[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6ECTW76GYR1XS8BEXR7QV69":3,"skill-same:01M6ECTW76GYR1XS8BEXR7QV69":95},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":26,"publishedAt":47,"linkOnly":48,"markdownContent":49,"markdownBody":50,"frontMatter":51,"refName":56,"commitSha":57,"skillDirectory":58,"forksCount":59,"repositoryHtmlUrl":60,"repositoryHomepageUrl":6,"ownerHtmlUrl":61,"ownerBlogUrl":62,"ownerDescription":63,"updatedAt":64,"install":65,"resources":78},"01M6ECTW76GYR1XS8BEXR7QV69","bgpt-paper-search",null,"Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.","levalencia\u002Fagent-god-mode","levalencia","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6962857?v=4","01M6EB0FZZ4BPQPC4HET4QY6NM","Luis Valencia ","01M6EB0G4HWHPWW0H9X7QKSAHP",3,"MIT","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fbgpt-paper-search\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000021","资料检索与研究","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"search","paper","bgpt paper search","use","scientific","via","literature","sample","methods","evidence synthesis","evidence","MCP","results","quality","text","Bash","server","https","mcp server","github","2026-11-10 10:12:30",false,"---\nname: bgpt-paper-search\ndescription: Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.\nallowed-tools: Bash\nlicense: MIT\nmetadata:\n    skill-author: BGPT\n    website: https:\u002F\u002Fbgpt.pro\u002Fmcp\n    github: https:\u002F\u002Fgithub.com\u002Fconnerlambden\u002Fbgpt-mcp\n---\n\n# BGPT Paper Search\n\n## Overview\n\nBGPT is a remote MCP server that searches a curated database of scientific papers built from raw experimental data extracted from full-text studies. Unlike traditional literature databases that return titles and abstracts, BGPT returns structured data from the actual paper content — methods, quantitative results, sample sizes, quality assessments, and 25+ metadata fields per paper.\n\n## When to Use This Skill\n\nUse this skill when:\n- Searching for scientific papers with specific experimental details\n- Conducting systematic or scoping literature reviews\n- Finding quantitative results, sample sizes, or effect sizes across studies\n- Comparing methodologies used in different studies\n- Looking for papers with quality scores or evidence grading\n- Needing structured data from full-text papers (not just abstracts)\n- Building evidence tables for meta-analyses or clinical guidelines\n\n## Setup\n\nBGPT is a remote MCP server — no local installation required.\n\n### Claude Desktop \u002F Claude Code\n\nAdd to your MCP configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"bgpt\": {\n      \"command\": \"npx\",\n      \"args\": [\"mcp-remote\", \"https:\u002F\u002Fbgpt.pro\u002Fmcp\u002Fsse\"]\n    }\n  }\n}\n```\n\n### npm (alternative)\n\n```bash\nnpx bgpt-mcp\n```\n\n## Usage\n\nOnce configured, use the `search_papers` tool provided by the BGPT MCP server:\n\n```\nSearch for papers about: \"CRISPR gene editing efficiency in human cells\"\n```\n\nThe server returns structured results including:\n- **Title, authors, journal, year, DOI**\n- **Methods**: Experimental techniques, models, protocols\n- **Results**: Key findings with quantitative data\n- **Sample sizes**: Number of subjects\u002Fsamples\n- **Quality scores**: Study quality assessments\n- **Conclusions**: Author conclusions and implications\n\n## Pricing\n\n- **Free tier**: 50 searches per network, no API key required\n- **Paid**: $0.01 per result with an API key from [bgpt.pro\u002Fmcp](https:\u002F\u002Fbgpt.pro\u002Fmcp)\n\n## Complementary Skills\n\nPairs well with:\n- `literature-review` — Use BGPT to gather structured data, then synthesize with literature-review workflows\n- `pubmed-database` — Use PubMed for broad searches, BGPT for deep experimental data\n- `biorxiv-database` — Combine preprint discovery with full-text data extraction\n- `citation-management` — Manage citations from BGPT search results\n","# BGPT Paper Search\n\n## Overview\n\nBGPT is a remote MCP server that searches a curated database of scientific papers built from raw experimental data extracted from full-text studies. Unlike traditional literature databases that return titles and abstracts, BGPT returns structured data from the actual paper content — methods, quantitative results, sample sizes, quality assessments, and 25+ metadata fields per paper.\n\n## When to Use This Skill\n\nUse this skill when:\n- Searching for scientific papers with specific experimental details\n- Conducting systematic or scoping literature reviews\n- Finding quantitative results, sample sizes, or effect sizes across studies\n- Comparing methodologies used in different studies\n- Looking for papers with quality scores or evidence grading\n- Needing structured data from full-text papers (not just abstracts)\n- Building evidence tables for meta-analyses or clinical guidelines\n\n## Setup\n\nBGPT is a remote MCP server — no local installation required.