[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6N5EBGQGT2PTGS9JCD9F128":3,"skill-same:01M6N5EBGQGT2PTGS9JCD9F128":102},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":22,"publishedAt":43,"linkOnly":44,"markdownContent":45,"markdownBody":46,"frontMatter":47,"refName":48,"commitSha":49,"skillDirectory":50,"forksCount":51,"repositoryHtmlUrl":52,"repositoryHomepageUrl":6,"ownerHtmlUrl":53,"ownerBlogUrl":6,"ownerDescription":54,"updatedAt":55,"install":56,"resources":69},"01M6N5EBGQGT2PTGS9JCD9F128","review-summarizer",null,"Scrape, analyze, and summarize product reviews from multiple platforms (Amazon, Google, Yelp, TripAdvisor). Extract key insights, sentiment analysis, pros\u002Fcons, and recommendations. Use when researching products for arbitrage, creating affiliate content, or making purchasing decisions.","Kernel8901\u002Fai-agent-skills-classification","Kernel8901","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F72647553?v=4","01M6KV388J2H5PNC8D8P940BJD","01M6KV38CS5B3AY63RKZB410YM",7,"MIT","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42],"review","review summarizer","summarizer","for","google","affiliate","reviews","amazon","insights","recommendations","arbitrage","use","decisions","analyze","scrape","analysis","sentiment","content","product","sentiment analysis","2026-11-10 10:12:30",false,"---\nname: review-summarizer\ndescription: Scrape, analyze, and summarize product reviews from multiple platforms (Amazon, Google, Yelp, TripAdvisor). Extract key insights, sentiment analysis, pros\u002Fcons, and recommendations. Use when researching products for arbitrage, creating affiliate content, or making purchasing decisions.\n---\n\n# Review Summarizer\n\n## Overview\n\nAutomatically scrape and analyze product reviews from multiple platforms to extract actionable insights. Generate comprehensive summaries with sentiment analysis, pros\u002Fcons identification, and data-driven recommendations.\n\n## Core Capabilities\n\n### 1. Multi-Platform Review Scraping\n\n**Supported Platforms:**\n- Amazon (product reviews)\n- Google (Google Maps, Google Shopping)\n- Yelp (business and product reviews)\n- TripAdvisor (hotels, restaurants, attractions)\n- Custom platforms (via URL pattern matching)\n\n**Scrape Options:**\n- All reviews or specific time ranges\n- Verified purchases only\n- Filter by rating (1-5 stars)\n- Include images and media\n- Max review count limits\n\n### 2. Sentiment Analysis\n\n**Analyzes:**\n- Overall sentiment score (-1.0 to +1.0)\n- Sentiment distribution (positive\u002Fneutral\u002Fnegative)\n- Key sentiment drivers (what causes positive\u002Fnegative reviews)\n- Trend analysis (sentiment over time)\n- Aspect-based sentiment (battery life, quality, shipping, etc.)\n\n### 3. Insight Extraction\n\n**Automatically identifies:**\n- Top pros mentioned in reviews\n- Common complaints and cons\n- Frequently asked questions\n- Use cases and applications\n- Competitive comparisons mentioned\n- Feature-specific feedback\n\n### 4. Summary Generation\n\n**Output formats:**\n- Executive summary (150-200 words)\n- Detailed breakdown by category\n- Pros\u002Fcons lists with frequency counts\n- Statistical summary (avg rating, review count, etc.)\n- CSV export for analysis\n- Markdown report for documentation\n\n### 5. Recommendation Engine\n\n**Generates recommendations based on:**\n- Overall sentiment score\n- Review quantity and recency\n- Verified purchase ratio\n- Aspect-based ratings\n- Competitive comparison\n\n## Quick Start\n\n### Summarize Amazon Product Reviews\n\n```python\n# Use scripts\u002Fscrape_reviews.py\npython3 scripts\u002Fscrape_reviews.py \\\n  --url \"https:\u002F\u002Famazon.com\u002Fproduct\u002Fdp\u002FB0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 100 \\\n  --output amazon_summary.md\n```\n\n### Compare Reviews Across Platforms\n\n```python\n# Use scripts\u002Fcompare_reviews.py\npython3 scripts\u002Fcompare_reviews.py \\\n  --product \"Sony WH-1000XM5\" \\\n  --platforms amazon,google,yelp \\\n  --output comparison_report.md\n```\n\n### Generate Quick Summary\n\n```python\n# Use scripts\u002Fquick_summary.py\npython3 scripts\u002Fquick_summary.py \\\n  --url \"https:\u002F\u002Famazon.com\u002Fproduct\u002Fdp\u002FB0XXXXX\" \\\n  --brief \\\n  --output summary.txt\n```\n\n## Scripts\n\n### `scrape_reviews.py`\nScrape and analyze reviews from a single URL.