Dogfood logo

Dogfood

OrganizationPopular
vercel-labs
dogfood

Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

Overview

Publishervercel-labs
Repositoryagent-browser
Skill namedogfood
Stars
42.8K
Forks
2.9K
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Dogfood 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/agent-browser.git /tmp/agent-browser
mkdir -p .claude/skills
cp -r /tmp/agent-browser/skill-data/dogfood .claude/skills/dogfood
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dogfood 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 Dogfood 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 Dogfood 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.

Dogfood

Systematically explore a web application, find issues, and produce a report with full reproduction evidence for every finding.

Setup

Only the Target URL is required. Everything else has sensible defaults -- use them unless the user explicitly provides an override.

ParameterDefaultExample override
Target URL(required)vercel.com, http://localhost:3000
Session nameSlugified domain (e.g., vercel.com -> vercel-com)--session my-session
Output directory./dogfood-output/Output directory: /tmp/qa
ScopeFull appFocus on the billing page
AuthenticationNoneSign in to user@example.com

If the user says something like "dogfood vercel.com", start immediately with defaults. Do not ask clarifying questions unless authentication is mentioned but credentials are missing.

Always use agent-browser directly -- never npx agent-browser. The direct binary uses the fast Rust client. npx routes through Node.js and is significantly slower.

Workflow

1. Initialize    Set up session, output dirs, report file
2. Authenticate  Sign in if needed, save state
3. Orient        Navigate to starting point, take initial snapshot
4. Explore       Systematically visit pages and test features
5. Document      Screenshot + record each issue as found
6. Wrap up       Update summary counts, close session

1. Initialize

bash
mkdir -p {OUTPUT_DIR}/screenshots {OUTPUT_DIR}/videos

Copy the report template into the output directory and fill in the header fields:

bash
cp {SKILL_DIR}/templates/dogfood-report-template.md {OUTPUT_DIR}/report.md

Start a named session:

bash
agent-browser --session {SESSION} open {TARGET_URL}
agent-browser --session {SESSION} wait --load domcontentloaded

2. Authenticate

If the app requires login:

bash
agent-browser --session {SESSION} snapshot -i
# Identify login form refs, fill credentials
agent-browser --session {SESSION} fill @e1 "{EMAIL}"
agent-browser --session {SESSION} fill @e2 "{PASSWORD}"
agent-browser --session {SESSION} click @e3
# Replace this with the target app's post-login URL, text, or JS condition:
agent-browser --session {SESSION} wait --url "{POST_LOGIN_URL_PATTERN}"
# Or:
# agent-browser --session {SESSION} wait --text "{POST_LOGIN_TEXT}"
# agent-browser --session {SESSION} wait --fn "{POST_LOGIN_CONDITION}"

For OTP/email codes: ask the user, wait for their response, then enter the code.

After successful login, save state for potential reuse:

bash
agent-browser --session {SESSION} state save {OUTPUT_DIR}/auth-state.json

3. Orient

Take an initial annotated screenshot and snapshot to understand the app structure:

bash
agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/initial.png
agent-browser --session {SESSION} snapshot -i

Identify the main navigation elements and map out the sections to visit.

4. Explore

Read references/issue-taxonomy.md for the full list of what to look for and the exploration checklist.

Strategy -- work through the app systematically:

  • Start from the main navigation. Visit each top-level section.
  • Within each section, test interactive elements: click buttons, fill forms, open dropdowns/modals.
  • Check edge cases: empty states, error handling, boundary inputs.
  • Try realistic end-to-end workflows (create, edit, delete flows).
  • Check the browser console for errors periodically.

At each page:

bash
agent-browser --session {SESSION} snapshot -i
agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/{page-name}.png
agent-browser --session {SESSION} errors
agent-browser --session {SESSION} console

Use your judgment on how deep to go. Spend more time on core features and less on peripheral pages. If you find a cluster of issues in one area, investigate deeper.

5. Document Issues (Repro-First)

Steps 4 and 5 happen together -- explore and document in a single pass. When you find an issue, stop exploring and document it immediately before moving on. Do not explore the whole app first and document later.

Every issue must be reproducible. When you find something wrong, do not just note it -- prove it with evidence. The goal is that someone reading the report can see exactly what happened and replay it.

