unbagrnd: Free, Fast & Open-source AI-powered background remover for Windows, macOS, Linux and Android

by zidniryi · Agent Tool · ★ 172

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

Security audit verdict: SAFE · quality 68/100

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.

aiai-toolsandroidandroid-appbackground-removalcomputer-visioncross-platformfree-softwareimage-editingimage-processing

Quick Facts

Stars172
Forks25
LanguageRust
CategoryAgent Tool
LicenseMIT
Quality Score68/100
Last Updated2026-10-14
Created2026-08-24
Platformsrust
Est. Tokens~24k

About unbagrnd

unbagrnd A small desktop app that uses AI to remove the background from images — entirely on your device. No cloud API, no account, no telemetry, and no internet access required after a one-time setup step. On-device AI. Background removal runs through a real neural network (ONNX Runtime), inferred locally on your machine. No image, filename, or metadata is ever sent anywhere.

Compatible Skills

These tools work well together with unbagrnd for enhanced workflows:

  • EchoIsland — semantic(0.25)+complementary+same_lang+similar_pop+shared_platform (59%)
  • imagesorcery-mcp — semantic(0.41)+complementary+rare_topics+similar_pop (58%)

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How this security grade is produced

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:

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