Enforce Zod schema authoring with strictObject, safeExtend, and meta() for IPFS data validation.
npx skills add https://github.com/carrot-foundation/schemas --skill rule-zod
Please help me install this Agent Skill. Skill: rule-zod Source: https://github.com/carrot-foundation/schemas/tree/main/.agents/skills/rule-zod Command: npx skills add https://github.com/carrot-foundation/schemas --skill rule-zod
Zod schema authoring in large projects often suffers from inconsistent shapes, optional fields being misused, and missing standardization for metadata. This Skill defines a disciplined pattern using z.strictObject(), .safeExtend(), and .meta() to create predictable, well-documented schema layers that map to IPFS/NFT data models.
Define a BaseIpfsSchema and extend it with NftIpfsSchema and MassIDNftIpfsSchema using z.strictObject(), .safeExtend(), and .meta() on each field.
High-intent search queries and answers about installing and using this skill.
Strict Zod schemas enforce IPFS data validation by using z.strictObject() to reject unknown properties at parse time. This approach ensures data integrity by preventing unexpected fields from passing validation in TypeScript models.
The best way to compose layered Zod schemas for NFT data models is using .safeExtend() instead of in-place merges. This preserves strictness while allowing progressive extension from a BaseIpfsSchema to specific schemas like MassIDNftIpfsSchema.
Document Zod schema fields by applying .meta() on every field. This enforces field-level documentation to support auto-generated docs and UX, ensuring consistent metadata across all NFT and IPFS data attributes.
Using .merge() can break strict object validation because it may lose the strictness enforced by z.strictObject(). Prefer .safeExtend() over .merge() to maintain consistent rejection of unknown properties during schema composition.
This Zod validation pattern applies to src/**/*.schema.ts and src/**/*.schemas.ts files. It guides the development of layered, type-safe schemas across NFT and IPFS models to maintain consistent data validation standards.
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