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Turn a vague image request into a generator-ready illustration prompt for Midjourney, DALL-E, Flux, Stable Diffusion, or similar tools. Use when the user needs visual direction, prompt structure, or reference-aware image prompting rather than image generation code.
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 | illustration-prompt |
| description | Turn a vague image request into a generator-ready illustration prompt for Midjourney, DALL-E, Flux, Stable Diffusion, or similar tools. Use when the user needs visual direction, prompt structure, or reference-aware image prompting rather than image generation code. |
Translate vague visual intent into a concrete, reference-aware prompt that a generator can follow without leaning on hand-wavy adjective soup.
/Users/nick/.codex/skills/illustration-prompt/references/reference-analysis.md when you need the reference-reading checklist and /Users/nick/.codex/skills/illustration-prompt/references/prompt-template.md for the 8-block prompt structure.no text in the image unless the user explicitly asks for text.Audit and improve a public GitHub repository's discoverability and any linked canonical site without confusing repository presentation with website SEO. Use for repository SEO, GitHub discoverability, topics/homepage/README optimization, GitHub Pages SEO, AEO, or agent-friendly public documentation.
Create, edit, analyze, or format spreadsheets (`.xlsx`, `.csv`, `.tsv`) with `openpyxl` and `pandas`, especially when formulas, references, and formatting must survive the change. Use when spreadsheet integrity matters more than ad hoc CSV hacking.
Analyze and reshape spreadsheet, CSV, or TSV data with `pandas` and stable output files. Use when the job is aggregation, filtering, joins, metrics, or tabular transformations rather than workbook-style editing.
Create or edit `.xlsx` workbooks while preserving formulas, references, formatting, and layout. Use when workbook integrity matters more than raw data analysis.
Produce a fast plain-text transcript with the bundled CLI using the light transcription model. Use when speaker diarization is not needed.
Produce a speaker-aware transcript with diarization and optional known-speaker hints using the bundled CLI. Use when the user explicitly needs speaker labels.