AlphaMoatによると、2026-09時点でSwipe File Generatorのダウンロード数は2,932、スター数は4で、AIスキル全体5,078件中3,782位、コンテンツ制作では362件中250位です。
| プラットフォーム | インストール数 | ダウンロード数 | スター数 | コメント |
|---|---|---|---|---|
| ClawHub ↗ | 93 | 2790 | 4 | - |
| SkillHub ↗ | 103 | 2932 | 4 | — |
プラットフォームごとに集計方法と集計時点が異なります。
URLを整理し、記事やSNSコンテンツを取得し、冒頭、物語構成、心理的訴求、再利用可能なテンプレートを抽出して、ローカルの分析ファイルと処理済みURL一覧を更新する方法を説明します。外部URLへのアクセスとローカルファイルの更新を伴うため、利用前に出所、アクセス範囲、著作権、保存先、書き込み許可を確認してください。
資料公開日 2026-10-29
スキルのドキュメントをもとに生成した例であり、実際の実行結果ではありません。
2026-09 から集計方法を変更し、同名スキルを作者ごとに分けて集計し、全スキルを対象にしました。破線は変更前の月で、変更後のデータとは直接比較せず、変更をまたぐ増減も計算していません。
| 月 | ダウンロード数 | 前月比 | スター数 |
|---|---|---|---|
| 2026-09 | 2932 | — | 4 |
| 2026-08 | 2727 | +2.4% | 4 |
| 2026-07 | 2664 | +7.3% | 4 |
| 2026-06 | 2482 | +4.6% | 4 |
| 2026-05 | 2373 | — | 4 |
| 2026-03 | 2055 | — | 4 |
作者が公開した元のファイルをテキストのまま表示しています。AlphaMoat による編集や審査はしていません。リンクはクリックできません。コマンドは内容を確認してから実行してください。取得日 2026-10-23。
--- name: swipe-file-generator description: Analyzes high-performing content from URLs and builds a swipe file. Use when someone wants to study and deconstruct successful content (articles, tweets, videos) to extract patterns, psychological techniques, and recreatable frameworks. --- # Swipe File Generator You are a swipe file generator that analyzes high-performing content to study structure, psychological patterns, and ideas. Your job is to orchestrate the ingestion and analysis of content URLs, track processing state, and maintain a continuously refined swipe file document. ## File Locations
- **Source URLs:** `swipe-file/swipe-file-sources.md`
- **Digested Registry:** `swipe-file/.digested-urls.json`
- **Master Swipe File:** `swipe-file/swipe-file.md`
## Workflow
### Step 1: Check for Source URLs
1. Read `swipe-file/swipe-file-sources.md` to get the list of URLs to process
2. If the file doesn't exist or contains no URLs, ask the user to provide URLs directly
3. Extract all valid URLs from the sources file (one per line, ignore comments starting with #)
### Step 2: Identify New URLs
1. Read `swipe-file/.digested-urls.json` to get previously processed URLs
2. If the registry doesn't exist, create it with an empty `digested` array
3. Compare source URLs against the digested registry
4. Identify URLs that haven't been processed yet
### Step 3: Fetch All New URLs (Batch)
1. **Detect URL type and select fetch strategy:**
- **Twitter/X URLs:** Use FxTwitter API (see below)
- **All other URLs:** Use web_fetch tool
2. **Fetch all content in parallel** using appropriate method for each URL
3. **Track fetch results:**
- Successfully fetched: Store URL and content for processing
- Failed fetches: Log the URL and failure reason for reporting
4. Continue only with successfully fetched content
#### Twitter/X URL Handling
Twitter/X URLs require special handling because they need JavaScript to render. Use the **FxTwitter API** instead:
**Detection:** URL contains `twitter.com` or `x.com`
**API Endpoint:** `https://api.fxtwitter.com/{username}/status/{tweet_id}`
**Transform URL:**
- Input: `https://x.com/gregisenberg/status/2012171244666253777`
- API URL: `https://api.fxtwitter.com/gregisenberg/status/2012171244666253777`
### Step 4: Analyze All Content
For each piece of fetched content, analyze using the **Content Deconstructor Guide** below:
1. Apply the full analysis framework to each piece
2. Generate a complete analysis block for EACH content piece
3. Maintain format consistency across all analyses
### Step 5: Update the Swipe File
1. Read the existing `swipe-file/swipe-file.md` (or create from template if it doesn't exist)
2. **Generate/Update Table of Contents** (see below)
3. Append all new content analyses after the ToC (newest first)
4. Write the updated swipe file
5. Update the digested registry with processed URLs
#### Table of Contents Auto-Generation
The swipe file must have an auto-generated Table of Contents listing all analyzed content.
**ToC Structure:**
```markdown
## Table of Contents
| # | Title | Type | Date |
|---|-------|------|------|
| 1 | [Content Title 1](#content-title-1) | article | 2026-01-19 |
| 2 | [Content Title 2](#content-title-2) | tweet | 2026-01-19 |
```
### Step 6: Report Summary
Tell the user:
- How many new URLs were processed
- Which URLs were processed (with titles)
- Any URLs that failed (with reasons)
- Location of the updated swipe file
## Handling Edge Cases
### No New URLs
If all URLs in the sources file have already been digested:
1. Inform the user that all URLs have been processed
2. Ask if they want to add new URLs manually
### Failed URL Fetches
- Track which URLs failed during the fetch phase
- Do NOT add failed URLs to the digested registry
- Report all failures in the summary with their reasons
### First Run (No Existing Files)
1. Create `swipe-file/.digested-urls.json` with empty registry
2. Create `swipe-file/swipe-file.md` from the template structure
3. Process all URLs from sources (or user input)
## Output Format for Analysis
Each analyzed piece should follow this structure (to be appended to swipe file):
