gongnyang-prompt-kit: 막연한 요청을 gpt-image-2 완성 프롬프트로 컴파일하는 Claude Code 스킬

by gongnyang · AI Tool · ★ 328

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

How to install gongnyang-prompt-kit

git clone https://github.com/gongnyang/gongnyang-prompt-kit

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 58/100

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

Stars328
Forks53
LanguageJavaScript
CategoryAI Tool
LicenseMIT
Quality Score58/100
Last Updated2026-07-27
Created2026-06-22
Platformsclaude-code, node
Est. Tokens~15k

About gongnyang-prompt-kit

🐾 공냥 프롬프트 킷 VOL.2 막연한 한마디를 gpt-image-2 완성 프롬프트로 컴파일하는 Claude Code 스킬.       데모 사이트 · 설치 · 라우팅 · English · 日本語 거친 요청 → 완성 프롬프트 → 검증기 통과 — 세 단계가 스킬 안에서 끝난다. 위 키비주얼도 이 킷으로 컴파일한 프롬프트(C11 시네마틱 키아트)로 생성했다. 왜 필요한가 나이브 프롬프트는 진다 — "포스터 하나 만들어줘" 수준의 한 줄을 그대로 넣으면, 모델은 그 막연함 그대로의 결과를 돌려준다. 컴파일러 접근 — 요청 신호를 라우팅 표에서 한 행으로 매핑하고, 그 패턴 하나로 바로 생성에 넣을 수 있는 완성 한국어 프로덕션 프롬프트를 만든다.

Compatible Skills

These tools work well together with gongnyang-prompt-kit for enhanced workflows:

  • gpt-image-2-gen-skill — semantic(0.75)+complementary+same_lang+similar_pop+shared_platform (75%)
  • ilab-gpt-conjure — semantic(0.46)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (74%)
  • ilab-conjure — semantic(0.46)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (74%)
  • gpt-image-skill — semantic(0.46)+complementary+shared_fw(openai)+same_lang+similar_pop+shared_platform (74%)

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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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