open-image-prompts — security grade SAFE, quality 69/100

Security audit verdict: SAFE · quality 69/100

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 NanmiCoder · Agent Tool · ★ 249

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

🔒 Is open-image-prompts safe to install? View the security audit →

About open-image-prompts

English · 简体中文 Open Image Prompts An open, local-first visual prompt archive with two installable Agent Skills: turns a rough brief into a clear art direction. retrieves traceable prompt-image references and opens a local comparison gallery. Working through a coding agent? AGENTS.md is the condensed setup, port, and Skill contract. The public dataset contains 18,610 source prompts, 33,023 images, 37,212 translations, 216,845 active v2 prompt labels, and a closed taxonomy of 185 visual labels. Labeling models, backfill tools, provider configuration, test runs, error logs, and other labeling-process records are not included. These counts are checked against by . Dataset assets ship through GitHub Releases instead of Git LFS: the repository clone stays small, and downloads the SQLite archive (80 MB) plus optional monthly image packs (4.3 GB total) with sha256 verification. See for the exact asset list. Repository vs. dataset assets This repository tracks the application code, frontend, API, Skills, taxonomy, and small dataset indexes. It intentionally does

image-skillimagespromptsopen-image-promptsopenimages

Quick Facts

Stars249
Forks34
LanguagePython
CategoryAgent Tool
LicenseMIT
Quality Score69.32065535169/100
Open Issues1
Last Updated2026-09-23
Created2026-07-23
Platformspython
Est. Tokens~15k

Compatible Skills

These tools work well together with open-image-prompts for enhanced workflows:

  • gpt_image_2_skill — semantic(0.52)+complementary+same_lang+similar_pop+shared_platform (68%)
  • z-image-studio — semantic(0.38)+complementary+same_lang+similar_pop+shared_platform (63%)
  • xiaohongshu-content-creator — semantic(0.33)+complementary+same_lang+similar_pop+shared_platform (62%)
  • imagesorcery-mcp — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)
  • oil-visual — semantic(0.32)+complementary+same_lang+similar_pop+shared_platform (61%)

open-image-prompts alternative? Top 6 similar tools

Looking for a open-image-prompts alternative? If you're comparing open-image-prompts with other agent tool tools, these 6 projects are the closest alternatives on Agent Skills Hub — ranked by topic overlap, star count, and community traction.

  • awesome-android-agent-skills by new-silvermoon · ⭐ 917

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  • claude-code-skill-factory by alirezarezvani · ⭐ 869

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  • agent-skills by hashicorp · ⭐ 860

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  • claude-plugins by Kamalnrf · ⭐ 563

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  • tutor-skills by RoundTable02 · ⭐ 400

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  • repren by jlevy · ⭐ 374

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Frequently Asked Questions

What is open-image-prompts?

open-image-prompts is Open, local-first visual prompt archive with traceable prompt-image references and installable Agent Skills.. It is categorized as a Agent Tool with 249 GitHub stars.

What programming language is open-image-prompts written in?

open-image-prompts is primarily written in Python. It covers topics such as image-skill, imagesprompts, open-image-prompts.

How do I install or use open-image-prompts?

You can find installation instructions and usage details in the open-image-prompts GitHub repository at github.com/NanmiCoder/open-image-prompts. The project has 249 stars and 34 forks, indicating an active community.

What license does open-image-prompts use?

open-image-prompts is released under the MIT license, making it free to use and modify according to the license terms.

What are the best alternatives to open-image-prompts?

The top alternatives to open-image-prompts on Agent Skills Hub include awesome-android-agent-skills, claude-code-skill-factory, agent-skills. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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