\n\n### Claude Desktop \u002F Claude Code\n\nAdd to your MCP configuration:\n\n```json\n{\n  \"mcpServers\": {\n    \"bgpt\": {\n      \"command\": \"npx\",\n      \"args\": [\"mcp-remote\", \"https:\u002F\u002Fbgpt.pro\u002Fmcp\u002Fsse\"]\n    }\n  }\n}\n```\n\n### npm (alternative)\n\n```bash\nnpx bgpt-mcp\n```\n\n## Usage\n\nOnce configured, use the `search_papers` tool provided by the BGPT MCP server:\n\n```\nSearch for papers about: \"CRISPR gene editing efficiency in human cells\"\n```\n\nThe server returns structured results including:\n- **Title, authors, journal, year, DOI**\n- **Methods**: Experimental techniques, models, protocols\n- **Results**: Key findings with quantitative data\n- **Sample sizes**: Number of subjects\u002Fsamples\n- **Quality scores**: Study quality assessments\n- **Conclusions**: Author conclusions and implications\n\n## Pricing\n\n- **Free tier**: 50 searches per network, no API key required\n- **Paid**: $0.01 per result with an API key from [bgpt.pro\u002Fmcp](https:\u002F\u002Fbgpt.pro\u002Fmcp)\n\n## Complementary Skills\n\nPairs well with:\n- `literature-review` — Use BGPT to gather structured data, then synthesize with literature-review workflows\n- `pubmed-database` — Use PubMed for broad searches, BGPT for deep experimental data\n- `biorxiv-database` — Combine preprint discovery with full-text data extraction\n- `citation-management` — Manage citations from BGPT search results\n",{"name":5,"license":15,"metadata":52,"description":7,"allowed-tools":42},{"github":53,"website":54,"skill-author":55},"https:\u002F\u002Fgithub.com\u002Fconnerlambden\u002Fbgpt-mcp","https:\u002F\u002Fbgpt.pro\u002Fmcp","BGPT","main","fa638b97a18ff6d9f26cf0eaf3dfa123531bcc87","organized-skills\u002Fbgpt-paper-search",1,"https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode","https:\u002F\u002Fgithub.com\u002Flevalencia","https:\u002F\u002Fmedium.com\u002F@luisevalencia","Microsoft MVP - Artificial Intelligence\r\nMachine Learning Enthusiast\r\n","2026-05-02 06:55:36",{"source":8,"skillName":5,"commands":66,"prompt":76,"zipAvailable":77},[67,70,73],{"tool":68,"command":69},"npx","npx skills add levalencia\u002Fagent-god-mode --skill bgpt-paper-search",{"tool":71,"command":72},"bunx","bunx skills add levalencia\u002Fagent-god-mode --skill bgpt-paper-search",{"tool":74,"command":75},"pnpm","pnpm dlx skills add levalencia\u002Fagent-god-mode --skill bgpt-paper-search","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fbgpt-paper-search\u002FSKILL.md",true,[79,85,91],{"path":80,"resourceType":81,"resourceTypeDesc":82,"sizeBytes":83,"sourceHtmlUrl":84},"agents\u002Fopenai.yaml","REFERENCE","参考文档",113,"https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fplugins\u002Flife-science-research\u002Fskills\u002Fbiobankjapan-phewas-skill\u002Fagents\u002Fopenai.yaml",{"path":86,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":89,"sourceHtmlUrl":90},"scripts\u002Fbiobankjapan_phewas.py","SCRIPT","脚本",7541,"https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fplugins\u002Flife-science-research\u002Fskills\u002Fbiobankjapan-phewas-skill\u002Fscripts\u002Fbiobankjapan_phewas.py",{"path":92,"resourceType":87,"resourceTypeDesc":88,"sizeBytes":93,"sourceHtmlUrl":94},"scripts\u002Fvariant_resolution.py",12206,"https:\u002F\u002Fgithub.com\u002FThamielis\u002F.agents\u002Fblob\u002Fmain\u002Fplugins\u002Flife-science-research\u002Fskills\u002Fbiobankjapan-phewas-skill\u002Fscripts\u002Fvariant_resolution.py",[96,117,149,175,201,229],{"skillNo":97,"name":98,"nameZh":6,"description":99,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":100,"categories":101,"keywords":102,"publishedAt":47},"01M6EM8ES5M9J4S8N78NJST0YC","zarr-python","Chunked N-D arrays for cloud storage. Compressed arrays, parallel I\u002FO, S3\u002FGCS integration, NumPy\u002FDask\u002FXarray compatible, for large-scale scientific computing pipelines.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzarr-python\u002FSKILL.md",[],[103,104,105,106,107,108,109,110,111,112,113,114,115,116,31],"python","zarr python","storage","integration","s3","gcs","cloud","dask","parallel","pipelines","arrays","numpy","scientific computing","scale",{"skillNo":118,"name":119,"nameZh":6,"description":120,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":121,"categories":122,"keywords":132,"publishedAt":47},"01M6EM8AR6ZFKZ9YVW8TTG850Z","zabbix","Zabbix monitoring system automation via API and Python. Use when: (1) Managing hosts, templates, items, triggers, or host groups, (2) Automating monitoring configuration, (3) Sending data via Zabbix trapper\u002Fsender, (4) Querying historical data or events, (5) Bulk operations on Zabbix