Here’s a secure-by-default OpenClaw setup that matches “reviewer-based” norms (the bot can propose + prepare, but a human must approve + execute anything risky). I’ll structure this as a checklist you can implement in order. (References: skill structure + safety patterns , workflows + reviewer gating , and diagnostic commands .) 1) Decide your security posture (default: “read-mostly, write-by-approval”) Default policy (recommended): - ✅ Read access: OK (calendar read, repo read, docs read, web research) - ⚠️ Write access: allowed only with explicit approval steps - ❌ Destructive actions: never without “type-the-confirmation-phrase” approval This mirrors the “submit PR → wait for human approval → deploy” pattern described in the autonomous dev workflow. 2) Isolate the environment (separation prevents “oops” from becoming “breach”) Minimum isolation: - Run OpenClaw on a dedicated machine / VM (don’t co-mingle with your personal daily-driver). - Use a separate OS user account for the bot. - Keep a clean boundary between: This aligns with the “dedicated accounts + limited permissions + approval workflows” guidance. Network hygiene (secure defaults): - Prefer outbound-only connectivity. - If you need inbound control UI access, restrict by VPN / allowlist. - Turn on OS firewall; block unnecessary ports. 3) Use dedicated accounts + least privilege everywhere Create dedicated “bot” identities: - Email account for the bot (no access to your personal inbox) - GitHub user/service account (scoped to only necessary repos) - Separate API keys per integration (don’t reuse your personal keys) Permissions: - Start with read-only scopes. - Add write scopes only after the bot proves reliability on a narrow workflow. - For GitHub: prefer “PR creation” over “push to main”; require reviews for merges. This matches the security section (dedicated accounts, limit sensitive info, approvals). 4) Secrets management: “no secrets in prompts, logs, or skill files”

1) which model to use. Using gemini 3 flash atm but it will add up on heavy ussge. For now i use Molt for planning, advice, research, strategies, it builds a few tools for itself, content creation. I will expand to automations, website building, tool, app creation. 2) how to manage multiple unrelated projects at once without leaking or interfering 3) how to use different models depending on use cases

Security-hardened. Production-ready. Deploy in minutes, not days. What you get out of the box: - Container isolation — Agent crashes don't take down your server - Authentication by default — Unique gateway tokens, no exposed endpoints - Hardened configuration — Firewall, non-root execution, fail2ban - Private access controls — Only approved devices connect - TLS-secured — All traffic encrypted end-to-end No more: - Manual security hardening nightmares - "Is my gateway exposed?" anxiety - 40-hour DevOps learning curves - Fragile laptop-dependent setups The real story: DigitalOcean (10M+ developers) just validated OpenClaw as production-grade. That's huge. Best for: → Client deployments that need security compliance → Always-on agents (actual uptime) → Going from experiment → production without rebuilding everything How to get started: DigitalOcean Marketplace → Search "OpenClaw" → 1-Click Deploy What questions do you have about the security setup? Let's talk about how this changes your deployment strategy. Who's spinning one up? 👇 I will review the deployed config and run my custom gpt to validate the DO build #OpenClaw #Moltbot #AIAgents #DigitalOcean #DevOps

I'm as excited as you. I will be working hard over the next week to build out this community. I'm offering 10 random people access to this community for free. Who will be the first to post?