Openclaw Workspace Pro

Sets up a production-ready OpenClaw workspace with artifact workflows, secure secrets, memory compaction, and long-running agent patterns via one command.

This package appears coherent and low-risk, but take these precautions before installing: 1) Verify the repository source (https://github.com/Eugene9D/openclaw-workspace-pro) and inspect templates (templates/*) for any content you don't want added. 2) Run the installer in a test or staging workspace first (not a production workspace) to confirm behaviour and backups. 3) Back up your existing /data/.openclaw/workspace before running install.sh (install will write files there and append to AGENTS.md/TOOLS.md). 4) Review the created .env template and never paste secrets into docs; populate .env manually and keep it gitignored. 5) Note the network allowlist guidance in TOOLS-additions.md and customize it; the templates include a permissive set of allowed domains you should tailor to your environment. 6) Run the installer as an unprivileged user where possible (avoid running as root) so accidental wide filesystem changes are limited. 7) If you want to limit agent autonomy, remember skills can be invoked by agents by default—consider restricting autonomous invocation on your platform if you have strict controls. Overall the package is consistent with its stated purpose; the remaining steps are standard operational hygiene.

Type: OpenClaw Skill Name: openclaw-workspace-pro Version: 1.0.0 This skill bundle is designed to enhance an OpenClaw agent's workspace with production-ready patterns, focusing on security, organization, and long-running capabilities. The `install.sh` script performs standard file system operations (creating directories, copying templates, appending to existing documentation files like `AGENTS.md` and `TOOLS.md`) within the designated workspace. Crucially, the instructions in `SKILL.md` and the appended template files (`templates/AGENTS-additions.md`, `templates/TOOLS-additions.md`, `templates/MEMORY-COMPACTION.md`) actively promote strong security practices for the AI agent, such as secure secrets management via `.env` (explicitly forbidding plaintext credentials in committed files or chat logs), establishing a network allowlist, and explicitly prohibiting access to services like Pastebin or Ngrok without security review. There is no evidence of malicious intent, data exfiltration, unauthorized execution, or prompt injection designed to subvert the agent; instead, the instructions aim to guide the agent towards secure and organized operation.