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Yaml

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vercel-labs
yaml

YAML wire format for json-render with streaming parser, prompt generation, and AI SDK transform. Use when working with @json-render/yaml, YAML-based spec streaming, yaml-spec/yaml-edit fences, or YAML prompt generation.

Overview

Publishervercel-labs
Repositoryjson-render
Skill nameyaml
Stars
16.5K
Forks
887
Bundled files
Instructions only
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vercel-labs on GitHub. Read the source before you install it.

Installation

Install the Yaml AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vercel-labs/json-render.git /tmp/json-render
mkdir -p .claude/skills
cp -r /tmp/json-render/skills/yaml .claude/skills/yaml
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Yaml in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Yaml on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Yaml is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

@json-render/yaml

YAML wire format for @json-render/core. Progressive rendering and surgical edits via streaming YAML.

Key Concepts

  • YAML wire format: Alternative to JSONL that uses code fences (yaml-spec, yaml-edit, yaml-patch, diff)
  • Streaming parser: Incrementally parses YAML, emits JSON Patch operations via diffing
  • Edit modes: Patch (RFC 6902), merge (RFC 7396), and unified diff
  • AI SDK transform: TransformStream that converts YAML fences into json-render patches

Generating YAML Prompts

typescript
import { yamlPrompt } from "@json-render/yaml";
import { catalog } from "./catalog";

// Standalone mode (LLM outputs only YAML)
const systemPrompt = yamlPrompt(catalog, {
  mode: "standalone",
  editModes: ["merge"],
  customRules: ["Always use dark theme"],
});

// Inline mode (LLM responds conversationally, wraps YAML in fences)
const chatPrompt = yamlPrompt(catalog, { mode: "inline" });

Options:

  • system (string) — Custom system message intro
  • mode ("standalone" | "inline") — Output mode, default "standalone"
  • customRules (string[]) — Additional rules appended to prompt
  • editModes (EditMode[]) — Edit modes to document, default ["merge"]

AI SDK Transform

Use pipeYamlRender as a drop-in replacement for pipeJsonRender:

typescript
import { pipeYamlRender } from "@json-render/yaml";
import { createUIMessageStream, createUIMessageStreamResponse } from "ai";

const stream = createUIMessageStream({
  execute: async ({ writer }) => {
    writer.merge(pipeYamlRender(result.toUIMessageStream()));
  },
});
return createUIMessageStreamResponse({ stream });

For multi-turn edits, pass the previous spec:

typescript
pipeYamlRender(result.toUIMessageStream(), {
  previousSpec: currentSpec,
});

The transform recognizes four fence types:

  • yaml-spec — Full spec, parsed progressively line-by-line
  • yaml-edit — Partial YAML deep-merged with current spec (RFC 7396)
  • yaml-patch — RFC 6902 JSON Patch lines
  • diff — Unified diff applied to serialized spec

Streaming Parser (Low-Level)

typescript
import { createYamlStreamCompiler } from "@json-render/yaml";

const compiler = createYamlStreamCompiler<Spec>();

// Feed chunks as they arrive from any source
const { result, newPatches } = compiler.push("root: main\n");
compiler.push("elements:\n  main:\n    type: Card\n");

// Flush remaining data at end of stream
const { result: final } = compiler.flush();

// Reset for next stream (optionally with initial state)
compiler.reset({ root: "main", elements: {} });

Methods: push(chunk), flush(), getResult(), getPatches(), reset(initial?)

Edit Modes (from @json-render/core)

The YAML package uses the universal edit mode system from core:

typescript
import { buildEditInstructions, buildEditUserPrompt } from "@json-render/core";
import type { EditMode } from "@json-render/core";

// Generate edit instructions for YAML format
const instructions = buildEditInstructions({ modes: ["merge", "patch"] }, "yaml");

// Build user prompt with current spec context
const userPrompt = buildEditUserPrompt({
  prompt: "Change the title to Dashboard",
  currentSpec: spec,
  config: { modes: ["merge"] },
  format: "yaml",
  serializer: (s) => yamlStringify(s, { indent: 2 }).trimEnd(),
});

Fence Constants

For custom parsing, use the exported constants:

typescript
import {
  YAML_SPEC_FENCE,   // "```yaml-spec"
  YAML_EDIT_FENCE,   // "```yaml-edit"
  YAML_PATCH_FENCE,  // "```yaml-patch"
  DIFF_FENCE,        // "```diff"
  FENCE_CLOSE,       // "```"
} from "@json-render/yaml";

Key Exports

ExportDescription
yamlPromptGenerate YAML system prompt from catalog
createYamlTransformAI SDK TransformStream for YAML fences
pipeYamlRenderConvenience pipe wrapper (replaces pipeJsonRender)
createYamlStreamCompilerStreaming YAML parser with patch emission
YAML_SPEC_FENCEFence constant for yaml-spec
YAML_EDIT_FENCEFence constant for yaml-edit
YAML_PATCH_FENCEFence constant for yaml-patch
DIFF_FENCEFence constant for diff
FENCE_CLOSEFence close constant
diffToPatchesRe-export: object diff to JSON Patch
deepMergeSpecRe-export: RFC 7396 deep merge

Frequently asked questions

What does the Yaml AI skill do?

YAML wire format for json-render with streaming parser, prompt generation, and AI SDK transform. Use when working with @json-render/yaml, YAML-based spec streaming, yaml-spec/yaml-edit fences, or YAML prompt generation.

Why use Yaml on TypingMind?

Because you install it once and use it with any model. Yaml is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Yaml in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel-labs/json-render/tree/main/skills/yaml. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Yaml?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Yaml?

As many as you like. As long as a model supports skills, you can use Yaml with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Yaml AI skill free?

Yes. It is published on GitHub by vercel-labs under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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