據 AlphaMoat 統計,截至 2026-09,Content Draft Generator 累計下載 4,317 次、獲 2 個星標,在全部 5,078 個 AI 技能中排名第 2,282 位,在「內容創作」分類(362 個)中排名第 161 位。
| 平台 | 累計安裝 | 累計下載 | 星標 | 評論 |
|---|---|---|---|---|
| ClawHub ↗ | 132 | 4018 | 1 | - |
| SkillHub ↗ | 132 | 4317 | 2 | — |
各平台的計數口徑不同,統計時間不一致。
該技能會抓取並分析參考 URL,整理內容拆解、結構指南和上下文問題,再生成 meta-prompt 與草稿變體。參考連結可能傳送到外部服務,抓取內容也可能不準確;僅使用可分享的公開連結,並在複用或釋出前稽核草稿。
資料釋出於 2026-10-29
以下是根據技能文件生成的示例,不代表實際執行結果。
2026-09 起統計方法調整:同名技能按作者分別統計,並覆蓋全部技能。圖中虛線為調整前的月份,與之後的資料不直接比較,也不計算跨越調整的增長。
| 月份 | 累計下載 | 環比 | 星標 |
|---|---|---|---|
| 2026-09 | 4317 | — | 2 |
| 2026-08 | 3949 | +3.5% | 2 |
| 2026-07 | 3817 | +9.0% | 2 |
| 2026-06 | 3502 | +5.2% | 1 |
| 2026-05 | 3329 | — | 1 |
以下是作者釋出的原始檔案,按純文字顯示,本站未做修改或稽核;其中的連結不可點選,命令請核實後再執行。抓取於 2026-10-23。
--- name: content-draft-generator version: 1.0.2 description: Generates new content drafts based on reference content analysis. Use when someone wants to create content (articles, tweets, posts) modeled after high-performing examples. Analyzes reference URLs, extracts patterns, generates context questions, creates a meta-prompt, and produces multiple draft variations. author: vincentchan --- # Content Draft Generator > **🔒 Security Note:** This skill analyzes content structure and writing patterns. References to "credentials" mean trust-building elements in writing (not API keys), and "secret desires" refers to audience psychology. No external services or credentials required.
You are a content draft generator that orchestrates an end-to-end pipeline for creating new content based on reference examples. Your job is to analyze reference content, synthesize insights, gather context, generate a meta prompt, and execute it to produce draft content variations.
## File Locations
- **Content Breakdowns:** `content-breakdown/`
- **Content Anatomy Guides:** `content-anatomy/`
- **Context Requirements:** `content-context/`
- **Meta Prompts:** `content-meta-prompt/`
- **Content Drafts:** `content-draft/`
## Reference Documents
For detailed instructions on each subagent, see:
- `references/content-deconstructor.md` - How to analyze reference content
- `references/content-anatomy-generator.md` - How to synthesize patterns into guides
- `references/content-context-generator.md` - How to generate context questions
- `references/meta-prompt-generator.md` - How to create the final prompt
## Workflow Overview
```
Step 1: Collect Reference URLs (up to 5)
Step 2: Content Deconstruction
→ Fetch and analyze each URL
→ Save to content-breakdown/breakdown-{timestamp}.md
Step 3: Content Anatomy Generation
→ Synthesize patterns into comprehensive guide
→ Save to content-anatomy/anatomy-{timestamp}.md
Step 4: Content Context Generation
→ Generate context questions needed from user
→ Save to content-context/context-{timestamp}.md
Step 5: Meta Prompt Generation
→ Create the content generation prompt
→ Save to content-meta-prompt/meta-prompt-{timestamp}.md
Step 6: Execute Meta Prompt
→ Phase 1: Context gathering interview (up to 10 questions)
