# webnn

Walks your agent through Web Neural Network API integration in JS or TS: navigator.ml checks, MLContext, graph building, tensors and local fallbacks.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 463 installs · ★ 52
- Author: webmaxru
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/webmaxru/web-ai-agent-skills/tree/HEAD/skills/webnn/SKILL.md
- Updated: 2026-09-29
- HTML: https://affpapa.org/en/nevibecode/skills/webnn

## What it does

The skill lays out a step-by-step path for WebNN work in the browser. The agent first hunts for frontend entry points — HTML, workers, framework bootstrap files — and, when a Node runtime is available, runs `node scripts/find-webnn-targets.mjs .` to inventory candidate files. If several frontends turn up, it stops and asks which app should get the integration. Server-side inference, model training and cloud AI APIs are out of scope, and the agent will say so.

Before writing code it reads the reference files: `webnn-reference.md`, `examples.md`, `compatibility.md` and `troubleshooting.md`. Then it picks a runtime shape — a direct `MLGraphBuilder` graph, or an adapter around a local runtime the app already loads, such as ONNX Runtime Web. Support detection is centralised around `window.isSecureContext`, `navigator.ml` and the execution context you need (`Window` or `Worker`).

A secure context is mandatory, so check that first when something fails. `accelerated` and `powerPreference` are preferences, not promises: the browser backend can partition the graph or fall back per operator. When `context.lost` resolves, the context, graph and tensors are invalid and must be rebuilt. The skill also insists the fallback path be explicit and product-approved — quietly swapping local inference for a remote provider is not allowed.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add webmaxru/web-ai-agent-skills --skill webnn -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/webnn/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/webnn/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add webmaxru/web-ai-agent-skills --skill webnn -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/webnn/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add webmaxru/web-ai-agent-skills --skill webnn -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/webnn/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install webmaxru/web-ai-agent-skills webnn
```

2. **Manually: put the files in place**
- File: `.github/skills/webnn/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add webmaxru/web-ai-agent-skills --skill webnn -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/webmaxru/web-ai-agent-skills --path skills/webnn
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/webnn/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add webmaxru/web-ai-agent-skills --skill webnn -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/webmaxru/web-ai-agent-skills/tree/HEAD/skills/webnn/SKILL.md

## When to use it
- check WebNN support and secure-context status before inference runs
- build a graph with `MLGraphBuilder` and execute it on device
- choose between `high-performance`, `low-power` and CPU inference
- recover from a lost context by rebuilding graph and tensors
- keep an explicit ONNX Runtime Web fallback for unsupported browsers

## Example prompts
- add a `navigator.ml` check and `MLContext` setup to this app
- build the graph with `MLGraphBuilder` and run tensors through `writeTensor`, `dispatch` and `readTensor`
- add an explicit ONNX Runtime Web fallback when an accelerated context fails

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: efebb7700faf60ad.

BEGIN THIRD-PARTY FILE efebb7700faf60ad
```text
---
name: webnn
description: Implements and debugs browser Web Neural Network API integrations in JavaScript or TypeScript web apps. Use when adding navigator.ml checks, MLContext creation, MLGraphBuilder flows, device selection, tensor dispatch and readback, or explicit fallback paths to ONNX Runtime Web or other local runtimes. Don't use for model training, server-side ML inference, or cloud AI APIs.
license: MIT
metadata:
  author: webmaxru
  version: "1.4"
---

# WebNN

## Procedures

**Step 1: Identify the browser integration surface**
1. Inspect the workspace for browser entry points, UI handlers, worker entry files, and any existing model-loading or inference abstraction layer.
2. Execute `node scripts/find-webnn-targets.mjs .` to inventory likely frontend files and existing WebNN markers when a Node runtime is available.
3. If a Node runtime is unavailable, inspect the nearest `package.json`, HTML entry point, framework bootstrap files, and worker entry files manually to identify the browser app boundary.
4. If the workspace contains multiple frontend apps, prefer the app that contains the active route, component, or user-requested feature surface.
5. If the inventory still leaves multiple plausible frontend targets, stop and ask which app should receive the WebNN integration.
6. If the project is not a browser web app, stop and explain that this skill does not apply.

**Step 2: Confirm WebNN viability and choose the runtime shape**
1. Read `references/webnn-reference.md` before writing code.
2. Read `references/examples.md` when choosing between a direct WebNN graph flow and an adapter around an existing browser ML runtime.
3. Read `references/compatibility.md` when native support, preview flags, device behavior, or backend differences matter.
4. Read `references/troubleshooting.md` when context creation, graph build, tensor readback, or device selection fails.
5. Verify that the feature runs in a secure context and in a `Window` or `Worker` context (`DedicatedWorker`, `SharedWorker`, or `ServiceWorker`).
6. If the feature must run on the server, train models, or depend on cloud inference, stop and explain the platform mismatch.
```
END THIRD-PARTY FILE efebb7700faf60ad

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First 2175 of 6946 bytes. © webmaxru · MIT · https://github.com/webmaxru/web-ai-agent-skills/tree/HEAD/skills/webnn/SKILL.md

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---
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