\n\n**Parameters:**\n- `--url`: Product or business review URL (required)\n- `--platform`: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)\n- `--max-reviews`: Maximum reviews to fetch (default: 100)\n- `--verified-only`: Filter to verified purchases only\n- `--min-rating`: Minimum rating to include (1-5)\n- `--time-range`: Time filter (7d, 30d, 90d, all) (default: all)\n- `--output`: Output file (default: summary.md)\n- `--format`: Output format (markdown, json, csv)\n\n**Example:**\n```bash\npython3 scripts\u002Fscrape_reviews.py \\\n  --url \"https:\u002F\u002Famazon.com\u002Fdp\u002FB0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 200 \\\n  --verified-only \\\n  --format markdown \\\n  --output product_summary.md\n```\n\n### `compare_reviews.py`\nCompare reviews for a product across multiple platforms.\n\n**Parameters:**\n- `--product`: Product name or keyword (required)\n- `--platforms`: Comma-separated platforms (default: all)\n- `--max-reviews`: Max reviews per platform (default: 50)\n- `--output`: Output file\n- `--format`: Output format (markdown, json)\n\n**Example:**\n```bash\npython3 scripts\u002Fcompare_reviews.py \\\n  --product \"AirPods Pro 2\" \\\n  --platforms amazon,google,yelp \\\n  --max-reviews 75 \\\n  --output comparison.md\n```\n\n### `sentiment_analysis.py`\nAnalyze sentiment of review text.\n\n**Parameters:**\n- `--input`: Input file or text (required)\n- `--type`: Input type (file, text, url)\n- `--aspects`: Analyze specific aspects (comma-separated)\n- `--output`: Output file\n\n**Example:**\n```bash\npython3 scripts\u002Fsentiment_analysis.py \\\n  --input reviews.txt \\\n  --type file \\\n  --aspects battery,sound,quality \\\n  --output sentiment_report.md\n```\n\n### `quick_summary.py`\nGenerate a brief executive summary.\n\n**Parameters:**\n- `--url`: Review URL (required)\n- `--brief`: Brief summary only (no detailed breakdown)\n- `--words`: Summary word count (default: 150)\n- `--output`: Output file\n\n**Example:**\n```bash\npython3 scripts\u002Fquick_summary.py \\\n  --url \"https:\u002F\u002Fyelp.com\u002Fbiz\u002Fexample-business\" \\\n  --brief \\\n  --words 100 \\\n  --output summary.txt\n```\n\n### `export_data.py`\nExport review data for further analysis.\n\n**Parameters:**\n- `--input`: Summary file or JSON data (required)\n- `--format`: Export format (csv, json, excel)\n- `--output`: Output file\n\n**Example:**\n```bash\npython3 scripts\u002Fexport_data.py \\\n  --input product_summary.json \\\n  --format csv \\\n  --output reviews_data.csv\n```\n\n## Output Format\n\n### Markdown Summary Structure\n\n```markdown\n# Product Review Summary: [Product Name]\n\n## Overview\n- **Platform:** Amazon\n- **Reviews Analyzed:** 247\n- **Average Rating:** 4.3\u002F5.0\n- **Overall Sentiment:** +0.72 (Positive)\n\n## Key Insights\n\n### Top Pros\n1. Excellent sound quality (89 reviews)\n2. Great battery life (76 reviews)\n3. Comfortable fit (65 reviews)\n\n### Top Cons\n1. Expensive (34 reviews)\n2. Connection issues (22 reviews)\n3. Limited color options (18 reviews)\n\n## Sentiment Analysis\n- **Positive:** 78% (193 reviews)\n- **Neutral:** 15% (37 reviews)\n- **Negative:** 7% (17 reviews)\n\n## Recommendation\n✅ **Recommended** - Strong positive sentiment with high customer satisfaction.\n```\n\n## Best Practices\n\n### For Arbitrage Research\n1. **Compare across platforms** - Check Amazon vs eBay seller ratings\n2. **Look for red flags** - High return rates, quality complaints\n3. **Check authenticity** - Verified purchases only\n4. **Analyze trends** - Recent review sentiment vs older reviews\n\n### For Affiliate Content\n1. **Extract real quotes** - Use actual customer feedback\n2. **Identify use cases** - How people use the product\n3. **Find pain points** - Problems the product solves\n4. **Build credibility** - Use data from many reviews\n\n### For Purchasing Decisions\n1. **Check recent reviews** - Last 30-90 days\n2. **Look at 1-star reviews** - Understand worst-case scenarios\n3. **Consider your needs** - Match features to your use case\n4. **Compare alternatives** - Use compare_reviews.py\n\n## Integration Opportunities\n\n### With Price Tracker\nUse review summaries to validate arbitrage opportunities:\n```bash\n# 1. Find arbitrage opportunity\nprice-tracker\u002Fscripts\u002Fcompare_prices.py --keyword \"Sony WH-1000XM5\"\n\n# 2. Validate with reviews\nreview-summarizer\u002Fscripts\u002Fscrape_reviews.py --url [amazon_url]\nreview-summarizer\u002Fscripts\u002Fscrape_reviews.py --url [ebay_url]\n\n# 3. Make informed decision\n```\n\n### With Content Recycler\nGenerate content from review insights:\n```bash\n# 1. Summarize reviews\nreview-summarizer\u002Fscripts\u002Fscrape_reviews.py --url [amazon_url]\n\n# 2. Use insights in article\nseo-article-gen --keyword \"[product name] review\" --use-insights review_summary.json\n\n# 3. Recycle across platforms\ncontent-recycler\u002Fscripts\u002Frecycle_content.py --input article.md\n```\n\n## Automation\n\n### Weekly Review Monitoring\n\n```bash\n# Monitor competitor products\n0 9 * * 1 \u002Fpath\u002Fto\u002Freview-summarizer\u002Fscripts\u002Fcompare_reviews.py \\\n  --product \"competitor-product\" \\\n  --platforms amazon,google \\\n  --output \u002Fpath\u002Fto\u002Fcompetitor_analysis.md\n```\n\n### Alert on Negative Trends\n\n```bash\n# Check for sentiment drops below threshold\nif [ $(grep -o \"Sentiment: -\" summary.md | wc -l) -gt 0 ]; then\n  echo \"Negative sentiment alert\" | mail -s \"Review Alert\" user@example.com\nfi\n```\n\n## Data Privacy & Ethics\n\n- Only scrape publicly available reviews\n- Respect robots.txt and rate limits\n- Don't store PII (personal information)\n- Aggregate data, don't expose individual reviewers\n- Follow platform terms of service\n\n## Limitations\n\n- Rate limiting on some platforms\n- Cannot access verified purchase status on all platforms\n- Fake reviews may skew analysis\n- Language support varies by platform\n- Some platforms block scraping\n\n---\n\n**Make data-driven decisions. Automate research. Scale intelligence.**\n","# Review Summarizer\n\n## Overview\n\nAutomatically scrape and analyze product reviews from multiple platforms to extract actionable insights. Generate comprehensive summaries with sentiment analysis, pros\u002Fcons identification, and data-driven recommendations.\n\n## Core Capabilities\n\n### 1. Multi-Platform Review Scraping\n\n**Supported Platforms:**\n- Amazon (product reviews)\n- Google (Google Maps, Google Shopping)\n- Yelp (business and product reviews)\n- TripAdvisor (hotels, restaurants, attractions)\n- Custom platforms (via URL pattern matching)\n\n**Scrape Options:**\n- All reviews or specific time ranges\n- Verified purchases only\n- Filter by rating (1-5 stars)\n- Include images and media\n- Max review count limits\n\n### 2. Sentiment Analysis\n\n**Analyzes:**\n- Overall sentiment score (-1.0 to +1.0)\n- Sentiment distribution (positive\u002Fneutral\u002Fnegative)\n- Key sentiment drivers (what causes positive\u002Fnegative reviews)\n- Trend analysis (sentiment over time)\n- Aspect-based sentiment (battery life, quality, shipping, etc.)\n\n### 3. Insight Extraction\n\n**Automatically identifies:**\n- Top pros mentioned in reviews\n- Common complaints and cons\n- Frequently asked questions\n- Use cases and applications\n- Competitive comparisons mentioned\n- Feature-specific feedback\n\n### 4. Summary Generation\n\n**Output formats:**\n- Executive summary (150-200 words)\n- Detailed breakdown by category\n- Pros\u002Fcons lists with frequency counts\n- Statistical summary (avg rating, review count, etc.)\n- CSV export for analysis\n- Markdown report for documentation\n\n### 5. Recommendation Engine\n\n**Generates recommendations based on:**\n- Overall sentiment score\n- Review quantity and recency\n- Verified purchase ratio\n- Aspect-based ratings\n- Competitive comparison\n\n## Quick Start\n\n### Summarize Amazon Product Reviews\n\n```python\n# Use scripts\u002Fscrape_reviews.py\npython3 scripts\u002Fscrape_reviews.py \\\n  --url \"https:\u002F\u002Famazon.com\u002Fproduct\u002Fdp\u002FB0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 100 \\\n  --output amazon_summary.md\n```\n\n### Compare Reviews Across Platforms\n\n```python\n# Use scripts\u002Fcompare_reviews.py\npython3 scripts\u002Fcompare_reviews.py \\\n  --product \"Sony WH-1000XM5\" \\\n  --platforms amazon,google,yelp \\\n  --output comparison_report.md\n```\n\n### Generate Quick Summary\n\n```python\n# Use scripts\u002Fquick_summary.py\npython3 scripts\u002Fquick_summary.py \\\n  --url \"https:\u002F\u002Famazon.com\u002Fproduct\u002Fdp\u002FB0XXXXX\" \\\n  --brief \\\n  --output summary.txt\n```\n\n## Scripts\n\n### `scrape_reviews.py`\nScrape and analyze reviews from a single URL.\n\n**Parameters:**\n- `--url`: Product or business review URL (required)\n- `--platform`: Platform (amazon, google, yelp, tripadvisor) (auto-detected if omitted)\n- `--max-reviews`: Maximum reviews to fetch (default: 100)\n- `--verified-only`: Filter to verified purchases only\n- `--min-rating`: Minimum rating to include (1-5)\n- `--time-range`: Time filter (7d, 30d, 90d, all) (default: all)\n- `--output`: Output file (default: summary.md)\n- `--format`: Output format (markdown, json, csv)\n\n**Example:**\n```bash\npython3 scripts\u002Fscrape_reviews.py \\\n  --url \"https:\u002F\u002Famazon.com\u002Fdp\u002FB0XXXXX\" \\\n  --platform amazon \\\n  --max-reviews 200 \\\n  --verified-only \\\n  --format markdown \\\n  --output product_summary.md\n```\n\n### `compare_reviews.py`\nCompare reviews for a product across multiple platforms.\n\n**Parameters:**\n- `--product`: Product name or keyword (required)\n- `--platforms`: Comma-separated platforms (default: all)\n- `--max-reviews`: Max reviews per platform (default: 50)\n- `--output`: Output file\n- `--format`: Output format (markdown, json)\n\n**Example:**\n```bash\npython3 scripts\u002Fcompare_reviews.py \\\n  --product \"AirPods Pro 2\" \\\n  --platforms amazon,google,yelp \\\n  --max-reviews 75 \\\n  --output comparison.md\n```\n\n### `sentiment_analysis.py`\nAnalyze sentiment of review text.\n\n**Parameters:**\n- `--input`: Input file or text (required)\n- `--type`: Input type (file, text, url)\n- `--aspects`: Analyze specific aspects (comma-separated)\n- `--output`: Output file\n\n**Example:**\n```bash\npython3 scripts\u002Fsentiment_analysis.py \\\n  --input reviews.txt \\\n  --type file \\\n  --aspects battery,sound,quality \\\n  --output sentiment_report.md\n```\n\n### `quick_summary.py`\nGenerate a brief executive summary.\n\n**Parameters:**\n- `--url`: Review URL (required)\n- `--brief`: Brief summary only (no detailed breakdown)\n- `--words`: Summary word count (default: 150)\n- `--output`: Output file\n\n**Example:**\n```bash\npython3 scripts\u002Fquick_summary.py \\\n  --url \"https:\u002F\u002Fyelp.com\u002Fbiz\u002Fexample-business\" \\\n  --brief \\\n  --words 100 \\\n  --output summary.txt\n```\n\n### `export_data.py`\nExport review data for further analysis.\n\n**Parameters:**\n- `--input`: Summary file or JSON data (required)\n- `--format`: Export format (csv, json, excel)\n- `--output`: Output file\n\n**Example:**\n```bash\npython3 scripts\u002Fexport_data.py \\\n  --input product_summary.json \\\n  --format csv \\\n  --output reviews_data.csv\n```\n\n## Output Format\n\n### Markdown Summary Structure\n\n```markdown\n# Product Review Summary: [Product Name]\n\n## Overview\n- **Platform:** Amazon\n- **Reviews Analyzed:** 247\n- **Average Rating:** 4.3\u002F5.0\n- **Overall Sentiment:** +0.72 (Positive)\n\n## Key Insights\n\n### Top Pros\n1. Excellent sound quality (89 reviews)\n2. Great battery life (76 reviews)\n3. Comfortable fit (65 reviews)\n\n### Top Cons\n1. Expensive (34 reviews)\n2. Connection issues (22 reviews)\n3. Limited color options (18 reviews)\n\n## Sentiment Analysis\n- **Positive:** 78% (193 reviews)\n- **Neutral:** 15% (37 reviews)\n- **Negative:** 7% (17 reviews)\n\n## Recommendation\n✅ **Recommended** - Strong positive sentiment with high customer satisfaction.\n```\n\n## Best Practices\n\n### For Arbitrage Research\n1. **Compare across platforms** - Check Amazon vs eBay seller ratings\n2. **Look for red flags** - High return rates, quality complaints\n3. **Check authenticity** - Verified purchases only\n4. **Analyze trends** - Recent review sentiment vs older reviews\n\n### For Affiliate Content\n1. **Extract real quotes** - Use actual customer feedback\n2. **Identify use cases** - How people use the product\n3. **Find pain points** - Problems the product solves\n4. **Build credibility** - Use data from many reviews\n\n### For Purchasing Decisions\n1. **Check recent reviews** - Last 30-90 days\n2. **Look at 1-star reviews** - Understand worst-case scenarios\n3. **Consider your needs** - Match features to your use case\n4. **Compare alternatives** - Use compare_reviews.py\n\n## Integration Opportunities\n\n### With Price Tracker\nUse review summaries to validate arbitrage opportunities:\n```bash\n# 1. Find arbitrage opportunity\nprice-tracker\u002Fscripts\u002Fcompare_prices.py --keyword \"Sony WH-1000XM5\"\n\n# 2. Validate with reviews\nreview-summarizer\u002Fscripts\u002Fscrape_reviews.py --url [amazon_url]\nreview-summarizer\u002Fscripts\u002Fscrape_reviews.py --url [ebay_url]\n\n# 3. Make informed decision\n```\n\n### With Content Recycler\nGenerate content from review insights:\n```bash\n# 1. Summarize reviews\nreview-summarizer\u002Fscripts\u002Fscrape_reviews.py --url [amazon_url]\n\n# 2. Use insights in article\nseo-article-gen --keyword \"[product name] review\" --use-insights review_summary.json\n\n# 3. Recycle across platforms\ncontent-recycler\u002Fscripts\u002Frecycle_content.py --input article.md\n```\n\n## Automation\n\n### Weekly Review Monitoring\n\n```bash\n# Monitor competitor products\n0 9 * * 1 \u002Fpath\u002Fto\u002Freview-summarizer\u002Fscripts\u002Fcompare_reviews.py \\\n  --product \"competitor-product\" \\\n  --platforms amazon,google \\\n  --output \u002Fpath\u002Fto\u002Fcompetitor_analysis.md\n```\n\n### Alert on Negative Trends\n\n```bash\n# Check for sentiment drops below threshold\nif [ $(grep -o \"Sentiment: -\" summary.md | wc -l) -gt 0 ]; then\n  echo \"Negative sentiment alert\" | mail -s \"Review Alert\" user@example.com\nfi\n```\n\n## Data Privacy & Ethics\n\n- Only scrape publicly available reviews\n- Respect robots.txt and rate limits\n- Don't store PII (personal information)\n- Aggregate data, don't expose individual reviewers\n- Follow platform terms of service\n\n## Limitations\n\n- Rate limiting on some platforms\n- Cannot access verified purchase status on all platforms\n- Fake reviews may skew analysis\n- Language support varies by platform\n- Some platforms block scraping\n\n---\n\n**Make data-driven decisions. Automate research. Scale intelligence.**\n",{"name":5,"description":7},"main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","内容创意\u002Freview-summarizer",1,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification","https:\u002F\u002Fgithub.com\u002FKernel8901","a little little little dobe in life but science geek","2026-04-05 16:43:56",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill review-summarizer",{"tool":62,"command":63},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill review-summarizer",{"tool":65,"command":66},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill review-summarizer","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002FSKILL.md",true,[70,76,82,86,90,94,98],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"_meta.json","REFERENCE","参考文档",294,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002F_meta.json",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"scripts\u002F__init__.py","SCRIPT","脚本",28,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002Fscripts\u002F__init__.py",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"scripts\u002Fcompare_reviews.py",4960,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002Fscripts\u002Fcompare_reviews.py",{"path":87,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":88,"sourceHtmlUrl":89},"scripts\u002Fexport_data.py",2427,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002Fscripts\u002Fexport_data.py",{"path":91,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":92,"sourceHtmlUrl":93},"scripts\u002Fquick_summary.py",1878,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002Fscripts\u002Fquick_summary.