[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6F76NQ0MST3EF1K7P04HQT4":3,"skill-same:01M6F76NQ0MST3EF1K7P04HQT4":75},{"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":46,"linkOnly":47,"markdownContent":48,"markdownBody":49,"frontMatter":50,"refName":34,"commitSha":55,"skillDirectory":56,"forksCount":57,"repositoryHtmlUrl":58,"repositoryHomepageUrl":6,"ownerHtmlUrl":59,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":60,"install":61,"resources":74},"01M6F76NQ0MST3EF1K7P04HQT4","hf-mcp",null,"Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.","boisenoise\u002Fskills-collections","boisenoise","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F193180?v=4","01M6CDBDQK6BDN55ZMA6B6PKBM","01M6CDBDVSJ3JPTBYN26FZ7TZK",12,"Apache-2.0","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-hf-mcp\u002FSKILL.md",[17],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45],"hf mcp","hf","MCP","repo","2026","when","papers","critical","as","get","wed","main","fetch","hugging face","gradio","Tools","server","search","run","documentation","official","ai tools","mcp server","2026-11-10 10:12:30",false,"---\nname: hf-mcp\ndescription: Use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.\nrisk: critical\nsource: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fhf-mcp\u002Fskills\u002Fhf-mcp\nsource_repo: huggingface\u002Fskills\nsource_type: official\ndate_added: 2026-07-01\nlicense: Apache-2.0\nlicense_source: https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE\n---\n\n# Hugging Face MCP Server\n## When to Use\n\nUse this skill when you need use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.\n\n\nConnect AI assistants to the Hugging Face Hub. Setup: https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Fmcp\n\nDiscover actual tool schemas and account permissions first; named Space tools and job operations are examples, not guaranteed installed functions. Popularity\u002Fdownloads are discovery signals, not evidence of task quality or safety. Review model\u002Fdata\u002Fcode licenses, revision and gated terms before use. Space invocation uploads input to its operator; show destination\u002Fpayload\u002Fretention and obtain authorization for private media or paid compute. Jobs need a bounded runtime\u002Fcost, reviewed pinned image\u002Fdependency\u002Fscript inputs, scoped secret references and a cancellation path; status\u002Flogs must confirm the actual job. A request to search does not authorize model execution, downloads, jobs or recurring schedules.\n\n## Use Cases & Examples\n\n### Find the Best Model for a Task\n\n```\nUser: \"Find the best model for code generation\"\n\n1. model_search(task=\"text-generation\", query=\"code\", sort=\"trendingScore\", limit=10)\n2. hub_repo_details(repo_ids=[\"top-result-id\"], include_readme=true)\n```\n\n### Compare Models from Different Providers\n\n```\nUser: \"Compare Llama vs Qwen for text generation\"\n\n1. model_search(author=\"meta-llama\", task=\"text-generation\", sort=\"downloads\", limit=5)\n2. model_search(author=\"Qwen\", task=\"text-generation\", sort=\"downloads\", limit=5)\n3. hub_repo_details(repo_ids=[\"meta-llama\u002FLlama-3.2-1B\", \"Qwen\u002FQwen3-8B\"], include_readme=true)\n```\n\n### Find Training Datasets\n\n```\nUser: \"Find datasets for sentiment analysis in English\"\n\n1. dataset_search(query=\"sentiment\", tags=[\"language:en\", \"task_categories:text-classification\"], sort=\"downloads\")\n2. hub_repo_details(repo_ids=[\"top-dataset-id\"], repo_type=\"dataset\", include_readme=true)\n```\n\n### Discover AI Tools (MCP Spaces)\n\n```\nUser: \"Find a tool that can remove image backgrounds\"\n\n1. space_search(query=\"background removal\", mcp=true)\n2. dynamic_space(operation=\"view_parameters\", space_name=\"result-space-id\")\n3. dynamic_space(operation=\"invoke\", space_name=\"result-space-id\", parameters=\"{...