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 →
by EYamanS · Agent Tool · ★ 154
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
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Texel Studio The only pixel art tool that actually paints like a real artist. Every other AI pixel art generator is a diffusion model pretending to understand pixels. They output blurry approximations — inconsistent colors, broken edges, half-pixel artifacts, and results that look different every time you run the same prompt. They don't understand what a pixel is. Texel Studio is different. An AI agent picks up a brush, places pixels on a canvas one at a time, steps back to look at what it drew, and decides what to fix. It uses shapes, noise fills, and detail tools — the same way a human pixel artist works. Every pixel is intentional. Every color is from your palette. The output is exact, consistent, and game-ready. This is the open-source engine that powers texel.studio.
| Stars | 154 |
| Forks | 23 |
| Language | Python |
| Category | Agent Tool |
| Quality Score | 70.3207521312487/100 |
| Open Issues | 1 |
| Last Updated | 2026-07-02 |
| Created | 2026-04-01 |
| Platforms | python |
| Est. Tokens | ~16k |
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texel-studio is AI pixel art agent that paints like a real artist. Not diffusion — actual tool-based painting with shapes, noise, and per-pixel control.. It is categorized as a Agent Tool with 154 GitHub stars.
texel-studio is primarily written in Python. It covers topics such as ai-agent, ai-art, ai-pixel-art.
You can find installation instructions and usage details in the texel-studio GitHub repository at github.com/EYamanS/texel-studio. The project has 154 stars and 23 forks, indicating an active community.
The top alternatives to texel-studio on Agent Skills Hub include Nomi, ComfyUI_Skills_OpenClaw, UltraGameStudio. 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: