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 resemble-ai · MCP Server · ★ 83
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is detect-skill safe to install? View the security audit →
Resemble Detect Skill An agent skill for deepfake detection and media intelligence — powered by Resemble AI. Give any AI agent the ability to detect AI-generated audio, images, and video, then analyze completed detection results using Resemble's Detect and Intelligence APIs. Compatible Agents Works with any agent that supports markdown skills: Install Via skills.sh (recommended): Manual: Copy into your agent's skills directory. What It Does This skill teaches your agent to use Resemble's Detect and Intelligence APIs directly: | Deepfa
| Stars | 83 |
| Forks | 10 |
| Category | MCP Server |
| License | Apache-2.0 |
| Quality Score | 71.610792224061/100 |
| Open Issues | 1 |
| Last Updated | 2026-09-23 |
| Created | 2026-04-08 |
| Platforms | claude-code, cli, gemini, mcp |
| Est. Tokens | ~8k |
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detect-skill is Agent skill for deepfake detection & media safety — detect AI-generated audio, images, and video with Resemble AI. It is categorized as a MCP Server with 83 GitHub stars.
You can find installation instructions and usage details in the detect-skill GitHub repository at github.com/resemble-ai/detect-skill. The project has 83 stars and 10 forks, indicating an active community.
detect-skill is released under the Apache-2.0 license, making it free to use and modify according to the license terms.
The top alternatives to detect-skill on Agent Skills Hub include shellward, kaggle-skill, WhyBuddy. 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.
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