a cloud service with tiered pricing based on the number of projects (papers) per month or per user. Users pay a monthly or annual fee for access to the pipeline, with premium tiers for faster execution or higher quality sub-agents.
Provide a free tier with limited features (e.g., up to 5 stages or limited literature search). Charge per completed paper or per phase for full access, with discounts for bulk usage. This model attracts users with a taste of the value and converts them to paying customers.
License the pipeline to universities, research labs, or corporate R&D departments for unlimited use by their members. Include custom integration with internal tools, dedicated support, and training. Charge an annual site license fee based on the number of users.
💬 Integration Tip
To integrate, ensure your environment has Python 3 and required dependencies, then configure the skill workspace path. For production use, set up robust error handling, memory for history, and a secure way to manage API keys for literature search services like arXiv and Semantic Scholar.
Scored Apr 19, 2026
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