security/hardening/openclaw-deployment-hardening/SKILL.md
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.
npx skillsauth add bagelhole/devops-security-agent-skills openclaw-deployment-hardeningInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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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:
development
Design and operationalize SRE dashboards that surface reliability, latency, error, saturation, and capacity signals across services. Use when building observability views for SLOs, incident response, and executive reliability reporting.
testing
Harden OpenClaw self-hosted environments with baseline host controls, auth tightening, secret handling, network segmentation, and safe update/rollback workflows. Use when deploying OpenClaw in home labs, startups, or production-like local AI infrastructure.
devops
Deploy, manage, and optimize vector databases for AI applications. Covers Qdrant, Weaviate, pgvector, and Pinecone — collection management, indexing strategies, backup, and performance tuning for production RAG and semantic search workloads.
testing
Deploy ML models on Kubernetes with KServe (formerly KFServing) and NVIDIA Triton Inference Server. Includes canary deployments, autoscaling, model versioning, A/B testing, and GPU resource management for production model serving.