Choose the right level of evidence for the issue:

Interactive / behavioral issues (functional, ux, console errors on action)

These require user interaction to reproduce -- use full repro with video and step-by-step screenshots:

  1. Start a repro video before reproducing:
bash
agent-browser --session {SESSION} record start {OUTPUT_DIR}/videos/issue-{NNN}-repro.webm
  1. Walk through the steps at human pace. Pause 1-2 seconds between actions so the video is watchable. Take a screenshot at each step:
bash
agent-browser --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-step-1.png
sleep 1
# Perform action (click, fill, etc.)
sleep 1
agent-browser --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-step-2.png
sleep 1
# ...continue until the issue manifests
  1. Capture the broken state. Pause so the viewer can see it, then take an annotated screenshot:
bash
sleep 2
agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/issue-{NNN}-result.png
  1. Stop the video:
bash
agent-browser --session {SESSION} record stop
  1. Write numbered repro steps in the report, each referencing its screenshot.
Static / visible-on-load issues (typos, placeholder text, clipped text, misalignment, console errors on load)

These are visible without interaction -- a single annotated screenshot is sufficient. No video, no multi-step repro:

bash
agent-browser --session {SESSION} screenshot --annotate {OUTPUT_DIR}/screenshots/issue-{NNN}.png

Write a brief description and reference the screenshot in the report. Set Repro Video to N/A.


For all issues:

  1. Append to the report immediately. Do not batch issues for later. Write each one as you find it so nothing is lost if the session is interrupted.

  2. Increment the issue counter (ISSUE-001, ISSUE-002, ...).

6. Wrap Up

Aim to find 5-10 well-documented issues, then wrap up. Depth of evidence matters more than total count -- 5 issues with full repro beats 20 with vague descriptions.

After exploring:

  1. Re-read the report and update the summary severity counts so they match the actual issues. Every ### ISSUE- block must be reflected in the totals.
  2. Close the session:
bash
agent-browser --session {SESSION} close
  1. Tell the user the report is ready and summarize findings: total issues, breakdown by severity, and the most critical items.

Guidance

  • Repro is everything. Every issue needs proof -- but match the evidence to the issue. Interactive bugs need video and step-by-step screenshots. Static bugs (typos, placeholder text, visual glitches visible on load) only need a single annotated screenshot.
  • Verify reproducibility before collecting evidence. Before recording video or taking screenshots, verify the issue is reproducible with at least one retry. If it can't be reproduced consistently, it's not a valid issue.
  • Don't record video for static issues. A typo or clipped text doesn't benefit from a video. Save video for issues that involve user interaction, timing, or state changes.
  • For interactive issues, screenshot each step. Capture the before, the action, and the after -- so someone can see the full sequence.
  • Write repro steps that map to screenshots. Each numbered step in the report should reference its corresponding screenshot. A reader should be able to follow the steps visually without touching a browser.
  • Use the right snapshot command.
    • snapshot -i — for finding clickable/fillable elements (buttons, inputs, links)
    • snapshot (no flag) — for reading page content (text, headings, data lists)
  • Be thorough but use judgment. You are not following a test script -- you are exploring like a real user would. If something feels off, investigate.
  • Write findings incrementally. Append each issue to the report as you discover it. If the session is interrupted, findings are preserved. Never batch all issues for the end.
  • Never delete output files. Do not rm screenshots, videos, or the report mid-session. Do not close the session and restart. Work forward, not backward.
  • Never read the target app's source code. You are testing as a user, not auditing code. Do not read HTML, JS, or config files of the app under test. All findings must come from what you observe in the browser.
  • Check the console. Many issues are invisible in the UI but show up as JS errors or failed requests.
  • Test like a user, not a robot. Try common workflows end-to-end. Click things a real user would click. Enter realistic data.
  • Type like a human. When filling form fields during video recording, use type instead of fill -- it types character-by-character. Use fill only outside of video recording when speed matters.
  • Pace repro videos for humans. Add sleep 1 between actions and sleep 2 before the final result screenshot. Videos should be watchable at 1x speed -- a human reviewing the report needs to see what happened, not a blur of instant state changes.
  • Be efficient with commands. Batch multiple agent-browser commands in a single shell call when they are independent (e.g., agent-browser ... screenshot ... && agent-browser ... console). Use agent-browser --session {SESSION} scroll down 300 for scrolling -- do not use key or evaluate to scroll.

References

ReferenceWhen to Read
references/issue-taxonomy.mdStart of session -- calibrate what to look for, severity levels, exploration checklist

Templates

TemplatePurpose
templates/dogfood-report-template.mdCopy into output directory as the report file

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Dogfood AI skill do?

Systematically explore and test a web application to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", "test this app/site/platform", or review the quality of a web application. Produces a structured report with full reproduction evidence -- step-by-step screenshots, repro videos, and detailed repro steps for every issue -- so findings can be handed directly to the responsible teams.

Why use Dogfood on TypingMind?

Because you install it once and use it with any model. Dogfood 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 Dogfood in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vercel-labs/agent-browser/tree/main/skill-data/dogfood. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Dogfood?

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 Dogfood?

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

Is the Dogfood 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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