py",{"path":95,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":96,"sourceHtmlUrl":97},"scripts\u002Fscrape_reviews.py",9861,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002Fscripts\u002Fscrape_reviews.py",{"path":99,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":100,"sourceHtmlUrl":101},"scripts\u002Fsentiment_analysis.py",3932,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F内容创意\u002Freview-summarizer\u002Fscripts\u002Fsentiment_analysis.py",[103,126,160,171,181,209],{"skillNo":104,"name":105,"nameZh":6,"description":106,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":107,"categories":108,"keywords":109,"publishedAt":43},"01M6Q5R6WZ06RRB9RQTW54B34B","zyfai","Earn yield on any Ethereum wallet on Base, Arbitrum, and Plasma. Use when a user wants passive DeFi yield on their funds. Deploys a non-custodial subaccount (Safe) linked to their EOA, enables automated yield optimization, and lets them deposit\u002Fwithdraw anytime.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzyfai-sdk\u002FSKILL.md",[],[105,110,111,112,113,114,34,115,116,117,118,119,120,121,122,123,124,125],"safe","ethereum","defi","Optimization","user","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":127,"name":128,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":129,"categories":130,"keywords":142,"publishedAt":43},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[131,134,137],{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":135,"categoryName":136,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":138,"categoryName":139,"taxonomyType":140,"taxonomyTypeDesc":141},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[143,144,145,146,147,148,149,112,150,115,151,152,153,154,155,156,157,158,159],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":161,"name":162,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":168,"publishedAt":43},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[165,166,167],{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":138,"categoryName":139,"taxonomyType":140,"taxonomyTypeDesc":141},[143,169,170,144,145,146,147,148,149,112,150,115,152,153,154,155,156,157,158,159],"api skill","zerion api skill",{"skillNo":172,"name":173,"nameZh":6,"description":6,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":174,"categories":175,"keywords":179,"publishedAt":43},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[176,177,178],{"categoryNo":132,"categoryName":133,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":135,"categoryName":136,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":138,"categoryName":139,"taxonomyType":140,"taxonomyTypeDesc":141},[146,143,180,144,145,147,148,112,150,115,151,152,153,154,155,156,157,158,159],"zerion api mcp",{"skillNo":182,"name":183,"nameZh":6,"description":184,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":185,"categories":186,"keywords":190,"publishedAt":43},"01M6Q5QQYZB7A501BDEQWKGSJT","yahoo-finance-forex","Fetches real-time FOREX news and market data from Yahoo Finance for major currency pairs (EUR\u002FUSD, GBP\u002FUSD, USD\u002FJPY, etc.). Analyzes sentiment and provides trading context.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-forex\u002FSKILL.md",[187],{"categoryNo":188,"categoryName":189,"taxonomyType":140,"taxonomyTypeDesc":141},"01JBSEEDCAT000000000000040","财务",[191,192,193,194,195,196,197,39,198,199,200,201,202,203,204,205,206,207,208,26],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data",{"skillNo":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":213,"categories":214,"keywords":216,"publishedAt":43},"01M6Q5QM6VS84WZVM7BKXXPF32","yahoo-finance","This skill should be used when the user asks to \"get stock prices\", \"check stock quotes\", \"look up earnings\", \"get financial data\", \"find trending stocks\", or needs stock market data from Yahoo Finance.","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fyahoo-finance-cli\u002FSKILL.md",[215],{"categoryNo":188,"categoryName":189,"taxonomyType":140,"taxonomyTypeDesc":141},[191,192,194,217,218,219,220,221,222,201,223,224,225,226,227,228,229,230,159,208],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]