ragflow-runbookEnd-to-end runbook for deploying, operating, troubleshooting, and monitoring RAGFlow (runtime ops only).
Install via ClawdBot CLI:
clawdbot install YitingOU/ragflow-runbookGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses system directories or attempts privilege escalation
sudo chownCalls external URL not in known-safe list
https://github.com/infiniflow/ragflow.gitUses known external API (expected, informational)
raw.githubusercontent.comAI Analysis
The skill's external API usage (RAGFlow) is consistent with its stated purpose of deployment and operations. The primary risk is the potential for privilege escalation via `sudo chown` commands, which could be misused if the skill is maliciously modified, but the current definition shows no evidence of credential harvesting, data exfiltration, or hidden malicious instructions.
Generated Mar 21, 2026
IT teams use this skill to deploy and maintain RAGFlow instances for internal knowledge management, ensuring uptime and troubleshooting startup or connectivity issues. It guides them through environment setup, health checks, and API validation to support departments like HR or R&D.
MSPs deploy RAGFlow for multiple clients, using this skill to standardize installation, monitor system health, and perform automated smoke tests. It helps them quickly diagnose port conflicts or service failures across different client environments, reducing support tickets.
DevOps engineers integrate RAGFlow into CI/CD pipelines, using this skill to automate deployment, run readiness checks, and send alerts via OpenClaw for failures. It ensures reliable retrieval-augmented generation services in production environments for applications like customer support bots.
Universities or training centers deploy RAGFlow for research or student projects, using this skill to set up instances on lab servers, troubleshoot performance issues, and validate API access for educational tools. It simplifies maintenance for non-expert staff.
Startups use this skill to quickly spin up RAGFlow for prototyping AI-driven features, such as document search or chatbots, focusing on deployment and basic operations without deep infrastructure knowledge. It helps validate concepts before scaling to production.
Offer ongoing support packages for RAGFlow deployments, including monitoring, troubleshooting, and updates, charged monthly or annually. This model provides predictable revenue from clients needing reliable AI infrastructure without in-house expertise.
Provide one-time or project-based services to deploy and configure RAGFlow for clients, using this skill to ensure best practices and smooth integration. Revenue comes from fixed fees or hourly rates for setup, training, and initial health checks.
License the custom scripts included in this skill (e.g., ragflow_ping.py, ragflow_alert.py) as part of a broader ops toolkit, selling licenses to enterprises for internal use. This model generates revenue through one-time purchases or annual licenses.
💬 Integration Tip
Integrate this skill with existing monitoring tools like Prometheus or alerting systems via OpenClaw to automate health checks and reduce manual intervention in production environments.
Scored Apr 19, 2026
Audited Apr 16, 2026 · audit v1.0
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