by steve02081504 · Codex Skill · ★ 722
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
docker pull ghcr.io/steve02081504/fountTaken from the project's README on GitHub. Check the security audit before running it.
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. Full audit →
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 | 722 |
| Forks | 60 |
| Language | JavaScript |
| Category | Codex Skill |
| Quality Score | 49/100 |
| Open Issues | 6 |
| Last Updated | 2026-08-10 |
| Created | 2024-09-24 |
| Platforms | browser, node |
| Est. Tokens | ~20k |
TL;DR fount is a programmable, standardised, modular, customisable agent runtime platform that loads various parts to provide services. For code-related questions, see deepwiki. The name fount is all lowercase—it's ount, not ount.
Looking for a fount alternative? If you're comparing fount 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.
⭐ All-in-one AI companion! Super Agent Party = Self hosted neuro sama + openclaw! ⭐ 全能AI伴侣！超级智能体派对 = 自托管neuro
PowerMem: AI Memory Plugin— Accurate, Agile, Affordable. Make AI Agent smarter.
🍙 A personal AI agent & local memory hub for all AI agents, gives every AI one shared, fully controlled memo
✨ Self hosted, always-on AI agent platform run in containers. Create multiple bots with long memory, and conne
Make your own story. User-friendly software for LLM roleplaying
A simple yet powerful agent framework for personal assistants, designed to enable intelligent interaction, mul
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: