Evolve GBrain schema packs by adding, updating, and validating page types.
npx skills add https://github.com/weiping/gbrain-cn --skill schema-author
Please help me install this Agent Skill. Skill: schema-author Source: https://github.com/weiping/gbrain-cn/tree/main/skills/schema-author Command: npx skills add https://github.com/weiping/gbrain-cn --skill schema-author
schema-author helps you fix and improve your brain’s schema pack so previously invisible content (untyped pages) becomes correctly typed, expert-routable, and queryable with consistent prefixes, link verbs, and extraction settings.
pages.type across matching pages, producing measurable coverage improvements while keeping mutations idempotent and validated.Tell the agent: "Propose schema types for my corpus, then fork my active bundled pack if needed, add the best new types, sync to backfill existing pages, and verify coverage."
High-intent search queries and answers about installing and using this skill.
To add new page types to a knowledge graph schema, you define primitives, prefixes, aliases, and expert routing flags. This ensures previously untyped content becomes correctly typed and queryable with consistent link verbs.
To fix untyped or orphan pages in your metadata corpus, you audit schema pack health to detect coverage gaps and dead prefixes. This flags content needing alignment before applying validated, idempotent mutations.
Schema pack forking creates a separate branch to safely evolve schema structures without breaking existing queries. You need it when introducing new domain-specific page categories or updating extraction settings.
Yes, you can backfill existing pages during a schema update by running a dry-run sync. This process updates page types across matching pages idempotently, producing measurable coverage improvements without data loss.
You propose new schema types by analyzing ingested page patterns within your corpus. The system generates corpus-driven candidate types, allowing you to refine decisions and verify coverage before applying them.
Yes, schema evolution supports YAML-frontmatter-driven skill discovery. This mechanism allows agents to detect missing types, mismatched schema coverage, or the need for new domain-specific categories for expert routing.
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