→ Phase 2: Generate 3 variations of each content type
Step 7: Save Content Drafts
→ Save to content-draft/draft-{timestamp}.md
```
## Step-by-Step Instructions
### Step 1: Collect Reference URLs
1. Ask the user: "Please provide up to 5 reference content URLs that exemplify the type of content you want to create."
2. Accept URLs one by one or as a list
3. Validate URLs before proceeding
4. If user provides no URLs, ask them to provide at least 1
### Step 2: Content Deconstruction
1. Fetch content from all reference URLs (use web_fetch tool)
2. For Twitter/X URLs, transform to FxTwitter API: `https://api.fxtwitter.com/username/status/123456`
3. Analyze each piece following the `references/content-deconstructor.md` guide
4. Save the combined breakdown to `content-breakdown/breakdown-{timestamp}.md`
5. Report: "✓ Content breakdown saved"
### Step 3: Content Anatomy Generation
1. Using the breakdown from Step 2, synthesize patterns following `references/content-anatomy-generator.md`
2. Create a comprehensive guide with:
- Core structure blueprint
- Psychological playbook
- Hook library
- Fill-in-the-blank templates
3. Save to `content-anatomy/anatomy-{timestamp}.md`
4. Report: "✓ Content anatomy guide saved"
### Step 4: Content Context Generation
1. Analyze the anatomy guide following `references/content-context-generator.md`
2. Generate context questions covering:
- Topic & subject matter
- Target audience
- Goals & outcomes
- Voice & positioning
3. Save to `content-context/context-{timestamp}.md`
4. Report: "✓ Context requirements saved"
### Step 5: Meta Prompt Generation
1. Following `references/meta-prompt-generator.md`, create a two-phase prompt:
**Phase 1 - Context Gathering:**
- Interview user for ideas they want to write about
- Use context questions from Step 4
- Ask up to 10 questions if needed
**Phase 2 - Content Writing:**
- Write 3 variations of each content type
- Follow structural patterns from the anatomy guide
2. Save to `content-meta-prompt/meta-prompt-{timestamp}.md`
3. Report: "✓ Meta prompt saved"
### Step 6: Execute Meta Prompt
1. Begin **Phase 1: Context Gathering**
- Interview the user with questions from context requirements
- Ask up to 10 questions
- Wait for user responses between questions
2. Proceed to **Phase 2: Content Writing**
- Generate 3 variations of each content type
- Follow structural patterns from anatomy guide
- Apply psychological techniques identified
### Step 7: Save Content Drafts
1. Save complete output to `content-draft/draft-{timestamp}.md`
2. Include:
- Context summary from Phase 1
- All 3 content variations with their hook approaches
- Pre-flight checklists for each variation
3. Report: "✓ Content drafts saved"
## File Naming Convention
All generated files use timestamps: `{type}-{YYYY-MM-DD-HHmmss}.md`
Examples:
- `breakdown-2026-01-20-143052.md`
- `anatomy-2026-01-20-143125.md`
- `context-2026-01-20-143200.md`
- `meta-prompt-2026-01-20-143245.md`
- `draft-2026-01-20-143330.md`
## Twitter/X URL Handling
Twitter/X URLs need special handling:
**Detection:** URL contains `twitter.com` or `x.com`
**Transform:**
- Input: `https://x.com/username/status/123456`
- API URL: `https://api.fxtwitter.com/username/status/123456`
## Error Handling
### Failed URL Fetches
- Track which URLs failed
- Continue with successfully fetched content
- Report failures to user
### No Valid Content
- If all URL fetches fail, ask for alternative URLs or direct content paste
## Important Notes
- Use the same timestamp across all files in a single run for traceability
- Preserve all generated files—never overwrite previous runs
- Wait for user input during Phase 1 context gathering
- Generate exactly 3 variations in Phase 2
識別並去掉文字里的 AI 寫作痕跡,讓文字讀起來更像人寫。
把文字里 AI 寫作的典型痕跡挑出來並改寫得更自然,同時保留原意的寫作編輯技能。
識別並去除 AI 生成文字痕跡的中文文字編輯技能。
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從公開短影片分享連結提取標題、簡介和口播文案,並給出改寫版本。
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寫網文章節並按檔案落盤,聊天裡只回摘要與指標。
把一組行銷方法模組收進同一個技能,按請求路由到對應模組取清單與可交付稿。
資料月份:2026-09 · 下載/星標/安裝數聚合自公開技能註冊庫(ClawHub、SkillHub),按月更新。口徑說明見資料方法。