access-review
FreeConduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
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Secure OpenClaw deployments with preflight hardening checks, CI/CD guardrails, container runtime restrictions, and post-deploy verification. Use when shipping OpenClaw with Docker, Kubernetes, or automated release pipelines.
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See how to installReview the instructions the agent will receive before you install.
Use this skill to add repeatable security gates around OpenClaw build and deployment workflows.
Add mandatory controls to CI before artifacts are promoted:
Example CI step order:
# Build
npm ci
npm run build
# Security gates
trivy fs .
trivy image my-registry/openclaw:${GIT_SHA}
syft my-registry/openclaw:${GIT_SHA} -o spdx-json > sbom.json
cosign sign --key cosign.key my-registry/openclaw:${GIT_SHA}
Run OpenClaw with restrictive defaults:
no-new-privileges enabledKubernetes-oriented expectations:
runAsNonRoot: trueallowPrivilegeEscalation: falsereadOnlyRootFilesystem: trueRequire explicit promotion checks:
Avoid mutable latest tags for production OpenClaw services.
Run a hardening smoke test immediately after rollout:
kubectl get pods -n openclaw
kubectl auth can-i --as=system:serviceaccount:openclaw:default list secrets -n openclaw
kubectl get networkpolicy -n openclaw
kubectl logs deploy/openclaw -n openclaw --tail=200
Verify:
Maintain a hardened rollback workflow:
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Conduct periodic access reviews and certifications. Implement access governance and recertification workflows. Use when managing access compliance.
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Build automated evaluation suites for AI agents using golden datasets, rubrics, and regression gates. Use when shipping agent features, validating prompt changes, or gating deployments on quality.
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Instrument AI agents with tracing, token metrics, latency, and cost visibility. Use for reliability and debugging.
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Secure AI agents against prompt injection, tool abuse, and data exfiltration with defense-in-depth controls. Use when building, deploying, or hardening agentic AI systems that invoke tools, access data, or interact with production infrastructure.
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Secure AI coding agents (Claude Code, Cursor, Codex, Copilot) with permission boundaries, secret protection, code review gates, and safe sandbox configurations for team environments.
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Use service mesh patterns for AI inference traffic management, mTLS, canary releases, policy enforcement, and cross-cluster resilience.
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