deepinspect-openclaw-guardrailsProvides deterministic preflight risk classification for commands, returning allow, require approval, or block decisions with detailed reason codes.
Install via ClawdBot CLI:
clawdbot install parmindersk/deepinspect-openclaw-guardrailsGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Accesses sensitive credential files or environment variables
~/.aws/credentialsPotentially destructive shell commands in tool definitions
rm -rf /Calls external URL not in known-safe list
https://x.y/z.shUses known external API (expected, informational)
api.anthropic.comGenerated Mar 22, 2026
Integrate DeepInspect Guardrails into CI/CD pipelines to automatically vet shell commands before execution, preventing risky operations like unauthorized remote script downloads or destructive file deletions. This ensures deployment safety and compliance by flagging commands that require manual approval, reducing human error in automated workflows.
Use the guardrails to monitor and control command-line actions in financial data analysis environments, blocking high-risk patterns such as secret access or system mutations that could compromise sensitive transaction data. It helps maintain audit trails with reason codes, ensuring regulatory compliance and protecting against insider threats in banking or fintech operations.
Apply the skill to secure healthcare IT systems by screening administrative commands for risks like privilege escalation or outside workspace access, which could lead to data breaches or system downtime. This supports HIPAA compliance by allowing only approved actions on patient data servers, enhancing operational reliability in hospitals or clinics.
Implement guardrails in computer science labs to prevent students from executing destructive commands like 'rm -rf' on shared systems, while allowing safe commands for learning. It provides explainable decisions with reason codes, helping instructors manage risk and teach cybersecurity best practices in academic settings.
Deploy the guardrails to secure e-commerce backend operations by vetting commands used for inventory updates or server maintenance, blocking patterns that could cause service outages or data loss. This ensures high availability and security during peak sales events, protecting customer transactions and business continuity.
Offer DeepInspect Guardrails as a cloud-based service with tiered pricing based on usage volume or number of protected endpoints, targeting enterprises needing scalable command security. Revenue streams include monthly subscriptions, premium support, and custom policy tuning services, appealing to DevOps teams and IT departments.
Sell perpetual licenses for on-premise deployment, allowing organizations with strict data sovereignty requirements to integrate the guardrails into their private infrastructure. Revenue comes from one-time license fees, annual maintenance contracts, and optional training workshops, ideal for regulated industries like finance or government.
Release a core version as open source to build community adoption, while monetizing through premium features like advanced analytics, enterprise support, and custom policy development. Revenue is generated from consulting, extended warranties, and add-on modules, leveraging a freemium model to attract startups and large firms alike.
💬 Integration Tip
Start by testing with common commands in a sandbox environment, then gradually integrate into production workflows using the provided CLI and policy tuning options for minimal disruption.
Scored Apr 19, 2026
AI Analysis
The skill is a local policy engine for command classification and does not appear to send user data externally. The primary risk is that its local policy file could be misconfigured to allow dangerous commands, but this is a deployment/configuration risk, not an inherent malicious behavior in the skill's code. The external API reference (api.anthropic.com) is informational from the rule-based scan and not part of the skill's execution flow as described.
Audited Apr 16, 2026 · audit v1.0
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