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 hraness · MCP Server · ★ 7
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is slopcamera safe to install? View the security audit →
Slopcamera lets Codex, Claude Code, and other coding agents make images, diagrams, animation, 3D scenes, Blender films, and edited video from source files they can keep revising.
| Stars | 7 |
| Forks | 1 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63.8207830433463/100 |
| Open Issues | 6 |
| Last Updated | 2026-10-02 |
| Created | 2026-07-24 |
| Platforms | claude-code, cli, codex, mcp, node |
| Est. Tokens | ~18k |
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slopcamera is Slopcamera lets Codex, Claude Code, and other coding agents make images, diagrams, animation, 3D scenes, Blender films, and edited video from source files they can keep revising.. It is categorized as a MCP Server with 7 GitHub stars.
slopcamera is primarily written in TypeScript. It covers topics such as agent-skills, ai-agents, ai-video-generation.
You can find installation instructions and usage details in the slopcamera GitHub repository at github.com/hraness/slopcamera. The project has 7 stars and 1 forks, indicating an active community.
slopcamera is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to slopcamera on Agent Skills Hub include mcp-video, sheets-cli, markdy-com. 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: