@nifrajs/content

Typed, schema-validated content collections for nifra - Markdown + frontmatter, framework-agnostic.

Part of the nifra full-stack TypeScript framework - one core, five UI libraries, every runtime. Scaffold a new app with bun create nifra.

Install

bun add @nifrajs/content

Docs

• Reference: https://nifra.dev/docs
• AI-readable: https://nifra.dev/llms.txt

Typed indexes and joins

Build an equality lookup and deterministic query index from a baked collection. where is field equality, range supports typed string/number comparisons, sorting is stable (slug is the tie-breaker), and pages use an opaque cursor rather than an offset:

const posts = indexCollection(await bakeCollection(blog), {
  by: "section",
  sort: { field: "publishedAt", dir: "desc" },
})

const page = posts.query({ where: { section: "news" }, limit: 20 })
const next = page.nextCursor === undefined
  ? undefined
  : posts.query({ where: { section: "news" }, cursor: page.nextCursor, limit: 20 })

posts.baked is JSON-safe and can be serialized into an edge bundle; fromBakedIndex rehydrates it without filesystem access. joinCollections(left, right, "id") gives one-to-many rows by default. Pass { cardinality: "one-to-one" } to require unique right-side keys; duplicate right keys fail the build loudly. The join key must exist on both frontmatter types with the same TypeScript value type. These APIs index only validated local/baked content; credentialed CMS ingestion and tenant data remain outside this public package.

MIT

For AI agents

Start with LLM.md - this package's contract card (the exports you call + its footguns), one cheap read instead of the whole corpus. For the wider framework: the repo's AGENTS.md is the copy-paste quick reference, and llms-full.txt is the full machine-readable corpus. Run nifra check as the done-gate, or nifra mcp to give the agent live project tools.