}\")\n```\n\n### Generate Images\n\n```\nUser: \"Create an image of a robot reading a book\"\n\n1. dynamic_space(operation=\"discover\")  # See available tasks\n2. gr1_flux1_schnell_infer(prompt=\"a robot sitting in a library reading a book, warm lighting, detailed\")\n```\n\n### Research a Topic\n\n```\nUser: \"What are the latest papers on RLHF?\"\n\n1. paper_search(query=\"reinforcement learning from human feedback\", results_limit=10)\n2. hub_repo_details(repo_ids=[\"paper-linked-model\"], include_readme=true)  # If paper links to models\n```\n\n### Learn How to Use a Library\n\n```\nUser: \"How do I fine-tune with LoRA using PEFT?\"\n\n1. hf_doc_search(query=\"LoRA fine-tuning\", product=\"peft\")\n2. hf_doc_fetch(doc_url=\"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fpeft\u002F...\")\n```\n\n### Run a Quick GPU Job\n\n```\nUser: \"Run this Python script on a GPU\"\n\nhf_jobs(operation=\"uv\", args={\n  \"script\": \"# \u002F\u002F\u002F script\\n# dependencies = [\\\"torch\\\"]\\n# \u002F\u002F\u002F\\nimport torch\\nprint(torch.cuda.is_available())\",\n  \"flavor\": \"t4-small\"\n})\n```\n\n### Train a Model on Cloud GPU\n\n```\nUser: \"Run my training script on an A10G\"\n\nhf_jobs(operation=\"run\", args={\n  \"image\": \"pytorch\u002Fpytorch:2.5.1-cuda12.4-cudnn9-runtime\",\n  \"command\": [\"\u002Fbin\u002Fsh\", \"-lc\", \"pip install transformers trl && python train.py\"],\n  \"flavor\": \"a10g-small\",\n  \"secrets\": {\"HF_TOKEN\": \"$HF_TOKEN\"}\n})\n```\n\n### Check Job Status\n\n```\nUser: \"What's happening with my training job?\"\n\n1. hf_jobs(operation=\"ps\")\n2. hf_jobs(operation=\"logs\", args={\"job_id\": \"job-xxxxx\"})\n```\n\n### Explore What's Trending\n\n```\nUser: \"What models are trending right now?\"\n\nmodel_search(sort=\"trendingScore\", limit=20)\n```\n\n### Get Model Card Details\n\n```\nUser: \"Tell me about Mistral-7B\"\n\nhub_repo_details(repo_ids=[\"mistralai\u002FMistral-7B-v0.1\"], include_readme=true)\n```\n\n### Find Quantized Models\n\n```\nUser: \"Find GGUF versions of Llama 3\"\n\nmodel_search(query=\"Llama 3 GGUF\", sort=\"downloads\", limit=10)\n```\n\n### Use a Gradio Space as a Tool\n\n```\nUser: \"Transcribe this audio file\"\n\n1. space_search(query=\"speech to text transcription\", mcp=true)\n2. dynamic_space(operation=\"view_parameters\", space_name=\"openai\u002Fwhisper\")\n3. dynamic_space(operation=\"invoke\", space_name=\"openai\u002Fwhisper\", parameters=\"{\\\"audio\\\": \\\"...\\\"}\")\n```\n\n### Schedule Recurring Jobs\n\n```\nUser: \"Run this data sync every day at midnight\"\n\nhf_jobs(operation=\"scheduled uv\", args={\n  \"script\": \"...\",\n  \"cron\": \"0 0 * * *\",\n  \"flavor\": \"cpu-basic\"\n})\n```\n\n## Tool Selection Guide\n\n| Goal | Tool |\n|------|------|\n| Find models | `model_search` |\n| Find datasets | `dataset_search` |\n| Find Spaces\u002Fapps | `space_search` |\n| Find papers | `paper_search` |\n| Get repo README\u002Fdetails | `hub_repo_details` |\n| Learn library usage | `hf_doc_search` → `hf_doc_fetch` |\n| Run code on GPU\u002FCPU | `hf_jobs` |\n| Use Gradio apps as tools | `dynamic_space` |\n| Generate images | `gr1_flux1_schnell_infer` or `dynamic_space` |\n| Check auth | `hf_whoami` |\n\n## Tips\n\n- Use `sort=\"trendingScore\"` to find what's popular now\n- Use `sort=\"downloads\"` to discover popular options; benchmark fit and inspect provenance independently\n- Set `mcp=true` in `space_search` to find Spaces usable as tools\n- Use `include_readme=true` in `hub_repo_details` for full model\u002Fdataset documentation\n- For jobs accessing private repos, always include `secrets: {\"HF_TOKEN\": \"$HF_TOKEN\"}`\n- Use `dynamic_space(operation=\"discover\")` to see all available Space-based tasks\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n","# Hugging Face MCP Server\n## When to Use\n\nUse this skill when you need use Hugging Face Hub via MCP server tools. Search models, datasets, Spaces, papers. Get repo details, fetch documentation, run compute jobs, and use Gradio Spaces as AI tools. Available when connected to the HF MCP server.