[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6JP5BWH2EQME2WRF2SN4BBH":3,"skill-same:01M6JP5BWH2EQME2WRF2SN4BBH":87},{"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":41,"linkOnly":42,"markdownContent":43,"markdownBody":44,"frontMatter":45,"refName":46,"commitSha":47,"skillDirectory":48,"forksCount":49,"repositoryHtmlUrl":50,"repositoryHomepageUrl":51,"ownerHtmlUrl":52,"ownerBlogUrl":53,"ownerDescription":6,"updatedAt":54,"install":55,"resources":68},"01M6JP5BWH2EQME2WRF2SN4BBH","hivefiver_mcp_research_loop",null,"Run iterative MCP-backed research with provider readiness checks, confidence scoring, contradiction control, and fallback governance.","LongLeo287\u002FOmniClaw","LongLeo287","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F141803367?v=4","01M6JK75C62VCNZKR5BRZ386YV","LongLeo","01M6JK75GQ78TSP1ZPXNKK3MCH",9,"MIT","https:\u002F\u002Fgithub.com\u002FLongLeo287\u002FOmniClaw\u002Fblob\u002Fmain\u002Fecosystem\u002Fskills\u002Fhivefiver_mcp_research_loop\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],"MCP","research","loop","research loop","hivefiver mcp research loop","governance","run","confidence scoring","fallback","provider","scoring","control","readiness","with","2026-11-10 10:12:30",false,"---\nname: hivefiver_mcp_research_loop\n\ndescription: Run iterative MCP-backed research with provider readiness checks, confidence scoring, contradiction control, and fallback governance.\n---\n\n# HiveFiver MCP Research Loop\n\nUse this skill when planning or architecture decisions require evidence-backed research.\n\n## Workflow\n1. Run provider readiness check.\n2. Build query matrix by domain lane.\n3. Retrieve evidence in grounded order:\n- DeepWiki + Repomix (repository grounding)\n- Context7 (official semantics)\n- Tavily + Exa (external corroboration)\n4. Build contradiction register.\n5. Score confidence and emit setup TODOs.\n\n## Provider Contract\nRequired providers:\n- Context7\n- DeepWiki\n- Repomix\n- Tavily\n- Exa (MCP or provider-native)\n\nIf any provider is unavailable, continue but downgrade confidence.\n\n## Confidence Contract\n- `full`: corroborated, no critical gaps.\n- `partial`: usable with non-critical gaps.\n- `low`: critical gaps or unresolved contradictions.\n\n## Retry Contract\n- Validate contradictions up to 10 iterations.\n- At attempt 10, force explicit caveat block.\n\n## DeepWiki QA Translation\n- Use transformed context layers for control.\n- Keep output structure stable.\n- Treat TODO\u002Ftask updates as first-class state.\n\n## References\n- `references\u002Fprovider-matrix.md`\n\n## Template\n- `templates\u002Fevidence-table.md`\n\n## Scripts\n- `scripts\u002Fcheck-mcp-readiness.mjs`\n- `scripts\u002Fscore-confidence.sh`\n","# HiveFiver MCP Research Loop\n\nUse this skill when planning or architecture decisions require evidence-backed research.\n\n## Workflow\n1. 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