yaml

A YAML-based data format for json-render, a system that describes user interfaces as data. It reads YAML as it arrives and turns it into interface updates or edits.

In plain words
What is it for?
Use it when working with @json-render/yaml, streaming YAML specifications, YAML edit or patch blocks, or prompts that ask an AI to generate YAML.
Why use it?
It lets an AI or other stream send readable YAML instead of JSON lines while the interface updates progressively. It also supports several ways to apply changes to existing data.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/vercel-labs/json-render/yaml
Any agent
npx skills add vercel-labs/json-render --skill yaml
Clone the repo
git clone --depth 1 https://github.com/vercel-labs/json-render

Made for: Claude Code, Codex.

Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,128 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00048 $0.01128
Opus 5 $0.00024 $0.00564
Sonnet 5 $0.00010 $0.00226
Haiku 4.5 $0.00005 $0.00113

Measured 2d ago against content hash 510a4898a18d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

yaml scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/yaml/SKILL.md · 142 lines

How it starts

The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.

@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

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:

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:

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

Read the full file on GitHub · 142 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 142 lines · 48 tokens per session scan A 510a4898a18d

Subscribe to this mod's changes

yaml is a skill published in the GitHub repository vercel-labs/json-render (16,056 stars, last pushed 4d ago), licensed Apache-2.0. It adds 48 tokens to every session and 1,128 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Related

Other skills, from other repositories

systematic-debugging

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.

obra/superpowers · 21 tokens

brainstorming

You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.

obra/superpowers · 37 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

agent-host-chat-contributions

Build and review cross-cutting agent-host chat behavior through lifecycle contributions. Use when adding turn lifecycle side effects, prompt or context injection, restored-history transformation, protocol-action observation, or when reviewing changes that add code to AgentSideEffects or AgentService.

microsoft/vscode · 56 tokens

auto-perf-optimize

Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.

microsoft/vscode · 62 tokens