\n\n\nConnect AI assistants to the Hugging Face Hub. Setup: https:\u002F\u002Fhuggingface.co\u002Fsettings\u002Fmcp\n\nDiscover actual tool schemas and account permissions first; named Space tools and job operations are examples, not guaranteed installed functions. Popularity\u002Fdownloads are discovery signals, not evidence of task quality or safety. Review model\u002Fdata\u002Fcode licenses, revision and gated terms before use. Space invocation uploads input to its operator; show destination\u002Fpayload\u002Fretention and obtain authorization for private media or paid compute. Jobs need a bounded runtime\u002Fcost, reviewed pinned image\u002Fdependency\u002Fscript inputs, scoped secret references and a cancellation path; status\u002Flogs must confirm the actual job. A request to search does not authorize model execution, downloads, jobs or recurring schedules.\n\n## Use Cases & Examples\n\n### Find the Best Model for a Task\n\n```\nUser: \"Find the best model for code generation\"\n\n1. model_search(task=\"text-generation\", query=\"code\", sort=\"trendingScore\", limit=10)\n2. hub_repo_details(repo_ids=[\"top-result-id\"], include_readme=true)\n```\n\n### Compare Models from Different Providers\n\n```\nUser: \"Compare Llama vs Qwen for text generation\"\n\n1. model_search(author=\"meta-llama\", task=\"text-generation\", sort=\"downloads\", limit=5)\n2. model_search(author=\"Qwen\", task=\"text-generation\", sort=\"downloads\", limit=5)\n3. hub_repo_details(repo_ids=[\"meta-llama\u002FLlama-3.2-1B\", \"Qwen\u002FQwen3-8B\"], include_readme=true)\n```\n\n### Find Training Datasets\n\n```\nUser: \"Find datasets for sentiment analysis in English\"\n\n1. dataset_search(query=\"sentiment\", tags=[\"language:en\", \"task_categories:text-classification\"], sort=\"downloads\")\n2. hub_repo_details(repo_ids=[\"top-dataset-id\"], repo_type=\"dataset\", include_readme=true)\n```\n\n### Discover AI Tools (MCP Spaces)\n\n```\nUser: \"Find a tool that can remove image backgrounds\"\n\n1. space_search(query=\"background removal\", mcp=true)\n2. dynamic_space(operation=\"view_parameters\", space_name=\"result-space-id\")\n3. dynamic_space(operation=\"invoke\", space_name=\"result-space-id\", parameters=\"{...}\")\n```\n\n### Generate Images\n\n```\nUser: \"Create an image of a robot reading a book\"\n\n1. dynamic_space(operation=\"discover\")  # See available tasks\n2. gr1_flux1_schnell_infer(prompt=\"a robot sitting in a library reading a book, warm lighting, detailed\")\n```\n\n### Research a Topic\n\n```\nUser: \"What are the latest papers on RLHF?\"\n\n1. paper_search(query=\"reinforcement learning from human feedback\", results_limit=10)\n2. hub_repo_details(repo_ids=[\"paper-linked-model\"], include_readme=true)  # If paper links to models\n```\n\n### Learn How to Use a Library\n\n```\nUser: \"How do I fine-tune with LoRA using PEFT?\"\n\n1. hf_doc_search(query=\"LoRA fine-tuning\", product=\"peft\")\n2. hf_doc_fetch(doc_url=\"https:\u002F\u002Fhuggingface.co\u002Fdocs\u002Fpeft\u002F...\")\n```\n\n### Run a Quick GPU Job\n\n```\nUser: \"Run this Python script on a GPU\"\n\nhf_jobs(operation=\"uv\", args={\n  \"script\": \"# \u002F\u002F\u002F script\\n# dependencies = [\\\"torch\\\"]\\n# \u002F\u002F\u002F\\nimport torch\\nprint(torch.cuda.is_available())\",\n  \"flavor\": \"t4-small\"\n})\n```\n\n### Train a Model on Cloud GPU\n\n```\nUser: \"Run my training script on an A10G\"\n\nhf_jobs(operation=\"run\", args={\n  \"image\": \"pytorch\u002Fpytorch:2.5.1-cuda12.4-cudnn9-runtime\",\n  \"command\": [\"\u002Fbin\u002Fsh\", \"-lc\", \"pip install transformers trl && python train.py\"],\n  \"flavor\": \"a10g-small\",\n  \"secrets\": {\"HF_TOKEN\": \"$HF_TOKEN\"}\n})\n```\n\n### Check Job Status\n\n```\nUser: \"What's happening with my training job?