[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6N0CEE9VTZG5NYH09FRG7HT":3,"skill-same:01M6N0CEE9VTZG5NYH09FRG7HT":100},{"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":30,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":54,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":60,"updatedAt":61,"install":62,"resources":75},"01M6N0CEE9VTZG5NYH09FRG7HT","video-ad-deconstructor",null,"Deconstruct video ad creatives into marketing dimensions using Gemini AI. Extracts hooks, social proof, CTAs, target audience, emotional triggers, urgency tactics, and more. Use when analyzing competitor ads, generating creative briefs, or understanding what makes ads effective.","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人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002FSKILL.md",[17,22,25],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000016","图像与多媒体","SCENARIO","使用场景",{"categoryNo":23,"categoryName":24,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000019","营销推广",{"categoryNo":26,"categoryName":27,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000035","市场营销","PROFESSION","适用职业",[31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"video ad deconstructor","Video","ad","creative","more","briefs","proof","triggers","dimensions","social","gemini","hooks","use","ads","marketing","analyzing","gemini ai","2026-11-10 10:12:30",false,"---\nname: video-ad-deconstructor\nversion: 1.0.0\ndescription: Deconstruct video ad creatives into marketing dimensions using Gemini AI. Extracts hooks, social proof, CTAs, target audience, emotional triggers, urgency tactics, and more. Use when analyzing competitor ads, generating creative briefs, or understanding what makes ads effective.\n---\n\n# Video Ad Deconstructor\n\nAI-powered deconstruction of video ad creatives into actionable marketing insights.\n\n## What This Skill Does\n\n- **Generate Summaries**: Product, features, audience, CTA extraction\n- **Deconstruct Marketing Dimensions**: Hooks, social proof, urgency, emotion, etc.\n- **Support Multiple Content Types**: Consumer products and gaming ads\n- **Progress Tracking**: Callback support for long analyses\n- **JSON Output**: Structured data for downstream processing\n\n## Setup\n\n### 1. Environment Variables\n\n```bash\n# Required for Gemini\nGOOGLE_APPLICATION_CREDENTIALS=\u002Fpath\u002Fto\u002Fservice-account.json\n```\n\n### 2. Dependencies\n\n```bash\npip install vertexai\n```\n\n## Usage\n\n### Basic Ad Deconstruction\n\n```python\nfrom scripts.deconstructor import AdDeconstructor\nfrom scripts.models import ExtractedVideoContent\nimport vertexai\nfrom vertexai.generative_models import GenerativeModel\n\n# Initialize Vertex AI\nvertexai.init(project=\"your-project-id\", location=\"us-central1\")\ngemini_model = GenerativeModel(\"gemini-1.5-flash\")\n\n# Create deconstructor\ndeconstructor = AdDeconstructor(gemini_model=gemini_model)\n\n# Create extracted content (from video-ad-analyzer or manually)\ncontent = ExtractedVideoContent(\n    video_path=\"ad.mp4\",\n    duration=30.0,\n    transcript=\"Tired of messy cables? Meet CableFlow...\",\n    text_timeline=[{\"at\": 0.0, \"text\": [\"50% OFF TODAY\"]}],\n    scene_timeline=[{\"timestamp\": 0.0, \"description\": \"Person frustrated with tangled cables\"}]\n)\n\n# Generate summary\nsummary = deconstructor.generate_summary(\n    transcript=content.transcript,\n    scenes=\"0.0s: Person frustrated with tangled cables\",\n    text_overlays=\"50% OFF TODAY\"\n)\nprint(summary)\n```\n\n### Full Deconstruction\n\n```python\n# Deconstruct all marketing dimensions\ndef on_progress(fraction, dimension):\n    print(f\"Progress: {fraction*100:.0f}% - Analyzed {dimension}\")\n\nanalysis = deconstructor.deconstruct(\n    extracted_content=content,\n    summary=summary,\n    is_gaming=False,  # Set True for gaming ads\n    on_progress=on_progress\n)\n\n# Access dimensions\nfor dimension, data in analysis.dimensions.items():\n    print(f\"\\n{dimension}:\")\n    print(data)\n```\n\n## Output Structure\n\n### Summary Output\n\n```\nProduct\u002FApp: CableFlow Cable Organizer\n\nKey Features:\nMagnetic design: Keeps cables organized automatically\nUniversal fit: Works with all cable types\nPremium materials: Durable silicone construction\n\nTarget Audience: Tech users frustrated with cable management\n\nCall to Action: Order now and get 50% off\n```\n\n### Deconstruction Output\n\n```python\n{\n    \"spoken_hooks\": {\n        \"elements\": [\n            {\n                \"hook_text\": \"Tired of messy cables?