[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M7CB8478TPPCN8S98A0KCNVC":3,"skill-same:01M7CB8478TPPCN8S98A0KCNVC":52},{"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":18,"publishedAt":19,"linkOnly":20,"markdownContent":21,"markdownBody":22,"frontMatter":23,"refName":30,"commitSha":31,"skillDirectory":32,"forksCount":33,"repositoryHtmlUrl":34,"repositoryHomepageUrl":6,"ownerHtmlUrl":35,"ownerBlogUrl":6,"ownerDescription":36,"updatedAt":37,"install":38,"resources":51},"01M7CB8478TPPCN8S98A0KCNVC","sf-datacloud-retrieve",null,"Salesforce Data Cloud Retrieve phase. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use sf-soql), segment creation or calculated insight design (use sf-datacloud-segment), or STDM\u002Fsession tracing\u002Fparquet analysis (use sf-ai-agentforce-observability).","om-scogo\u002Fskillsh-scraper","om-scogo","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F160466840?v=4","01M79AWB6DFVCQ7RK70CS34B96","Om Achrekar","01M79AWB6ZM9SKR8KDH1WSSM22",0,"MIT","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fjaganpro-sf-skills\u002Fsf-datacloud-retrieve\u002Fskills\u002Fsf-datacloud-retrieve\u002FSKILL.md",[],[],"2026-11-15 14:19:17",false,"---\nname: sf-datacloud-retrieve\ndescription: >\n  Salesforce Data Cloud Retrieve phase.\n  TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search,\n  search-index workflows, or metadata introspection for Data Cloud objects.\n  DO NOT TRIGGER when: the task is standard CRM SOQL (use sf-soql), segment creation\n  or calculated insight design (use sf-datacloud-segment), or STDM\u002Fsession tracing\u002Fparquet\n  analysis (use sf-ai-agentforce-observability).\nlicense: MIT\ncompatibility: \"Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org\"\nmetadata:\n  version: \"1.0.0\"\n  author: \"Gnanasekaran Thoppae\"\n  phase: \"Retrieve\"\n---\n\n# sf-datacloud-retrieve: Data Cloud Retrieve Phase\n\nUse this skill when the user needs **query, search, and metadata introspection** for Data Cloud: sync SQL, paginated SQL, async query workflows, table describe, vector search, hybrid search, or search index operations.\n\n## When This Skill Owns the Task\n\nUse `sf-datacloud-retrieve` when the work involves:\n- `sf data360 query *`\n- `sf data360 search-index *`\n- `sf data360 metadata *`\n- `sf data360 profile *` or `sf data360 insight *` inspection\n- understanding Data Cloud SQL results or query shape\n\nDelegate elsewhere when the user is:\n- writing standard CRM SOQL only → [sf-soql](..\u002Fsf-soql\u002FSKILL.md)\n- designing segment or calculated insight assets → [sf-datacloud-segment](..\u002Fsf-datacloud-segment\u002FSKILL.md)\n- analyzing STDM\u002Fsession tracing\u002Fparquet telemetry → [sf-ai-agentforce-observability](..\u002Fsf-ai-agentforce-observability\u002FSKILL.md)\n\n---\n\n## Required Context to Gather First\n\nAsk for or infer:\n- target org alias\n- whether the user needs quick count, medium result set, large export, schema inspection, or semantic search\n- table\u002Findex name if known\n- whether the task is read-only SQL or search-index lifecycle management\n\n---\n\n## Core Operating Rules\n\n- Treat Data Cloud SQL as its own query language, not SOQL.\n- Run the shared readiness classifier before relying on query\u002Fsearch surfaces: `node ~\u002F.claude\u002Fskills\u002Fsf-datacloud\u002Fscripts\u002Fdiagnose-org.mjs -o \u003Corg> --phase retrieve --json`.\n- Use describe before guessing columns.\n- Prefer `sqlv2` or async query flows for larger result sets.