\"\n\n1. hf_jobs(operation=\"ps\")\n2. hf_jobs(operation=\"logs\", args={\"job_id\": \"job-xxxxx\"})\n```\n\n### Explore What's Trending\n\n```\nUser: \"What models are trending right now?\"\n\nmodel_search(sort=\"trendingScore\", limit=20)\n```\n\n### Get Model Card Details\n\n```\nUser: \"Tell me about Mistral-7B\"\n\nhub_repo_details(repo_ids=[\"mistralai\u002FMistral-7B-v0.1\"], include_readme=true)\n```\n\n### Find Quantized Models\n\n```\nUser: \"Find GGUF versions of Llama 3\"\n\nmodel_search(query=\"Llama 3 GGUF\", sort=\"downloads\", limit=10)\n```\n\n### Use a Gradio Space as a Tool\n\n```\nUser: \"Transcribe this audio file\"\n\n1. space_search(query=\"speech to text transcription\", mcp=true)\n2. dynamic_space(operation=\"view_parameters\", space_name=\"openai\u002Fwhisper\")\n3. dynamic_space(operation=\"invoke\", space_name=\"openai\u002Fwhisper\", parameters=\"{\\\"audio\\\": \\\"...\\\"}\")\n```\n\n### Schedule Recurring Jobs\n\n```\nUser: \"Run this data sync every day at midnight\"\n\nhf_jobs(operation=\"scheduled uv\", args={\n  \"script\": \"...\",\n  \"cron\": \"0 0 * * *\",\n  \"flavor\": \"cpu-basic\"\n})\n```\n\n## Tool Selection Guide\n\n| Goal | Tool |\n|------|------|\n| Find models | `model_search` |\n| Find datasets | `dataset_search` |\n| Find Spaces\u002Fapps | `space_search` |\n| Find papers | `paper_search` |\n| Get repo README\u002Fdetails | `hub_repo_details` |\n| Learn library usage | `hf_doc_search` → `hf_doc_fetch` |\n| Run code on GPU\u002FCPU | `hf_jobs` |\n| Use Gradio apps as tools | `dynamic_space` |\n| Generate images | `gr1_flux1_schnell_infer` or `dynamic_space` |\n| Check auth | `hf_whoami` |\n\n## Tips\n\n- Use `sort=\"trendingScore\"` to find what's popular now\n- Use `sort=\"downloads\"` to discover popular options; benchmark fit and inspect provenance independently\n- Set `mcp=true` in `space_search` to find Spaces usable as tools\n- Use `include_readme=true` in `hub_repo_details` for full model\u002Fdataset documentation\n- For jobs accessing private repos, always include `secrets: {\"HF_TOKEN\": \"$HF_TOKEN\"}`\n- Use `dynamic_space(operation=\"discover\")` to see all available Space-based tasks\n\n## Limitations\n\n- Use this skill only when the task clearly matches its upstream product or API scope.\n- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.\n- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.\n",{"name":5,"risk":30,"source":51,"license":14,"date_added":52,"description":7,"source_repo":53,"source_type":43,"license_source":54},"https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Ftree\u002Fmain\u002Fhf-mcp\u002Fskills\u002Fhf-mcp",1782864000000,"huggingface\u002Fskills","https:\u002F\u002Fgithub.com\u002Fhuggingface\u002Fskills\u002Fblob\u002Fmain\u002FLICENSE","7bf2279c8a65758319526a721d5c4b75683aec9c","skills\u002Fantigravity-hf-mcp",3,"https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections","https:\u002F\u002Fgithub.com\u002Fboisenoise","2026-11-12 14:21:24",{"source":8,"skillName":5,"commands":62,"prompt":72,"zipAvailable":73},[63,66,69],{"tool":64,"command":65},"npx","npx skills add boisenoise\u002Fskills-collections --skill hf-mcp",{"tool":67,"command":68},"bunx","bunx skills add boisenoise\u002Fskills-collections --skill hf-mcp",{"tool":70,"command":71},"pnpm","pnpm dlx skills add boisenoise\u002Fskills-collections --skill hf-mcp","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-hf-mcp\u002FSKILL.md",true,[],[76,98,121,147,166,189],{"skillNo":77,"name":78,"nameZh":6,"description":79,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":80,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":46},"01M6WNH92BHVFXHGPDZCPJ0AG0","web-access","Use Treg for public web or platform data only when existing search, browser and connected tools cannot cover the task, with explicit cost and data boundaries.