by TechNickAI · Codex Skill · ★ 75
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.
The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.
| Stars | 75 |
| Forks | 12 |
| Language | Python |
| Category | Codex Skill |
| Quality Score | 65/100 |
| Last Updated | 2026-08-04 |
| Created | 2026-02-02 |
| Platforms | claude-code, python |
| Est. Tokens | ~17k |
Give your AI assistant memory, skills, and autonomy. A shareable config that turns Claude Code into an AI that remembers you, connects to your tools, and runs workflows while you sleep. OpenClaw Config Most AI assistants forget you the moment the conversation ends. OpenClaw doesn't.
Looking for a openclaw-config alternative? If you're comparing openclaw-config with other codex skill tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.
Self-hosted AI assistant built on DeepAgents, LangChain and LangGraph: Telegram chat, scheduled tasks, MCP int
Personal AI OS for Entrepreneurs. Ultimate OpenClaw setup in minutes. Experience frictionless flow states in y
Outcome-based persistent memory MCP server for Claude Code and OpenCode. Good advice promoted, bad advice demo
Growing Expertise for Coding Agents — structured expertise files that accumulate over time, live in git, work
Finish What You Start — Context engineering for Claude Code. Sessions last 20+ exchanges instead of cr
Persistent memory for AI coding agents
Explore other popular codex skill tools:
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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: