dv-metadata Skill
Dataverse schema authoring and inspection — tables, columns, relationships, forms, and views. Use when the user wants to define, evolve, or inspect the data model — add a column, create a table, set up a lookup, customize a form, build a view, or list existing columns and relationships. Published by microsoft in Dataverse-skills.
Decision snapshot
Is this a fit?
Data analysis, Includes SKILL.md, Reusable instructions
Compatibility not yet detected.
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Copy skill directory
2 months ago · MIT license
No specific cautions were detected. Review the source and requested permissions before installing.
What is dv-metadata Skill?
Dataverse schema authoring and inspection — tables, columns, relationships, forms, and views. Use when the user wants to define, evolve, or inspect the data model — add a column, create a table, set up a lookup, customize a form, build a view, or list existing columns and relationships. Published by microsoft in Dataverse-skills. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Data analysis
- Data analysis use cases
Declared skill metadata
- Source file: .github/plugins/dataverse/skills/dv-metadata/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use dv-metadata Skill
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/Dataverse-skills
- Skill file: .github/plugins/dataverse/skills/dv-metadata/SKILL.md
What it does
Dataverse schema authoring and inspection — tables, columns, relationships, forms, and views. Use when the user wants to define, evolve, or inspect the data model — add a column, create a table, set up a lookup, customize a form, build a view, or list existing columns and relationships.
Skill instructions
Skill: Metadata — Making Changes Before the first metadata change in a session: 1. Confirm the target environment with the user — see the Multi-Environment Rule in dv-overview. 2. Confirm the solution — ask "What solution should these components go into?" If SOLUTIONNAME is in .env, confirm it. If no solution exists yet, you MUST ask the user for the solution name and publisher prefix before creating anything. The publisher prefix is permanent — it cannot be changed after components are created with it. STOP and ask the user: "What solution name and publisher prefix should I use? The prefix (e.g., contoso, lit, soc) is permanent on every table and column." Then query existing publishers and show them — the user may want to reuse one: python Publisher discovery + solution creation — use SDK (never raw Web API). See dv-solution for the full publisher discovery flow. publishers = client.records.list("publisher", filter="customizationprefix ne 'none' and uniquename ne 'MicrosoftCorporation
Verified compatibility and discovery
Frequently asked questions
What is dv-metadata?
dv-metadata is a open-source AI agent skill with Copy skill directory. Dataverse schema authoring and inspection — tables, columns, relationships, forms, and views.
Who is dv-metadata best for?
dv-metadata is best for reusing agent instructions, scripts, and references, data analysis workflows.
How do I install dv-metadata?
Install or run dv-metadata using Copy skill directory. Check dv-metadata for the latest setup command.
Is dv-metadata actively maintained?
dv-metadata may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
Maintain this resource?
Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