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-web-access\u002FSKILL.md",[],[84,85,86,43,87,88,40,89,38,90,35,91,92,93,94,30,95,96,97,28,27],"web","access","web access","development","browser","data","read","only","youtube","free tools","platform","free","cover","normal",{"skillNo":99,"name":100,"nameZh":6,"description":101,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":102,"sourceHtmlUrl":103,"categories":104,"keywords":106,"publishedAt":46},"01M6WNF9XAYAB2CZDDJBAKNM93","treg","Use the Treg CLI or MCP catalog to discover, price and call authorized external tools with team-scoped credentials and a user-approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-treg\u002FSKILL.md",[105],{"categoryNo":18,"categoryName":19,"taxonomyType":20,"taxonomyTypeDesc":21},[100,25,107,108,43,87,109,38,110,111,112,113,114,115,116,30,117,118,119,120,27],"cli","github","license","Budget","credentials","team","catalog","use","call","discover","external tools","with","scoped","blob",{"skillNo":122,"name":123,"nameZh":6,"description":124,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":125,"sourceHtmlUrl":126,"categories":127,"keywords":133,"publishedAt":46},"01M6WN93MEAZ4E41DYAHJRPHQT","recruit","Turn a job description into an evidence-linked candidate shortlist through Treg, with job-related ranking and optional authorized business contact enrichment.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-recruit\u002FSKILL.md",[128],{"categoryNo":129,"categoryName":130,"taxonomyType":131,"taxonomyTypeDesc":132},"01JBSEEDCAT000000000000038","人力资源","PROFESSION","适用职业",[123,108,43,109,134,135,136,137,138,139,30,140,141,142,118,143,144,145,146,120,100,27],"https","email","enrichment","business","evidence","phone","ranking","post","job description","for","person","candidate","reason",{"skillNo":148,"name":149,"nameZh":6,"description":150,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":151,"sourceHtmlUrl":152,"categories":153,"keywords":154,"publishedAt":46},"01M6WN8R5RXSMHM9B4D9FAFX42","rea-growth","Analyze a company’s public growth and content through Treg, distinguish observations from hypotheses, and produce an evidence-linked playbook within an approved budget.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-rea-growth\u002FSKILL.md",[],[155,156,157,108,43,109,158,159,134,160,110,161,162,163,138,30,164,165,120,100,27],"growth","rea","rea growth","produce","content","playbook","marketing","analyze","public","observations","company",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":170,"sourceHtmlUrl":171,"categories":172,"keywords":173,"publishedAt":46},"01M6WN2EVE3RQRGWAKBT4S7YT2","make-ugc","Create original AI UGC-style product videos through Treg: research hooks, approve a licensed presenter and voice, generate clips, add captions and report costs.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-make-ugc\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-make-ugc\u002FSKILL.md",[],[174,175,108,176,43,177,109,178,179,180,181,182,30,183,184,185,186,187,188,120],"make","make ugc","research","create","Video","Captions","ads","short","hooks","head","style","niche","trending","talking-head","approve",{"skillNo":190,"name":191,"nameZh":6,"description":192,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":193,"sourceHtmlUrl":194,"categories":195,"keywords":196,"publishedAt":46},"01M6WN10ARAA63Y8K1YNGXJ2RM","lead-signals","Find and qualify public buying signals through Treg, rank fitting business accounts and optionally enrich authorized business contact information.","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FLICENSE","https:\u002F\u002Fgithub.com\u002Fboisenoise\u002Fskills-collections\u002Fblob\u002Fmain\u002Fskills\u002Fantigravity-lead-signals\u002FSKILL.md",[],[197,198,199,108,43,109,200,201,137,202,31,30,203,204,205,206,207,208,209,120],"signals","lead","lead signals","how","find","ready","how to","qualify","people","information","enrich","changes","funding"]