据 AlphaMoat 统计,截至 2026-09,Swipe File Generator 累计下载 2,932 次、获 4 个星标,在全部 5,078 个 AI 技能中排名第 3,782 位,在「内容创作」分类(362 个)中排名第 250 位。
| 平台 | 累计安装 | 累计下载 | 星标 | 评论 |
|---|---|---|---|---|
| ClawHub ↗ | 93 | 2,790 | 4 | - |
| SkillHub ↗ | 103 | 2,932 | 4 | — |
各平台的计数口径不同,统计时间不一致。
它描述整理待处理链接、获取文章或社交媒体内容、提炼开场、叙事结构、心理诉求和可复用模板,并更新本地分析文件与已处理链接登记表。流程会访问外部 URL 且写入或更新本地文件;使用前应确认链接来源、访问范围、版权边界、文件路径和写入授权。
资料发布于 2026-10-29
以下是根据技能文档生成的示例,不代表实际运行结果。
2026-09 起统计方法调整:同名技能按作者分别统计,并覆盖全部技能。图中虚线为调整前的月份,与之后的数据不直接比较,也不计算跨越调整的增长。
| 月份 | 累计下载 | 环比 | 星标 |
|---|---|---|---|
| 2026-09 | 2,932 | — | 4 |
| 2026-08 | 2,727 | +2.4% | 4 |
| 2026-07 | 2,664 | +7.3% | 4 |
| 2026-06 | 2,482 | +4.6% | 4 |
| 2026-05 | 2,373 | — | 4 |
| 2026-03 | 2,055 | — | 4 |
以下是作者发布的原始文件,按纯文本显示,本站未做修改或审核;其中的链接不可点击,命令请核实后再运行。抓取于 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
识别并去掉文本里的 AI 写作痕迹,让文字读起来更像人写。
把文本里 AI 写作的典型痕迹挑出来并改写得更自然,同时保留原意的写作编辑技能。
识别并去除 AI 生成文本痕迹的中文文字编辑技能。
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从公开短视频分享链接提取标题、简介和口播文案,并给出改写版本。
humanizer 为中英文文本提供更自然的表达和风格编辑指导。
写网文章节并按文件落盘,聊天里只回摘要与指针。
把一组营销方法模块收进同一个技能,按请求路由到对应模块取清单与可交付稿。
数据月份:2026-09 · 下载/星标/安装数聚合自公开技能注册库(ClawHub、SkillHub),按月更新。口径说明见数据方法。