\n- Use vector search or hybrid search only when the search index lifecycle is healthy.\n- Keep STDM\u002Fparquet\u002Fsession-tracing workflows out of this skill family.\n\n---\n\n## Recommended Workflow\n\n### 1. Classify readiness for retrieve work\n```bash\nnode ~\u002F.claude\u002Fskills\u002Fsf-datacloud\u002Fscripts\u002Fdiagnose-org.mjs -o \u003Corg> --phase retrieve --json\n# optional query-plane probe, only with a real table name\nnode ~\u002F.claude\u002Fskills\u002Fsf-datacloud\u002Fscripts\u002Fdiagnose-org.mjs -o \u003Corg> --phase retrieve --describe-table MyDMO__dlm --json\n```\n\n### 2. Choose the smallest correct query shape\n```bash\nsf data360 query sql -o \u003Corg> --sql 'SELECT COUNT(*) FROM \"ssot__Individual__dlm\"' 2>\u002Fdev\u002Fnull\nsf data360 query sqlv2 -o \u003Corg> --sql 'SELECT * FROM \"ssot__Individual__dlm\"' 2>\u002Fdev\u002Fnull\nsf data360 query async-create -o \u003Corg> --sql 'SELECT * FROM \"ssot__Individual__dlm\"' 2>\u002Fdev\u002Fnull\n```\n\n### 3. Use describe before guessing fields\n```bash\nsf data360 query describe -o \u003Corg> --table ssot__Individual__dlm 2>\u002Fdev\u002Fnull\n```\n\n### 4. Use vector or hybrid search only when an index exists\n```bash\nsf data360 search-index list -o \u003Corg> 2>\u002Fdev\u002Fnull\nsf data360 query vector -o \u003Corg> --index Knowledge_Index --query \"reset password\" --limit 5 2>\u002Fdev\u002Fnull\nsf data360 query hybrid -o \u003Corg> --index Knowledge_Index --query \"reset password\" --limit 5 2>\u002Fdev\u002Fnull\nsf data360 query hybrid -o \u003Corg> --index Insurance_Index --query \"weather damage coverage\" --prefilter \"Type_of_Insurance__c='Home'\" --limit 10 2>\u002Fdev\u002Fnull\n```\n\n### 5. Reuse curated search-index examples when creating indexes\nUse the phase-owned examples instead of inventing JSON from scratch:\n- `examples\u002Fsearch-indexes\u002Fvector-knowledge.json`\n- `examples\u002Fsearch-indexes\u002Fhybrid-structured.json`\n\n---\n\n## High-Signal Gotchas\n\n- Data Cloud SQL is not SOQL.\n- Table names should be double-quoted in SQL.\n- `sqlv2` is better than ad hoc OFFSET paging for medium result sets.\n- async query is preferable for large results.\n- search-index operations and vector\u002Fhybrid queries depend on the index lifecycle being healthy.\n- Hybrid search can use `--prefilter`, but only on fields configured as prefilter-capable when the search index was created.\n- HNSW index parameters are typically read-only on create; leave `userValues: []` unless the platform explicitly documents otherwise.\n- `query describe` is not a universal tenant probe; only run it with a known DMO or DLO table after broader readiness has been confirmed.\n\n---\n\n## Output Format\n\n```text\nRetrieve task: \u003Csql \u002F sqlv2 \u002F async \u002F describe \u002F vector \u002F search-index>\nTarget org: \u003Calias>\nTarget object: \u003Ctable or index>\nCommands: \u003Ckey commands run>\nVerification: \u003Cquery rows \u002F schema \u002F status>\nNext step: \u003Csegment \u002F harmonize \u002F follow-up>\n```\n\n---\n\n## References\n\n- [README.md](README.md)\n- [examples\u002Fsearch-indexes\u002Fvector-knowledge.json](examples\u002Fsearch-indexes\u002Fvector-knowledge.json)\n- [examples\u002Fsearch-indexes\u002Fhybrid-structured.json](examples\u002Fsearch-indexes\u002Fhybrid-structured.json)\n- [..\u002Fsf-datacloud\u002Fassets\u002Fdefinitions\u002Fsearch-index.template.json](..\u002Fsf-datacloud\u002Fassets\u002Fdefinitions\u002Fsearch-index.template.json)\n- [..\u002Fsf-datacloud\u002Freferences\u002Fplugin-setup.md](..\u002Fsf-datacloud\u002Freferences\u002Fplugin-setup.md)\n- [..\u002Fsf-datacloud\u002Freferences\u002Ffeature-readiness.md](..\u002Fsf-datacloud\u002Freferences\u002Ffeature-readiness.md)\n","# sf-datacloud-retrieve: Data Cloud Retrieve Phase\n\nUse this skill when the user needs **query, search, and metadata introspection** for Data Cloud: sync SQL, paginated SQL, async query workflows, table describe, vector search, hybrid search, or search index operations.\n\n## When This Skill Owns the Task\n\nUse `sf-datacloud-retrieve` when the work involves:\n- `sf data360 query *`\n- `sf data360 search-index *`\n- `sf data360 metadata *`\n- `sf data360 profile *` or `sf data360 insight *` inspection\n- understanding Data Cloud SQL results or query shape\n\nDelegate elsewhere when the user is:\n- writing standard CRM SOQL only → [sf-soql](..\u002Fsf-soql\u002FSKILL.md)\n- designing segment or calculated insight assets → [sf-datacloud-segment](..\u002Fsf-datacloud-segment\u002FSKILL.md)\n- analyzing STDM\u002Fsession tracing\u002Fparquet telemetry → [sf-ai-agentforce-observability](..\u002Fsf-ai-agentforce-observability\u002FSKILL.md)\n\n---\n\n## Required Context to Gather First\n\nAsk for or infer:\n- target org alias\n- whether the user needs quick count, medium result set, large export, schema inspection, or semantic search\n- table\u002Findex name if known\n- whether the task is read-only SQL or search-index lifecycle management\n\n---\n\n## Core Operating Rules\n\n- Treat Data Cloud SQL as its own query language, not SOQL.\n- Run the shared readiness classifier before relying on query\u002Fsearch surfaces: `node ~\u002F.claude\u002Fskills\u002Fsf-datacloud\u002Fscripts\u002Fdiagnose-org.mjs -o \u003Corg> --phase retrieve --json`.\n- Use describe before guessing columns.\n- Prefer `sqlv2` or async query flows for larger result sets.\n- Use vector search or hybrid search only when the search index lifecycle is healthy.\n- Keep STDM\u002Fparquet\u002Fsession-tracing workflows out of this skill family.\n\n---\n\n## Recommended Workflow\n\n### 1. Classify readiness for retrieve work\n```bash\nnode ~\u002F.claude\u002Fskills\u002Fsf-datacloud\u002Fscripts\u002Fdiagnose-org.mjs -o \u003Corg> --phase retrieve --json\n# optional query-plane probe, only with a real table name\nnode ~\u002F.claude\u002Fskills\u002Fsf-datacloud\u002Fscripts\u002Fdiagnose-org.mjs -o \u003Corg> --phase retrieve --describe-table MyDMO__dlm --json\n```\n\n### 2. Choose the smallest correct query shape\n```bash\nsf data360 query sql -o \u003Corg> --sql 'SELECT COUNT(*) FROM \"ssot__Individual__dlm\"' 2>\u002Fdev\u002Fnull\nsf data360 query sqlv2 -o \u003Corg> --sql 'SELECT * FROM \"ssot__Individual__dlm\"' 2>\u002Fdev\u002Fnull\nsf data360 query async-create -o \u003Corg> --sql 'SELECT * FROM \"ssot__Individual__dlm\"' 2>\u002Fdev\u002Fnull\n```\n\n### 3. Use describe before guessing fields\n```bash\nsf data360 query describe -o \u003Corg> --table ssot__Individual__dlm 2>\u002Fdev\u002Fnull\n```\n\n### 4. Use vector or hybrid search only when an index exists\n```bash\nsf data360 search-index list -o \u003Corg> 2>\u002Fdev\u002Fnull\nsf data360 query vector -o \u003Corg> --index Knowledge_Index --query \"reset password\" --limit 5 2>\u002Fdev\u002Fnull\nsf data360 query hybrid -o \u003Corg> --index Knowledge_Index --query \"reset password\" --limit 5 2>\u002Fdev\u002Fnull\nsf data360 query hybrid -o \u003Corg> --index Insurance_Index --query \"weather damage coverage\" --prefilter \"Type_of_Insurance__c='Home'\" --limit 10 2>\u002Fdev\u002Fnull\n```\n\n### 5. Reuse curated search-index examples when creating indexes\nUse the phase-owned examples instead of inventing JSON from scratch:\n- `examples\u002Fsearch-indexes\u002Fvector-knowledge.json`\n- `examples\u002Fsearch-indexes\u002Fhybrid-structured.json`\n\n---\n\n## High-Signal Gotchas\n\n- Data Cloud SQL is not SOQL.\n- Table names should be double-quoted in SQL.\n- `sqlv2` is better than ad hoc OFFSET paging for medium result sets.\n- async query is preferable for large results.\n- search-index operations and vector\u002Fhybrid queries depend on the index lifecycle being healthy.\n- Hybrid search can use `--prefilter`, but only on fields configured as prefilter-capable when the search index was created.\n- HNSW index parameters are typically read-only on create; leave `userValues: []` unless the platform explicitly documents otherwise.\n- `query describe` is not a universal tenant probe; only run it with a known DMO or DLO table after broader readiness has been confirmed.\n\n---\n\n## Output Format\n\n```text\nRetrieve task: \u003Csql \u002F sqlv2 \u002F async \u002F describe \u002F vector \u002F search-index>\nTarget org: \u003Calias>\nTarget object: \u003Ctable or index>\nCommands: \u003Ckey commands run>\nVerification: \u003Cquery rows \u002F schema \u002F status>\nNext step: \u003Csegment \u002F harmonize \u002F follow-up>\n```\n\n---\n\n## References\n\n- [README.md](README.md)\n- [examples\u002Fsearch-indexes\u002Fvector-knowledge.json](examples\u002Fsearch-indexes\u002Fvector-knowledge.json)\n- [examples\u002Fsearch-indexes\u002Fhybrid-structured.json](examples\u002Fsearch-indexes\u002Fhybrid-structured.json)\n- [..\u002Fsf-datacloud\u002Fassets\u002Fdefinitions\u002Fsearch-index.template.json](..\u002Fsf-datacloud\u002Fassets\u002Fdefinitions\u002Fsearch-index.template.json)\n- [..\u002Fsf-datacloud\u002Freferences\u002Fplugin-setup.md](..\u002Fsf-datacloud\u002Freferences\u002Fplugin-setup.md)\n- [..\u002Fsf-datacloud\u002Freferences\u002Ffeature-readiness.md](..\u002Fsf-datacloud\u002Freferences\u002Ffeature-readiness.md)\n",{"name":5,"license":15,"metadata":24,"description":28,"compatibility":29},{"phase":25,"author":26,"version":27},"Retrieve","Gnanasekaran Thoppae","1.0.0","Salesforce Data Cloud Retrieve phase. TRIGGER when: user runs Data Cloud SQL, describe, async queries, vector search, search-index workflows, or metadata introspection for Data Cloud objects. DO NOT TRIGGER when: the task is standard CRM SOQL (use sf-soql), segment creation or calculated insight design (use sf-datacloud-segment), or STDM\u002Fsession tracing\u002Fparquet analysis (use sf-ai-agentforce-observability).\n","Requires an external community sf data360 CLI plugin and a Data Cloud-enabled org","main","8859218955d9504cd18b59e7892100718039d3b4","data\u002Fskills\u002Fjaganpro-sf-skills\u002Fsf-datacloud-retrieve\u002Fskills\u002Fsf-datacloud-retrieve",1,"https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper","https:\u002F\u002Fgithub.com\u002Fom-scogo","SDE-1 @ Scogo ","2026-07-18 12:22:46",{"source":8,"skillName":5,"commands":39,"prompt":49,"zipAvailable":50},[40,43,46],{"tool":41,"command":42},"npx","npx skills add om-scogo\u002Fskillsh-scraper --skill sf-datacloud-retrieve",{"tool":44,"command":45},"bunx","bunx skills add om-scogo\u002Fskillsh-scraper --skill sf-datacloud-retrieve",{"tool":47,"command":48},"pnpm","pnpm dlx skills add om-scogo\u002Fskillsh-scraper --skill sf-datacloud-retrieve","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fjaganpro-sf-skills\u002Fsf-datacloud-retrieve\u002Fskills\u002Fsf-datacloud-retrieve\u002FSKILL.md",true,[],[53,60,65,72,77,84],{"skillNo":54,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":57,"categories":58,"keywords":59,"publishedAt":19},"01M7D6MC9FAAEV4CMNYX8W7JTG","arxiv-search","Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fyorkeccak-scientific-skills\u002Farxiv-search\u002Fskills\u002Farxiv-search\u002FSKILL.md",[],[],{"skillNo":61,"name":55,"nameZh":6,"description":56,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":62,"categories":63,"keywords":64,"publishedAt":19},"01M7D6M85CKKN87TA98VCQV4H7","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fyorkeccak-scientific-skills\u002Farxiv-search\u002FSkill.md",[],[],{"skillNo":66,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":69,"categories":70,"keywords":71,"publishedAt":19},"01M7D4ZTCS4DR3CE4JY06X84T2","working-with-legacy-code","Safely change and test untested codebases using Feathers' \"Working Effectively with Legacy Code\". Use when the user mentions \"legacy code\", \"no tests\", \"untested codebase\", \"how do I test this\", \"seams\", \"characterization tests\", \"golden master\", \"sprout method\", \"afraid to change this code\", \"monster method\", \"dependency breaking\", or \"inherited a messy codebase\". Also trigger when changing code without tests safely, getting a class under test when constructors, statics, or singletons block it, adding features to tangled modules, or planning incremental test coverage for an old codebase. Covers the legacy-code change algorithm, seams, characterization tests, sprout\u002Fwrap, and dependency-breaking techniques. For refactoring code that already has tests, see refactoring-patterns. For day-to-day code quality, see clean-code.","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fworking-with-legacy-code\u002Fworking-with-legacy-code\u002FSKILL.md",[],[],{"skillNo":73,"name":67,"nameZh":6,"description":68,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":74,"categories":75,"keywords":76,"publishedAt":19},"01M7D4ZHZKJD2RSNGAF63V14P4","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fworking-with-legacy-code\u002FSkill.md",[],[],{"skillNo":78,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":81,"categories":82,"keywords":83,"publishedAt":19},"01M7D4ZB4CJBFW5JE67CPH2QRV","web-typography","Select, pair, and implement typefaces for web projects. Use when the user mentions \"font pairing\", \"which typeface\", \"line height\", \"responsive typography\", \"web font loading\", \"type hierarchy\", \"variable fonts\", \"FOUT\u002FFOIT\", \"typographic scale\", or \"the text is hard to read\". Also trigger when choosing between system fonts and web fonts, optimizing font-loading performance, or designing readable long-form content. Covers readability evaluation, CSS implementation, and performance optimization. For overall UI design systems, see refactoring-ui. For dramatic typographic experiences, see top-design.","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fweb-typography\u002Fweb-typography\u002FSKILL.md",[],[],{"skillNo":85,"name":79,"nameZh":6,"description":80,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":86,"categories":87,"keywords":88,"publishedAt":19},"01M7D4Z4MVYVY3NMJ18BNV6WNA","https:\u002F\u002Fgithub.com\u002Fom-scogo\u002Fskillsh-scraper\u002Fblob\u002Fmain\u002Fdata\u002Fskills\u002Fwondelai-skills\u002Fweb-typography\u002FSkill.md",[],[]]