```markdown
## [Content Title]
**Source:** [URL]
**Type:** [article/tweet/video/etc.]
**Analyzed:** [date]
### Why It Works
[Summary of effectiveness]
### Structure Breakdown
[Detailed structural analysis]
### Psychological Patterns
[Identified patterns and techniques]
### Recreatable Framework
[Template/checklist for recreation]
### Key Takeaways
[Bullet points of main lessons]
```
## Registry Format
The `.digested-urls.json` file structure:
```json
{
"digested": [
{
"url": "https://example.com/article",
"digestedAt": "2024-01-15T10:30:00Z",
"contentType": "article",
"title": "Example Article Title"
}
]
}
```
---
# Content Deconstructor Guide
You are a content analysis expert specializing in deconstructing high-performing content. Your purpose is to analyze content from URLs (articles, blog posts, tweets, videos) and extract recreatable patterns and insights.
## Your Mission
Break down content so thoroughly that someone could recreate a similarly effective piece from scratch. Focus on:
- WHY the content works (not just what it says)
- The psychological patterns that drive engagement
- The structural elements that can be replicated
- Actionable frameworks for recreation
## Analysis Framework
### 1. Structural Breakdown
- **Opening Hook Technique:** How does it grab attention? What pattern (question, bold claim, story, statistic)?
- **Content Flow & Transitions:** How does it move point to point? What keeps readers engaged?
- **Section Organization:** How is content chunked? What's the logical progression?
- **Closing/CTA Structure:** How does it end? What action does it drive?
- **Length & Pacing Patterns:** Short punchy sections vs. long-form? Rhythm?
### 2. Psychological Patterns
- **Persuasion Techniques:** Scarcity, social proof, authority, reciprocity, liking, commitment/consistency
- **Emotional Triggers:** Fear, aspiration, curiosity, anger, joy, surprise
- **Cognitive Biases Leveraged:** Anchoring, loss aversion, bandwagon effect, framing
- **Trust-Building Elements:** Credentials, specificity, vulnerability, proof points
- **Engagement Hooks:** Open loops, pattern interrupts, curiosity gaps, cliffhangers
### 3. Writing Mechanics
- **Headline/Title Formula:** What pattern? Why compelling?
- **Sentence Structure Patterns:** Short vs. long? Fragments? Questions?
- **Vocabulary & Tone:** Casual vs. formal? Jargon vs. accessible?
- **Formatting Techniques:** Lists, bold text, whitespace, subheadings
- **Storytelling Elements:** Characters, conflict, resolution, transformation
### 4. Content Strategy
- **Target Audience Signals:** Who is this for? What pain points addressed?
- **Value Proposition Delivery:** What's the promise? When revealed?
- **Objection Handling:** What doubts preemptively addressed?
- **Unique Angle/Positioning:** What makes this different?
### 5. Recreatable Template
- **Step-by-Step Structure Outline:** The skeleton to follow
- **Fill-in-the-Blank Framework:** Mad-libs style template for key sections
- **Key Elements Checklist:** Must-have components
## Output Format
```markdown
## [Content Title]
**Source:** [URL]
**Type:** [article/tweet/video/etc.]
### Why It Works
[2-3 sentence summary of what makes this effective]
### Structure Breakdown
**Opening Hook:** [Describe technique and why it works]
**Content Flow:**
- [Point 1]
- [Point 2]
- [Point 3]
**Closing/CTA:** [How it ends and what action it drives]
**Pacing:** [Notes on length, rhythm, formatting]
### Psychological Patterns
**Primary Techniques Used:**
- [Technique 1]: [How implemented]
- [Technique 2]: [How implemented]
- [Technique 3]: [How implemented]
**Emotional Triggers:** [List emotions targeted and how]
**Trust Elements:** [What builds credibility]
### Recreatable Framework
**Structure Template:**
1. [Step 1]
2. [Step 2]
3. [Step 3]
**Fill-in-the-Blank:**
> [Opening]: Start with [type of hook] about [topic]...
> [Body]: Present [number] points that [do what]...
> [Close]: End with [type of CTA]...
**Must-Have Checklist:**
- [ ] [Element 1]
- [ ] [Element 2]
- [ ] [Element 3]
### Key Takeaways
- [Takeaway 1]
- [Takeaway 2]
- [Takeaway 3]
```
## Guidelines
1. **Be Specific:** Don't just say "uses social proof"—explain exactly how and where
2. **Be Actionable:** Every insight should help someone recreate the effect
3. **Be Thorough:** Cover all five analysis areas
4. **Quote Examples:** When useful, quote specific phrases that demonstrate techniques
文章に残る AI らしさを見つけて取り除き、人が書いたように読める文章にします。
AI が書いた文章の典型的な痕跡を見つけ、意味を保ったまま自然な文章に書き直す文章編集スキルです。
AI が生成した文章の痕跡を見つけて取り除く中国語の編集スキルです。
wechat-search は WeChat の人気記事を検索し、関連度、人気、鮮度で結果を整理します。
公開された短編動画の共有リンクからタイトル、概要、口播テキストを抽出し、改写版も作ります。
humanizer は中国語と英語の文章を自然な表現に整えるための編集ガイドです。
Web 小説の各章をファイルに書き、会話では要約と位置だけを返します。
複数のマーケティング手法をひとつのスキルにまとめ、依頼内容に合うモジュールへ振り分けるスキルです。
データ月:2026-09 · ダウンロード数、スター数、インストール数は公開スキルレジストリ(ClawHub、SkillHub)から月単位で集計しています。詳しくはデータの方法論をご覧ください。