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Starter template for creating new skills. Use when defining a new skill scaffold with frontmatter and concise execution instructions.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
| name | template-skill |
| description | Starter template for creating new skills. Use when defining a new skill scaffold with frontmatter and concise execution instructions. |
Production prompt-engineering pipeline for GPT-Image-2 / OpenAI image generation. Pairs a 'media-designer' agent with a hosted searchable corpus of 3,238 community-vetted prompts, decomposed across 10 controlled vocabularies (subjects, styles, lighting, cameras, moods, palettes, compositions, mediums, techniques, usecases). Each record carries: full prompt body, twitter/X attribution link, downloaded reference image. Workflow: agent diagnoses the user brief → searches the corpus → picks a mood-aligned base → refactors the chosen prompt into a parameterised {argument} template → resolves arguments from user intent → returns the final paste-ready prompt with attribution + reference image. Use when the user wants a polished image-generation prompt for ads, posters, product shots, portraits, character sheets, UI mockups, infographics, exploded-view diagrams, or any other GPT-Image-2 / OpenAI image task.
Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Codex, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
Search technical documentation using executable scripts to detect query type, fetch from llms.txt sources (context7.com), and analyze results. Use when user needs: (1) Topic-specific documentation (features/components/concepts), (2) Library/framework documentation, (3) GitHub repository analysis, (4) Documentation discovery with automated agent distribution strategy
Package entire code repositories into single AI-friendly files using Repomix. Capabilities include pack codebases with customizable include/exclude patterns, generate multiple output formats (XML, Markdown, plain text), preserve file structure and context, optimize for AI consumption with token counting, filter by file types and directories, add custom headers and summaries. Use when packaging codebases for AI analysis, creating repository snapshots for LLM context, analyzing third-party libraries, preparing for security audits, generating documentation context, or evaluating unfamiliar codebases.
Apply structured, reflective problem-solving for complex tasks requiring multi-step analysis, revision capability, and hypothesis verification. Use for complex problem decomposition, adaptive planning, analysis needing course correction, problems with unclear scope, multi-step solutions, and hypothesis-driven work.
Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Codex, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.