\",\n                \"timestamp\": \"0:00\",\n                \"hook_type\": \"Problem Question\",\n                \"effectiveness\": \"High - directly addresses pain point\"\n            }\n        ]\n    },\n    \"social_proof\": {\n        \"elements\": [\n            {\n                \"proof_type\": \"User Count\",\n                \"claim\": \"Over 1 million happy customers\",\n                \"credibility_score\": 7\n            }\n        ]\n    },\n    # ... more dimensions\n}\n```\n\n## Marketing Dimensions Deconstructed\n\n| Dimension | What It Extracts |\n|-----------|------------------|\n| `spoken_hooks` | Opening hooks from transcript |\n| `visual_hooks` | Attention-grabbing visuals |\n| `text_hooks` | On-screen text hooks |\n| `social_proof` | Testimonials, user counts, reviews |\n| `urgency_scarcity` | Limited time offers, stock warnings |\n| `emotional_triggers` | Fear, desire, belonging, etc. |\n| `problem_solution` | Pain points and solutions |\n| `cta_analysis` | Call-to-action effectiveness |\n| `target_audience` | Who the ad targets |\n| `unique_mechanism` | What makes product special |\n\n## Customizing Prompts\n\nEdit prompts in `prompts\u002Fmarketing_analysis.md` to customize:\n\n- What dimensions to analyze\n- Output format\n- Scoring criteria\n- Gaming vs consumer product focus\n\n## Common Questions This Answers\n\n- \"What hooks does this ad use?\"\n- \"What's the emotional appeal?\"\n- \"How does this ad create urgency?\"\n- \"Who is this ad targeting?\"\n- \"What social proof is shown?\"\n- \"Deconstruct this competitor's ad\"\n","# Video Ad Deconstructor\n\nAI-powered deconstruction of video ad creatives into actionable marketing insights.\n\n## What This Skill Does\n\n- **Generate Summaries**: Product, features, audience, CTA extraction\n- **Deconstruct Marketing Dimensions**: Hooks, social proof, urgency, emotion, etc.\n- **Support Multiple Content Types**: Consumer products and gaming ads\n- **Progress Tracking**: Callback support for long analyses\n- **JSON Output**: Structured data for downstream processing\n\n## Setup\n\n### 1. Environment Variables\n\n```bash\n# Required for Gemini\nGOOGLE_APPLICATION_CREDENTIALS=\u002Fpath\u002Fto\u002Fservice-account.json\n```\n\n### 2. Dependencies\n\n```bash\npip install vertexai\n```\n\n## Usage\n\n### Basic Ad Deconstruction\n\n```python\nfrom scripts.deconstructor import AdDeconstructor\nfrom scripts.models import ExtractedVideoContent\nimport vertexai\nfrom vertexai.generative_models import GenerativeModel\n\n# Initialize Vertex AI\nvertexai.init(project=\"your-project-id\", location=\"us-central1\")\ngemini_model = GenerativeModel(\"gemini-1.5-flash\")\n\n# Create deconstructor\ndeconstructor = AdDeconstructor(gemini_model=gemini_model)\n\n# Create extracted content (from video-ad-analyzer or manually)\ncontent = ExtractedVideoContent(\n    video_path=\"ad.mp4\",\n    duration=30.0,\n    transcript=\"Tired of messy cables? Meet CableFlow...