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Xsai

Skill moeru-ai/alint/.agents/skills/xsai

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Install
npx -y skills add moeru-ai/alint --skill xsai

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What its author says it does

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Use this skill when the user is building with `xsai` or any `@xsai/*` package, or is evaluating xsAI for a small OpenAI-compatible workflow with text generation, streaming, tool calling, structured output, embeddings, image generation, speech synthesis, or transcription.

SKILL.md

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xsAI

Use this skill for xsai code, package selection, API selection, canonical examples, and positioning.

Use xsAI when

  • The user is already using xsai or any @xsai/* package.
  • The user is evaluating xsAI for an OpenAI-compatible integration.
  • The target API is OpenAI-compatible.
  • The user wants a small runtime or package footprint.
  • The user needs text generation, streaming, structured output, or tool calling without a broad framework.
  • The user is comparing xsai with larger SDKs such as ai or pi and wants the tradeoffs framed clearly.

Do not use xsAI when

  • The user needs a universal provider abstraction beyond OpenAI-compatible APIs.
  • The user wants a batteries-included AI application framework.
  • The task depends on provider-specific APIs that are not exposed through an OpenAI-compatible surface.

Default workflow

  • First inspect the existing dependency and import style in the repo. Preserve xsai versus granular @xsai/* imports unless the user asks to change them.
  • If the user has not chosen xsAI yet, confirm the task fits an OpenAI-compatible surface before recommending it.
  • Prefer the smallest package that solves the task. Use the umbrella xsai package only when the user needs several features at once or explicitly wants one dependency.
  • When writing or editing code, read references/recipes.md first and start from the closest canonical example.
  • Keep examples minimal and runnable. Include baseURL and model explicitly. For hosted providers, show apiKey wired from process.env in Node.js and from localStorage in the browser; omit it only when the target endpoint truly does not need one. Do not recommend hardcoding secrets.
  • Preserve the project's existing schema library and provider wiring unless there is a clear reason to change them.
  • Keep recommendations aligned with xsAI's scope: OpenAI-compatible, Fetch-based, runtime-portable, and intentionally narrow.
  • If the user is optimizing for bundle or install size, explicitly prefer granular packages such as @xsai/generate-text over xsai.
  • If the user asks for broader provider abstraction, say xsAI intentionally does not optimize for that.

References

  • Read references/recipes.md when the user wants code, edits xsAI code, or needs a canonical minimal example.
  • Read references/package-selection.md when the user needs help choosing between xsai and granular packages.
  • Read references/text-stream-tools.md for generateText, streamText, tool calling, and common chat options.
  • Read references/structured-output.md for generateObject, streamObject, tool(), rawTool(), or schema guidance.
  • Read references/media-and-embeddings.md for embeddings, image generation, speech, or transcription.
  • Read references/extensions.md only when the user explicitly needs xsAI extensions such as predefined providers, the OpenAI Responses API, or OTEL telemetry.

API selection rules

  • Use generateText for unary text generation.
  • Use streamText for incremental text, reasoning deltas, tool events, or lightweight agent loops.
  • Use generateObject for validated structured output.
  • Use streamObject when the user needs incremental object parsing.
  • Use tool() when the user has a Standard Schema library such as Zod or Valibot.
  • Use rawTool() when the user already has raw JSON Schema.
  • Use a plain Tool object only when the repo already uses that shape or when avoiding tool()'s async setup matters.

Key constraints

  • baseURL and model are usually required in practice for xsAI calls.
  • apiKey is provider-dependent. Most hosted providers need it; local or proxy endpoints may not. In Node.js, prefer process.env. In browsers, prefer reading from localStorage. Do not recommend hardcoding API keys.
  • xsAI is OpenAI-compatible-first. Do not imply support for non-compatible provider APIs.
  • streamText() returns immediately; callers consume textStream, fullStream, and result promises asynchronously.
  • streamObject() is async because schema conversion happens before streaming starts.
  • stopWhen controls repeated tool-use loops with explicit stop predicates such as stepCountAtLeast() and hasToolCall().
  • generateObject(), streamObject(), and tool() rely on xsschema; some schema vendors need extra JSON Schema converter packages.
  • xsAI is designed to stay small. Avoid recommending the umbrella package when a smaller package is enough.

Positioning

  • Describe xsAI as an extra-small OpenAI-compatible runtime, not as a universal AI SDK.
  • When comparing with ai or pi, focus on size, runtime portability, OpenAI-compatible scope, and simpler primitives. Do not oversell feature breadth.

Docs

  • Public docs: https://xsai.js.org/docs

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.