gpt-image-2-skill: GPT Image 2 skill for Claude Code — best-in-class typography, two endpoints, Fal AI integration

Security audit verdict: SAFE · quality 64/100

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. Full audit →

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.

Quick Facts

About gpt-image-2-skill

GPT Image 2 Skill Generate or edit images with OpenAI's GPT Image 2 — best-in-class for fine typography — via Fal AI. A Claude Code skill that wires GPT Image 2 (ChatGPT Images 2.0) into your agent in one drop-in file. Two endpoints: text-to-image AND image edit (with optional masks). Sync API, no polling, single POST.

Compatible Skills

These tools work well together with gpt-image-2-skill for enhanced workflows:

• gongnyang-prompt-kit — semantic(0.37)+complementary+shared_fw(openai)+similar_pop+shared_platform (61%)
• gpt-image-skill — semantic(0.34)+complementary+shared_fw(openai)+similar_pop+shared_platform (60%)
• chatgpt-imagegen — semantic(0.31)+complementary+shared_fw(openai)+similar_pop+shared_platform (59%)
• image-use — semantic(0.31)+complementary+shared_fw(openai)+similar_pop+shared_platform (59%)
• mcp-image — semantic(0.22)+complementary+shared_fw(openai)+similar_pop+shared_platform (56%)

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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:

• SlowMist agent-security taxonomy
• Liu et al., An Empirical Study of Agent Skill Security (2026)
• Our census dataset (DOI 10.5281/zenodo.21292799) · Hugging Face mirror
• How we grade: all 42 rules, the source tiers and known limits
• Full methodology & who runs this