\",\n    text_timeline=[{\"at\": 0.0, \"text\": [\"50% OFF TODAY\"]}],\n    scene_timeline=[{\"timestamp\": 0.0, \"description\": \"Person frustrated with tangled cables\"}]\n)\n\n# Generate summary\nsummary = deconstructor.generate_summary(\n    transcript=content.transcript,\n    scenes=\"0.0s: Person frustrated with tangled cables\",\n    text_overlays=\"50% OFF TODAY\"\n)\nprint(summary)\n```\n\n### Full Deconstruction\n\n```python\n# Deconstruct all marketing dimensions\ndef on_progress(fraction, dimension):\n    print(f\"Progress: {fraction*100:.0f}% - Analyzed {dimension}\")\n\nanalysis = deconstructor.deconstruct(\n    extracted_content=content,\n    summary=summary,\n    is_gaming=False,  # Set True for gaming ads\n    on_progress=on_progress\n)\n\n# Access dimensions\nfor dimension, data in analysis.dimensions.items():\n    print(f\"\\n{dimension}:\")\n    print(data)\n```\n\n## Output Structure\n\n### Summary Output\n\n```\nProduct\u002FApp: CableFlow Cable Organizer\n\nKey Features:\nMagnetic design: Keeps cables organized automatically\nUniversal fit: Works with all cable types\nPremium materials: Durable silicone construction\n\nTarget Audience: Tech users frustrated with cable management\n\nCall to Action: Order now and get 50% off\n```\n\n### Deconstruction Output\n\n```python\n{\n    \"spoken_hooks\": {\n        \"elements\": [\n            {\n                \"hook_text\": \"Tired of messy cables?\",\n                \"timestamp\": \"0:00\",\n                \"hook_type\": \"Problem Question\",\n                \"effectiveness\": \"High - directly addresses pain point\"\n            }\n        ]\n    },\n    \"social_proof\": {\n        \"elements\": [\n            {\n                \"proof_type\": \"User Count\",\n                \"claim\": \"Over 1 million happy customers\",\n                \"credibility_score\": 7\n            }\n        ]\n    },\n    # ... more dimensions\n}\n```\n\n## Marketing Dimensions Deconstructed\n\n| Dimension | What It Extracts |\n|-----------|------------------|\n| `spoken_hooks` | Opening hooks from transcript |\n| `visual_hooks` | Attention-grabbing visuals |\n| `text_hooks` | On-screen text hooks |\n| `social_proof` | Testimonials, user counts, reviews |\n| `urgency_scarcity` | Limited time offers, stock warnings |\n| `emotional_triggers` | Fear, desire, belonging, etc. |\n| `problem_solution` | Pain points and solutions |\n| `cta_analysis` | Call-to-action effectiveness |\n| `target_audience` | Who the ad targets |\n| `unique_mechanism` | What makes product special |\n\n## Customizing Prompts\n\nEdit prompts in `prompts\u002Fmarketing_analysis.md` to customize:\n\n- What dimensions to analyze\n- Output format\n- Scoring criteria\n- Gaming vs consumer product focus\n\n## Common Questions This Answers\n\n- \"What hooks does this ad use?\"\n- \"What's the emotional appeal?\"\n- \"How does this ad create urgency?\"\n- \"Who is this ad targeting?\"\n- \"What social proof is shown?\"\n- \"Deconstruct this competitor's ad\"\n",{"name":5,"version":53,"description":7},"1.0.0","main","f4cb2d49f7667e79ef7804f50396d5aaffdf1f56","人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor",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":63,"prompt":73,"zipAvailable":74},[64,67,70],{"tool":65,"command":66},"npx","npx skills add Kernel8901\u002Fai-agent-skills-classification --skill video-ad-deconstructor",{"tool":68,"command":69},"bunx","bunx skills add Kernel8901\u002Fai-agent-skills-classification --skill video-ad-deconstructor",{"tool":71,"command":72},"pnpm","pnpm dlx skills add Kernel8901\u002Fai-agent-skills-classification --skill video-ad-deconstructor","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002FSKILL.md",true,[76,82,86,92,96],{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"_meta.json","REFERENCE","参考文档",310,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002F_meta.json",{"path":83,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":84,"sourceHtmlUrl":85},"prompts\u002Fmarketing_analysis.md",7777,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002Fprompts\u002Fmarketing_analysis.md",{"path":87,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":90,"sourceHtmlUrl":91},"scripts\u002Fdeconstructor.py","SCRIPT","脚本",11433,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002Fscripts\u002Fdeconstructor.py",{"path":93,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