prompt-architect
Transform rough ideas into professional-grade LLM prompts. Analyzes text, images, links, and documents to craft optimized prompts using proven frameworks (CoT, Few-Shot, Persona, etc.). USE WHEN: user wants to improve a prompt, create a prompt from scratch, optimize an existing prompt, convert a vague idea into a structured prompt, analyze why a prompt isn't working, or asks "write me a prompt for...", "improve this prompt", "prompt engineer this". DON'T USE WHEN: user wants to execute the prompt itself (just run it), wants general writing help without prompt context, asks for code/articles/tweets (use appropriate skill instead), or wants to chat about prompt engineering theory without producing a prompt. EDGE CASES: - "Fix this prompt" → this skill (optimization) - "Write me a blog post" → NOT this skill (content creation, not prompt creation) - "Write me a prompt that generates blog posts" → this skill - "Why isn't my prompt working?" → this skill (diagnosis + fix) - "اكتب لي برومبت" → this skill - "حسن هالبرومبت" → this skill - "اكتب لي مقال" → NOT this skill (use katib-al-maqalat) INPUTS: Rough idea, existing prompt, images, links, documents, or any combination. OUTPUTS: Optimized prompt in a code block, ready to copy. SUCCESS: Prompt is clear, structured, uses appropriate framework, and achieves the user's goal.
下記のコマンドをコピーしてターミナル(Mac/Linux)または PowerShell(Windows)に貼り付けてください。 ダウンロード → 解凍 → 配置まで全自動。
mkdir -p ~/.claude/skills && cd ~/.claude/skills && curl -L -o prompt-architect.zip https://jpskill.com/download/8815.zip && unzip -o prompt-architect.zip && rm prompt-architect.zip$d = "$env:USERPROFILE\.claude\skills"; ni -Force -ItemType Directory $d | Out-Null; iwr https://jpskill.com/download/8815.zip -OutFile "$d\prompt-architect.zip"; Expand-Archive "$d\prompt-architect.zip" -DestinationPath $d -Force; ri "$d\prompt-architect.zip"完了後、Claude Code を再起動 → 普通に「動画プロンプト作って」のように話しかけるだけで自動発動します。
💾 手動でダウンロードしたい(コマンドが難しい人向け)
- 1. 下の青いボタンを押して
prompt-architect.zipをダウンロード - 2. ZIPファイルをダブルクリックで解凍 →
prompt-architectフォルダができる - 3. そのフォルダを
C:\Users\あなたの名前\.claude\skills\(Win)または~/.claude/skills/(Mac)へ移動 - 4. Claude Code を再起動
⚠️ ダウンロード・利用は自己責任でお願いします。当サイトは内容・動作・安全性について責任を負いません。
🎯 このSkillでできること
下記の説明文を読むと、このSkillがあなたに何をしてくれるかが分かります。Claudeにこの分野の依頼をすると、自動で発動します。
📦 インストール方法 (3ステップ)
- 1. 上の「ダウンロード」ボタンを押して .skill ファイルを取得
- 2. ファイル名の拡張子を .skill から .zip に変えて展開(macは自動展開可)
- 3. 展開してできたフォルダを、ホームフォルダの
.claude/skills/に置く- · macOS / Linux:
~/.claude/skills/ - · Windows:
%USERPROFILE%\.claude\skills\
- · macOS / Linux:
Claude Code を再起動すれば完了。「このSkillを使って…」と話しかけなくても、関連する依頼で自動的に呼び出されます。
詳しい使い方ガイドを見る →- 最終更新
- 2026-05-18
- 取得日時
- 2026-05-18
- 同梱ファイル
- 1
📖 Claude が読む原文 SKILL.md(中身を展開)
この本文は AI(Claude)が読むための原文(英語または中国語)です。日本語訳は順次追加中。
The Prompt Architect
Transform rough concepts into professional-grade LLM prompts.
Core Workflow
Follow these 4 steps for every interaction. Do not skip steps.
Step 1: Ingest and Analyze
When the user submits input, do NOT generate the final prompt immediately. Perform deep analysis:
- Text: Identify core intent, even if vague
- Images: Extract visual style, subject, mood, composition details
- Links: Browse or infer context to extract key information
- Documents: Review and summarize relevant constraints
Step 2: Clarify (Mandatory)
Ask 5-10 clarifying questions based on analysis. Cover these categories:
| Category | What to Ask |
|---|---|
| Purpose | What specific outcome do you need? |
| Audience | Who consumes this output? |
| Tone & Style | Professional, witty, academic, cinematic? |
| Format | Code block, blog post, JSON, narrative? |
| Context | Background info the model needs? |
| Constraints | What to avoid? Length limits? |
| Examples | Specific styles or references to mimic? |
Adapt question count to complexity: simple requests get 5, complex/multimodal get up to 10-15.
Opening format:
I've analyzed your input. To craft the right prompt, I need a few details:
- [Question]
- [Question] ...
Step 3: Language Selection
After the user answers, ask exactly:
Would you like the final prompt in English or Arabic?
Step 4: Generate the Prompt
Construct the optimized prompt using:
- User's input + media analysis + answers to clarifying questions
- Appropriate framework from
references/frameworks.md - Quality criteria from
references/quality-criteria.md
Output rules:
- Deliver inside a code block for easy copying
- Include a brief note explaining which framework was used and why
- If the prompt is complex, add inline comments
Delivery format:
Here's your optimized prompt:
[Final Polished Prompt]Framework used: [Name] - [One-line reason]
Framework Selection Guide
Choose the right framework based on the task. See references/frameworks.md for full details.
| Task Type | Recommended Framework |
|---|---|
| Reasoning/analysis | Chain-of-Thought (CoT) |
| Creative/open-ended | Persona + constraints |
| Structured data output | JSON schema + few-shot |
| Multi-step workflows | Prompt chaining |
| Classification/decisions | Few-shot with edge cases |
| Complex problem-solving | Tree-of-Thought |
| Task + tool use | ReAct pattern |
Output Templates
See references/templates.md for ready-to-use prompt templates organized by use case:
- System prompt templates
- Analysis prompt templates
- Creative prompt templates
- Code generation templates
- Data extraction templates
Quality Checklist
Before delivering, verify against references/quality-criteria.md:
- Clarity: No ambiguity in instructions
- Structure: Logical flow, clear sections
- Specificity: Concrete examples over vague descriptions
- Constraints: Explicit boundaries (length, format, tone)
- Framework fit: Right technique for the task
- Testability: Can you tell if the output is correct?
Anti-Patterns to Avoid
- Vague role assignments ("Be a helpful assistant")
- Contradictory instructions
- Over-specification that kills creativity
- Missing output format specification
- No examples when few-shot would help
- Ignoring the model's strengths (multimodal, reasoning, etc.)