objects, (6) Maintenance window management, (7) User\u002Fpermission management","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fzabbix-api-skill\u002FSKILL.md",[123,126,127],{"categoryNo":124,"categoryName":125,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":128,"categoryName":129,"taxonomyType":130,"taxonomyTypeDesc":131},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[119,103,133,134,135,136,137,138,139,140,30,141,142,143,144,145,146,147,32,148],"automation","window management","monitoring","data","templates","api","system","operations","maintenance","objects","permission","bulk operations","triggers","host","events","bulk",{"skillNo":150,"name":151,"nameZh":6,"description":152,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":153,"categories":154,"keywords":155,"publishedAt":47},"01M6EM6Q9JG846ECQY9A210A26","x-twitter-growth","X\u002FTwitter growth engine for building audience, crafting viral content, and analyzing engagement. Use when the user wants to grow on X\u002FTwitter, write tweets or threads, analyze their X profile, research competitors on X, plan a posting strategy, or optimize engagement. Complements social-content (generic multi-platform) with X-specific depth: algorithm mechanics, thread engineering, reply strategy, profile optimization, and competitive intelligence via web search.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fx-twitter-growth\u002FSKILL.md",[],[156,157,158,159,27,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174],"twitter","x twitter growth","growth","competitive intelligence","analyzing","plan","marketing","web search","competitors","analyze","write","optimize","social","intelligence","viral","growth engine","mechanics","grow","competitive",{"skillNo":176,"name":177,"nameZh":6,"description":178,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":179,"categories":180,"keywords":181,"publishedAt":47},"01M6EM3FQNPYNKPC4CV9TCVNGD","what-if-oracle","Run structured What-If scenario analysis with multi-branch possibility exploration. Use this skill when the user asks speculative questions like \"what if...\", \"what would happen if...\", \"what are the possibilities\", \"explore scenarios\", \"scenario analysis\", \"possibility space\", \"what could go wrong\", \"best case \u002F worst case\", \"risk analysis\", \"contingency planning\", \"strategic options\", or any question about uncertain futures. Also trigger when the user faces a fork-in-the-road decision, wants to stress-test an idea, or needs to think through consequences before committing.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fwhat-if-oracle\u002FSKILL.md",[],[182,183,184,185,186,187,188,189,190,191,192,193,30,194,195,196,197,198,199,200],"oracle","what if oracle","test","planning","ux","futures","risk","analysis","explore","decision","branch","question","exploration","options","stress-test","strategies","trigger","case","multi",{"skillNo":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":205,"categories":206,"keywords":210,"publishedAt":47},"01M6EM08C06RGDYCJ86HAPK0FF","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvercel-react-best-practices\u002FSKILL.md",[207],{"categoryNo":208,"categoryName":209,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[211,212,213,214,215,216,217,218,219,220,221,222,223,136,224,225,226,227,228,145],"react","vercel react best practices","best practices","react best practices","vercel","writing","performance","components","react components","guidelines","tasks","code","Optimization","Next.js","refactoring","performance optimization","next","pages",{"skillNo":230,"name":231,"nameZh":6,"description":232,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":233,"categories":234,"keywords":243,"publishedAt":47},"01M6EM00S13326QM08DTM94J6Z","venue-templates","Access comprehensive LaTeX templates, formatting requirements, and submission guidelines for major scientific publication venues (Nature, Science, PLOS, IEEE, ACM), academic conferences (NeurIPS, ICML, CVPR, CHI), research posters, and grant proposals (NSF, NIH, DOE, DARPA). This skill should be used when preparing manuscripts for journal submission, conference papers, research posters, or grant proposals and need venue-specific formatting requirements and templates.","https:\u002F\u002Fgithub.com\u002Flevalencia\u002Fagent-god-mode\u002Fblob\u002Fmain\u002Forganized-skills\u002Fvenue-templates\u002FSKILL.md",[235,238,239,240],{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":241,"categoryName":242,"taxonomyType":130,"taxonomyTypeDesc":131},"01JBSEEDCAT000000000000036","内容创作者",[137,244,245,220,246,247,248,249,42,250,251,166,252,253,254,255,256,257,31,258],"venue templates","research","NeurIPS","ICML","LaTeX","science","access","read","edit","proposals","Academic","requirements","formatting","journal","chi"]