":94,"sourceHtmlUrl":95},"scripts\u002Fmodels.py",548,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002Fscripts\u002Fmodels.py",{"path":97,"resourceType":88,"resourceTypeDesc":89,"sizeBytes":98,"sourceHtmlUrl":99},"scripts\u002Fprompt_manager.py",4986,"https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F人工智能与大型语言模型\u002Fmeta-video-ad-deconstructor\u002Fscripts\u002Fprompt_manager.py",[101,124,156,167,177,207],{"skillNo":102,"name":103,"nameZh":6,"description":104,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":105,"categories":106,"keywords":107,"publishedAt":48},"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",[],[103,108,109,110,111,112,43,113,114,115,116,117,118,119,120,121,122,123],"safe","ethereum","defi","Optimization","user","Wallet","base","deposit","withdraw","yield","arbitrum","non-custodial","passive","defi yield","earn","yield optimization",{"skillNo":125,"name":126,"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":127,"categories":128,"keywords":138,"publishedAt":48},"01M6Q5R337B5JSNER4P4YS03TN","zerion-api","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api\u002FSKILL.md",[129,132,135],{"categoryNo":130,"categoryName":131,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":133,"categoryName":134,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发",{"categoryNo":136,"categoryName":137,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000030","开发工程师",[139,140,141,142,143,144,145,110,146,113,147,148,149,150,151,152,153,154,155],"api","zerion api","zerion","MCP","mcp server","server","blockchain","transactions","crypto wallet","crypto","evm","solana","query","across","positions","token","prices",{"skillNo":157,"name":158,"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":159,"categories":160,"keywords":164,"publishedAt":48},"01M6Q5QZFYTDAZMZ27BN5VZXAR","zerion-api-skill","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-skill\u002FSKILL.md",[161,162,163],{"categoryNo":130,"categoryName":131,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":133,"categoryName":134,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":136,"categoryName":137,"taxonomyType":28,"taxonomyTypeDesc":29},[139,165,166,140,141,142,143,144,145,110,146,113,148,149,150,151,152,153,154,155],"api skill","zerion api skill",{"skillNo":168,"name":169,"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":170,"categories":171,"keywords":175,"publishedAt":48},"01M6Q5QW17QW98RA3FE45E4RD8","zerion-api-mcp","https:\u002F\u002Fgithub.com\u002FKernel8901\u002Fai-agent-skills-classification\u002Fblob\u002Fmain\u002F金融\u002Fzerion-api-mcp\u002FSKILL.md",[172,173,174],{"categoryNo":130,"categoryName":131,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":133,"categoryName":134,"taxonomyType":20,"taxonomyTypeDesc":21},{"categoryNo":136,"categoryName":137,"taxonomyType":28,"taxonomyTypeDesc":29},[142,139,176,140,141,143,144,110,146,113,147,148,149,150,151,152,153,154,155],"zerion api mcp",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":186,"publishedAt":48},"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",[183],{"categoryNo":184,"categoryName":185,"taxonomyType":28,"taxonomyTypeDesc":29},"01JBSEEDCAT000000000000040","财务",[187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206],"finance","yahoo finance","forex","yahoo","yahoo finance forex","github","https","sentiment","real-time","python3","install","Market","usd","news","time","currency","gbp","pip","market data","for",{"skillNo":208,"name":209,"nameZh":6,"description":210,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":211,"categories":212,"keywords":214,"publishedAt":48},"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",[213],{"categoryNo":184,"categoryName":185,"taxonomyType":28,"taxonomyTypeDesc":29},[187,188,190,215,216,217,218,219,220,198,221,222,223,224,225,226,227,228,155,205],"cli","homebrew","find","check","brew","up","local","get","stock","link","trending","sf","quotes","ln"]