by alvinreal · Codex Skill · ★ 212
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is awesome-openclaw-tips safe to install? View the security audit →
Awesome OpenClaw Tips The practical playbook for turning OpenClaw from a fun chatbot into a reliable operating system for recurring work. These tips were gathered from hands-on use, docs, community setups, and repo deep-dives; see Acknowledgements. Table of Contents 💬 Messages MES-01: Enable ack reactions by setting messages.ackReactionScope 📨 Telegram [TEL-01: Use Telegram inline buttons for recurring actions](#tel-01-use-
No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
| Stars | 212 |
| Forks | 23 |
| Language | TypeScript |
| Category | Codex Skill |
| Quality Score | 60/100 |
| Open Issues | 2 |
| Last Updated | 2026-05-17 |
| Created | 2026-03-21 |
| Platforms | node |
| Est. Tokens | ~385k |
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awesome-openclaw-tips is Practical OpenClaw tips for memory, reliability, cost, automation, and multi-agent workflows.. It is categorized as a Codex Skill with 212 GitHub stars.
awesome-openclaw-tips is primarily written in TypeScript. It covers topics such as agents, ai, ai-assistant.
You can find installation instructions and usage details in the awesome-openclaw-tips GitHub repository at github.com/alvinreal/awesome-openclaw-tips. The project has 212 stars and 23 forks, indicating an active community.
The top alternatives to awesome-openclaw-tips on Agent Skills Hub include Awesome-AI-For-Security, awesome-openclaw, awesome-sre-agents. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: