[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M69BDYMX0FVKJE5GPWECAVZC":3,"skill-same:01M69BDYMX0FVKJE5GPWECAVZC":73},{"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":39,"linkOnly":40,"markdownContent":41,"markdownBody":42,"frontMatter":43,"refName":44,"commitSha":45,"skillDirectory":46,"forksCount":47,"repositoryHtmlUrl":48,"repositoryHomepageUrl":6,"ownerHtmlUrl":49,"ownerBlogUrl":50,"ownerDescription":51,"updatedAt":52,"install":53,"resources":66},"01M69BDYMX0FVKJE5GPWECAVZC","schema-discovery",null,"Explore Power BI semantic model schemas using DAX INFO functions. Progressive metadata discovery strategy, scope estimation, and narrowing techniques for tables, columns, measures, and relationships.","microsoft\u002FValueLens-for-Microsoft-Copilot","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M69BCX78JTCQ42HNQPM53DBN",23,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FValueLens-for-Microsoft-Copilot\u002Fblob\u002Fmain\u002Ffabric-app\u002F.agents\u002Fskills\u002Fschema-discovery\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38],"schema discovery","discovery","schema","progressive","dax","for","techniques","tables","columns","bi","scope","estimation","relationships","info","functions","power","strategy","model","explore","power bi","2026-11-10 10:12:30",false,"---\nname: schema-discovery\ndescription: >\n  Explore Power BI semantic model schemas using DAX INFO functions.\n  Progressive metadata discovery strategy, scope estimation, and\n  narrowing techniques for tables, columns, measures, and relationships.\n---\n\n# Schema Discovery\n\n## Table of Contents\n\n| Task | Reference | Notes |\n|---|---|---|\n| Must\u002FPrefer\u002FAvoid | [SKILL.md: Must\u002FPrefer\u002FAvoid](#must--prefer--avoid) | Guardrails for schema discovery |\n| Progressive Discovery Strategy | [SKILL.md: Progressive Schema Discovery](#progressive-schema-discovery) | Decision tree for on-demand metadata fetching |\n| Recommended Discovery Order | [SKILL.md: Recommended Discovery Order](#recommended-discovery-order) | Start with scope estimation → tables → columns → measures → relationships |\n| Metadata Object → INFO Function Map | [SKILL.md: Metadata Object → INFO Function Map](#metadata-object---info-function-map) | Tables, columns, measures, relationships, calc groups, calendars, UDFs, variations |\n| Scope Estimation Queries | [discovery-queries.md: Scope Estimation Queries](.\u002Freferences\u002Fdiscovery-queries.md#scope-estimation-queries) | Probe table\u002Fcolumn\u002Fmeasure\u002Frelationship counts before deep discovery |\n| INFO Output Columns | [discovery-queries.md: INFO Output Columns](.\u002Freferences\u002Fdiscovery-queries.md#info-output-columns) | INFO.VIEW.* (read access); INFO.* (may need elevated access) |\n| Narrowing Results (Projection + Filtering) | [discovery-queries.md: Narrowing Results](.\u002Freferences\u002Fdiscovery-queries.md#narrowing-results-projection--filtering) | SELECTCOLUMNS + FILTER to reduce output volume |\n| Advanced Metadata (Calc Groups, Calendars, UDFs) | [discovery-queries.md: Advanced Metadata Queries](.\u002Freferences\u002Fdiscovery-queries.md#advanced-metadata-queries) | INFO.CALCULATIONGROUPS, INFO.CALENDARS, INFO.USERDEFINEDFUNCTIONS, INFO.VARIATIONS |\n| Complete INFO Function Catalog | [discovery-queries.md: Complete INFO Function Catalog](.\u002Freferences\u002Fdiscovery-queries.md#complete-info-function-catalog-dynamic) | Dynamic query to enumerate all INFO functions in the engine |\n\n## Must \u002F Prefer \u002F Avoid\n\n### Must\n- Use fully-qualified `'Table'[Column]` for column references\n- Use simple `[Measure]` for measure references\n\n### Prefer\n- INFO.VIEW functions for initial metadata discovery (read access, lightweight)\n- Progressive schema discovery over full schema dumps\n- SELECTCOLUMNS + FILTER to narrow INFO results\n\n### Avoid\n- Fetching full schema upfront — discover incrementally based on need\n- Re-fetching metadata already discovered in this conversation\n- Using GetSemanticModelSchema or GenerateQuery MCP tools (these are not available)\n- Using `DiscoverArtifacts` to find a semantic model — that MCP tool is not available; use `npx fabric-app-data search` (see [fabric-cli](..\u002Ffabric-cli\u002FSKILL.md)) instead\n\n## Progressive Schema Discovery\n\nDiscover metadata incrementally based on what the user actually needs.\n\n### Strategy (Decision Tree)\n\n```\nUser asks a question\n  → Do I know which tables are relevant?\n    → NO: Run INFO.VIEW.TABLES() to get table inventory\n    → YES: Do I know the columns\u002Fmeasures for those tables?\n      → NO: Run filtered INFO.VIEW.COLUMNS() and INFO.VIEW.MEASURES() for those tables\n      → YES: Do I need relationships?\n        → YES: Run INFO.VIEW.RELATIONSHIPS() filtered to those tables\n        → NO: Do I need advanced metadata?\n          → Have elevated INFO functions already failed in this session?\n            → YES: Skip — assume no permission for all elevated queries\n            → NO: Try the relevant elevated query:\n              → Calculation groups: INFO.CALCULATIONGROUPS() + INFO.CALCULATIONITEMS()\n              → Calendars: INFO.CALENDARS()\n              → User-defined functions: INFO.USERDEFINEDFUNCTIONS()\n              → Variations: INFO.VARIATIONS()\n              → If query fails with permission error: mark elevated access as unavailable\n  → Use discovered schema to write DAX queries (see dax-authoring skill)\n```\n\n### Rules\n\n- **Start with scope estimation** — Run the scope probe query first to understand model size\n- **Discover on demand** — Only fetch tables\u002Fcolumns\u002Fmeasures relevant to the current user request\n- **Use INFO.VIEW functions first** (read access) — tables, columns, measures, relationships\n- **Use INFO functions** (may need elevated access) for: calculation groups, calculation items, variations, user-defined functions, calendars\n- **Handle permission failures gracefully** — If an elevated INFO function fails but INFO.VIEW.* functions succeeded, assume the user lacks elevated permissions and skip all elevated discoveries entirely\n- **Always narrow results** — Use SELECTCOLUMNS + FILTER to fetch only needed columns and rows\n- **Cache discovered schema mentally** — Don't re-fetch what you've already discovered in this conversation\n\n### Recommended Discovery Order\n\n1. **Scope estimation** — Count tables, columns, measures, relationships\n2. **Tables** — Get table names, identify relevant ones\n3. **Columns** — Get columns for relevant tables only\n4. **Measures** — Get measures (prefer using these over raw aggregations)\n5. **Relationships** — Get relationships between relevant tables\n6. **Advanced metadata** (if needed) — Calculation groups, calendars, UDFs, variations\n\n### Scope Estimation Query\n\n```dax\nEVALUATE\nROW(\n    \"TableCount\", COUNTROWS(INFO.VIEW.TABLES()),\n    \"ColumnCount\", COUNTROWS(INFO.VIEW.COLUMNS()),\n    \"MeasureCount\", COUNTROWS(INFO.VIEW.MEASURES()),\n    \"RelationshipCount\", COUNTROWS(INFO.VIEW.RELATIONSHIPS())\n)\n```\n\n### Metadata Object → INFO Function Map\n\n| Metadata Object | Primary INFO Functions | Access Level |\n|---|---|---|\n| Tables | `INFO.VIEW.TABLES()` | Read |\n| Columns | `INFO.VIEW.COLUMNS()` | Read |\n| Measures | `INFO.VIEW.MEASURES()` | Read |\n| Relationships | `INFO.VIEW.RELATIONSHIPS()` | Read |\n| Model config | `INFO.MODEL()` | May need elevated |\n| Calculation groups | `INFO.CALCULATIONGROUPS()`, `INFO.CALCULATIONITEMS()` | May need elevated |\n| Calendars | `INFO.CALENDARS()`, `INFO.CALENDARCOLUMNGROUPS()`, `INFO.CALENDARCOLUMNREFERENCES()` | May need elevated |\n| User-defined functions | `INFO.USERDEFINEDFUNCTIONS()` | May need elevated |\n| Variations | `INFO.VARIATIONS()` | May need elevated |\n\nFor the full query catalog and output column details, see [discovery-queries.md](.\u002Freferences\u002Fdiscovery-queries.md).\n\n## Running Discovery Queries\n\nUse `npx fabric-app-data query \u003Calias> --query '\u003CDAX>'` to execute INFO queries against a semantic model. For full CLI options (profiles, file input, result limits), see the `fabric-cli` skill.\n\n```bash\nnpx fabric-app-data query \u003Calias> --query \"EVALUATE INFO.VIEW.TABLES()\"\n```\n\n## Troubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| INFO functions return permission errors | Fall back to INFO.VIEW functions; mark elevated access as unavailable for this session |\n| Metadata output too large | Use scope estimation + narrowing patterns from [discovery-queries.md](.\u002Freferences\u002Fdiscovery-queries.md) |\n","# Schema Discovery\n\n## Table of Contents\n\n| Task | Reference | Notes |\n|---|---|---|\n| Must\u002FPrefer\u002FAvoid | [SKILL.md: Must\u002FPrefer\u002FAvoid](#must--prefer--avoid) | Guardrails for schema discovery |\n| Progressive Discovery Strategy | [SKILL.md: Progressive Schema Discovery](#progressive-schema-discovery) | Decision tree for on-demand metadata fetching |\n| Recommended Discovery Order | [SKILL.md: Recommended Discovery Order](#recommended-discovery-order) | Start with scope estimation → tables → columns → measures → relationships |\n| Metadata Object → INFO Function Map | [SKILL.md: Metadata Object → INFO Function Map](#metadata-object---info-function-map) | Tables, columns, measures, relationships, calc groups, calendars, UDFs, variations |\n| Scope Estimation Queries | [discovery-queries.md: Scope Estimation Queries](.\u002Freferences\u002Fdiscovery-queries.md#scope-estimation-queries) | Probe table\u002Fcolumn\u002Fmeasure\u002Frelationship counts before deep discovery |\n| INFO Output Columns | [discovery-queries.md: INFO Output Columns](.\u002Freferences\u002Fdiscovery-queries.md#info-output-columns) | INFO.VIEW.* (read access); INFO.* (may need elevated access) |\n| Narrowing Results (Projection + Filtering) | [discovery-queries.md: Narrowing Results](.\u002Freferences\u002Fdiscovery-queries.md#narrowing-results-projection--filtering) | SELECTCOLUMNS + FILTER to reduce output volume |\n| Advanced Metadata (Calc Groups, Calendars, UDFs) | [discovery-queries.md: Advanced Metadata Queries](.\u002Freferences\u002Fdiscovery-queries.md#advanced-metadata-queries) | INFO.CALCULATIONGROUPS, INFO.CALENDARS, INFO.USERDEFINEDFUNCTIONS, INFO.VARIATIONS |\n| Complete INFO Function Catalog | [discovery-queries.md: Complete INFO Function Catalog](.\u002Freferences\u002Fdiscovery-queries.md#complete-info-function-catalog-dynamic) | Dynamic query to enumerate all INFO functions in the engine |\n\n## Must \u002F Prefer \u002F Avoid\n\n### Must\n- Use fully-qualified `'Table'[Column]` for column references\n- Use simple `[Measure]` for measure references\n\n### Prefer\n- INFO.VIEW functions for initial metadata discovery (read access, lightweight)\n- Progressive schema discovery over full schema dumps\n- SELECTCOLUMNS + FILTER to narrow INFO results\n\n### Avoid\n- Fetching full schema upfront — discover incrementally based on need\n- Re-fetching metadata already discovered in this conversation\n- Using GetSemanticModelSchema or GenerateQuery MCP tools (these are not available)\n- Using `DiscoverArtifacts` to find a semantic model — that MCP tool is not available; use `npx fabric-app-data search` (see [fabric-cli](..\u002Ffabric-cli\u002FSKILL.md)) instead\n\n## Progressive Schema Discovery\n\nDiscover metadata incrementally based on what the user actually needs.\n\n### Strategy (Decision Tree)\n\n```\nUser asks a question\n  → Do I know which tables are relevant?\n    → NO: Run INFO.VIEW.TABLES() to get table inventory\n    → YES: Do I know the columns\u002Fmeasures for those tables?\n      → NO: Run filtered INFO.VIEW.COLUMNS() and INFO.VIEW.MEASURES() for those tables\n      → YES: Do I need relationships?\n        → YES: Run INFO.VIEW.RELATIONSHIPS() filtered to those tables\n        → NO: Do I need advanced metadata?\n          → Have elevated INFO functions already failed in this session?\n            → YES: Skip — assume no permission for all elevated queries\n            → NO: Try the relevant elevated query:\n              → Calculation groups: INFO.CALCULATIONGROUPS() + INFO.CALCULATIONITEMS()\n              → Calendars: INFO.CALENDARS()\n              → User-defined functions: INFO.USERDEFINEDFUNCTIONS()\n              → Variations: INFO.VARIATIONS()\n              → If query fails with permission error: mark elevated access as unavailable\n  → Use discovered schema to write DAX queries (see dax-authoring skill)\n```\n\n### Rules\n\n- **Start with scope estimation** — Run the scope probe query first to understand model size\n- **Discover on demand** — Only fetch tables\u002Fcolumns\u002Fmeasures relevant to the current user request\n- **Use INFO.VIEW functions first** (read access) — tables, columns, measures, relationships\n- **Use INFO functions** (may need elevated access) for: calculation groups, calculation items, variations, user-defined functions, calendars\n- **Handle permission failures gracefully** — If an elevated INFO function fails but INFO.VIEW.* functions succeeded, assume the user lacks elevated permissions and skip all elevated discoveries entirely\n- **Always narrow results** — Use SELECTCOLUMNS + FILTER to fetch only needed columns and rows\n- **Cache discovered schema mentally** — Don't re-fetch what you've already discovered in this conversation\n\n### Recommended Discovery Order\n\n1. **Scope estimation** — Count tables, columns, measures, relationships\n2. **Tables** — Get table names, identify relevant ones\n3. **Columns** — Get columns for relevant tables only\n4. **Measures** — Get measures (prefer using these over raw aggregations)\n5. **Relationships** — Get relationships between relevant tables\n6. **Advanced metadata** (if needed) — Calculation groups, calendars, UDFs, variations\n\n### Scope Estimation Query\n\n```dax\nEVALUATE\nROW(\n    \"TableCount\", COUNTROWS(INFO.VIEW.TABLES()),\n    \"ColumnCount\", COUNTROWS(INFO.VIEW.COLUMNS()),\n    \"MeasureCount\", COUNTROWS(INFO.VIEW.MEASURES()),\n    \"RelationshipCount\", COUNTROWS(INFO.VIEW.RELATIONSHIPS())\n)\n```\n\n### Metadata Object → INFO Function Map\n\n| Metadata Object | Primary INFO Functions | Access Level |\n|---|---|---|\n| Tables | `INFO.VIEW.TABLES()` | Read |\n| Columns | `INFO.VIEW.COLUMNS()` | Read |\n| Measures | `INFO.VIEW.MEASURES()` | Read |\n| Relationships | `INFO.VIEW.RELATIONSHIPS()` | Read |\n| Model config | `INFO.MODEL()` | May need elevated |\n| Calculation groups | `INFO.CALCULATIONGROUPS()`, `INFO.CALCULATIONITEMS()` | May need elevated |\n| Calendars | `INFO.CALENDARS()`, `INFO.CALENDARCOLUMNGROUPS()`, `INFO.CALENDARCOLUMNREFERENCES()` | May need elevated |\n| User-defined functions | `INFO.USERDEFINEDFUNCTIONS()` | May need elevated |\n| Variations | `INFO.VARIATIONS()` | May need elevated |\n\nFor the full query catalog and output column details, see [discovery-queries.md](.\u002Freferences\u002Fdiscovery-queries.md).\n\n## Running Discovery Queries\n\nUse `npx fabric-app-data query \u003Calias> --query '\u003CDAX>'` to execute INFO queries against a semantic model. For full CLI options (profiles, file input, result limits), see the `fabric-cli` skill.\n\n```bash\nnpx fabric-app-data query \u003Calias> --query \"EVALUATE INFO.VIEW.TABLES()\"\n```\n\n## Troubleshooting\n\n| Problem | Solution |\n|---------|----------|\n| INFO functions return permission errors | Fall back to INFO.VIEW functions; mark elevated access as unavailable for this session |\n| Metadata output too large | Use scope estimation + narrowing patterns from [discovery-queries.md](.\u002Freferences\u002Fdiscovery-queries.md) |\n",{"name":5,"description":7},"main","492bde55156d338649198105a73f7954165568ea","fabric-app\u002F.agents\u002Fskills\u002Fschema-discovery",5,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FValueLens-for-Microsoft-Copilot","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-12 13:01:48",{"source":8,"skillName":5,"commands":54,"prompt":64,"zipAvailable":65},[55,58,61],{"tool":56,"command":57},"npx","npx skills add microsoft\u002FValueLens-for-Microsoft-Copilot --skill schema-discovery",{"tool":59,"command":60},"bunx","bunx skills add microsoft\u002FValueLens-for-Microsoft-Copilot --skill schema-discovery",{"tool":62,"command":63},"pnpm","pnpm dlx skills add microsoft\u002FValueLens-for-Microsoft-Copilot --skill schema-discovery","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FValueLens-for-Microsoft-Copilot\u002Fblob\u002Fmain\u002Ffabric-app\u002F.agents\u002Fskills\u002Fschema-discovery\u002FSKILL.md",true,[67],{"path":68,"resourceType":69,"resourceTypeDesc":70,"sizeBytes":71,"sourceHtmlUrl":72},"references\u002Fdiscovery-queries.md","REFERENCE","参考文档",4211,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FValueLens-for-Microsoft-Copilot\u002Fblob\u002Fmain\u002Ffabric-app\u002F.agents\u002Fskills\u002Fschema-discovery\u002Freferences\u002Fdiscovery-queries.md",[74,99,134,158,180,198],{"skillNo":75,"name":76,"nameZh":6,"description":77,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":78,"categories":79,"keywords":80,"publishedAt":39},"01M69BE0DTF2948C1JH2JZQV3P","visuals","Use when user wants to incorporate charts, graphs, data grid,  or other visual representations of data into their project. 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Build browser-based apps that query semantic models via DAX using the FabricClient API.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FValueLens-for-Microsoft-Copilot\u002Fblob\u002Fmain\u002Ffabric-app\u002F.agents\u002Fskills\u002Ffabric-sdk\u002FSKILL.md",[],[165,166,167,148,9,23,168,169,170,149,123,171,172,173,85,121,174,175,93,176,177,178,179],"sdk","fabric sdk","fabric","connect","fabric data","semantic models","applications","api","how","runtime","app","how to","sources","at","data sources",{"skillNo":181,"name":182,"nameZh":6,"description":183,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":184,"categories":185,"keywords":186,"publishedAt":39},"01M69BDQ6CVBSZA9N5RBZR60J1","fabric-cli","How to use fabric-app-data CLI to manage Fabric data source connections. 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Covers DAX syntax rules, query patterns, time intelligence, and an iterative test workflow using the Fabric CLI query command.","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002FValueLens-for-Microsoft-Copilot\u002Fblob\u002Fmain\u002Ffabric-app\u002F.agents\u002Fskills\u002Fdax-authoring\u002FSKILL.md",[204],{"categoryNo":141,"categoryName":142,"taxonomyType":108,"taxonomyTypeDesc":109},[23,206,207,144,208,147,187,38,167,170,209,210,211,212,34,213,127,121,214,28],"dax authoring","authoring","workflow","Rules","syntax","command","write","models","time"]