Official agent skill

Dogfood Exploratory QA

by vercel-labs in vercel-labs/agent-browser

Explores a web app with the agent-browser CLI to find bugs and UX problems, then writes a report with screenshots, repro videos and step-by-step reproduction for each issue.

OfficialApache-2.0Auto-check passedTesting & QA

Install Dogfood Exploratory QA

skills CLI
$ npx skills add vercel-labs/agent-browser --skill dogfood -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install vercel-labs/agent-browser dogfood --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/vercel-labs/agent-browser.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skill-data/dogfood .claude/skills/dogfood && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
dogfood
GitHub stars
44k
Used in
8 other repos
Token cost
~2.7k tokens
SKILL.md length
1,114 words
Files
3 (incl. references)
Skills in repo
10
Repo updated
First seen
Licence
Apache-2.0

At a glance

Explores a web app with the agent-browser CLI to find bugs and UX problems, then writes a report with screenshots, repro videos and step-by-step reproduction for each issue.

  • Works in 6 steps: Initialize → Authenticate → Orient → …
  • Running an exploratory QA pass over a web app before release
  • SKILL.md covers Setup, Workflow, Guidance and References, plus 1 more section
  • Calls npx

What it does

You give the agent a target URL, the only required input; the session name, output directory, scope and authentication all have defaults. It sets up an output folder with screenshot and video subfolders and a report copied from a template, starts a named agent-browser session, signs in if the app needs it and saves the auth state, then takes an annotated screenshot and a snapshot to map the app's main sections before visiting them.

The skill insists on calling the agent-browser binary directly, because the direct binary uses a fast Rust client while going through npx is noticeably slower, and it allows Bash calls to agent-browser for that reason. When login needs a one-time code sent by email, the agent asks you and waits for your reply. Findings go into a structured report that carries full reproduction evidence for every issue, and an issue-taxonomy reference file defines how problems are categorized.

When your agent uses it

  • Running an exploratory QA pass over a web app before release
  • Hunting for bugs on a site and getting reproducible evidence
  • Checking one area, such as a billing page, for UX problems
  • Handing findings to the responsible teams with screenshots and videos

Example prompts

  • “Dogfood http://localhost:3000 and write up every issue you find.”
  • “QA the billing page on our staging site and record a repro video for each bug.”
  • “Do an exploratory test of the checkout flow on staging.example.com and save the report to ./qa-output.”

Requirements

  • The agent-browser CLI
  • A target URL the agent can open
  • Pre-approved tools (allowed-tools): Bash(agent-browser:*), Bash(npx agent-browser:*)

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Initialize
  2. Authenticate
  3. Orient
  4. Explore
  5. Document Issues (Repro-First)
  6. Wrap Up

What it can do on your machine

Read from SKILL.md and the folder at commit d957091. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Bash(agent-browser:*)
    • Bash(npx agent-browser:*)

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • npx

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use npx, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Dogfood Exploratory QA loads about 2.7k tokens when it runs, and up to ~3.6k if it reads all its reference files. Until then it costs about 117 tokens; SKILL.md has 1,114 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~117
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~3.6k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from vercel-labs/agent-browser at commit d957091, republished under its Apache-2.0 licence (© vercel-labs). 1,114 words, ~2,711 tokens.

Download SKILL.mdSave it as .claude/skills/dogfood/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
dogfood
description
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.
allowed-tools
Bash(agent-browser:*), Bash(npx agent-browser:*)

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.
Show full SKILL.md (473 more words)Show less

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

© vercel-labs, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in skill-data/dogfood of vercel-labs/agent-browser.

  • SKILL.md
  • references/issue-taxonomy.md
  • templates/dogfood-report-template.md

Open the folder on GitHubat commit d957091

Used in 8 other repositories

We found 9 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 8 other GitHub owners. This page covers the copy in vercel-labs/agent-browser, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Dogfood Exploratory QA next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Dogfood Exploratory QA compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Dogfood Exploratory QA this skillvercel-labs/agent-browser44k8 repos~2.7kAutomated safety check: PassApache-2.0
Aside Browser Drivergarrytan/gstack136k—~8.5kAutomated safety check: NotesMIT
Hands On Testktnyt/cclsp675—~1.7kAutomated safety check: PassMIT
Diff-Driven Smoke TestsSkyvern-AI/skyvern23k—~5.2kAutomated safety check: PassAGPL-3.0
Dogfoodredf0x1/camofox-browser412—~1.2kAutomated safety check: PassMIT
Whole-App Health Sweepreticlehq/reticle1.2k—~1.1kAutomated safety check: PassApache-2.0

Similar skills

  • Aside Browser Driver

    garrytan/gstack

    Drives a real browser through Aside so the agent can open a page, read it, click through a flow, take screenshots and check console errors.

    136k GitHub stars~8.5k tokensUpdated today
    Productivity & AutomationAuto-check: notes
  • Hands On Test

    ktnyt/cclsp

    Performs manual hands-on testing of a web application using playwright-cli.

    675 GitHub stars~1.7k tokensUpdated 7 mo ago
    Testing & QAAuto-check passed
  • Diff-Driven Smoke Tests

    Skyvern-AI/skyvern

    Reads your git diff, writes a handful of happy-path browser smoke tests, runs them with Skyvern or Chrome DevTools MCP and posts screenshot evidence to the PR.

    23k GitHub stars~5.2k tokensUpdated yesterday
    Testing & QAAuto-check passed
  • Dogfood

    redf0x1/camofox-browser

    QA testing workflow for CamoFox Browser — systematic testing with console capture, error detection, and Playwright tracing.

    412 GitHub stars~1.2k tokensUpdated 19 days ago
    Testing & QAAuto-check passed
  • Whole-App Health Sweep

    reticlehq/reticle

    Sweeps a running web app by clicking every reachable control, then reports dead buttons, console errors, failed requests and mismatches between API data and the screen.

    1.2k GitHub stars~1.1k tokensUpdated yesterday
    Testing & QAAuto-check passed
  • Diff-Driven QA

    Skyvern-AI/skyvern

    Reads your git diff, decides whether the change needs browser QA, API checks or repo tests, runs that validation and reports pass or fail with evidence.

    23k GitHub stars~4.7k tokensUpdated yesterday
    Testing & QAAuto-check: warnings

More from vercel-labs/agent-browser

All 10 skills in this repo
  • Agent Browser CLI

    vercel-labs/agent-browser

    Official

    Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking…

    44k GitHub starsUsed in 24 repos~864 tokens
    Auto-check passed
  • Electron App Automation

    vercel-labs/agent-browser

    Official

    Automates Electron desktop apps such as VS Code, Slack or Discord by connecting agent-browser to their Chrome DevTools Protocol port.

    44k GitHub starsUsed in 5 repos~1.7k tokens
    Auto-check passed
  • Slack Browser Automation

    vercel-labs/agent-browser

    Official

    Drives the Slack web app with the agent-browser CLI to check unread channels, search, read channel details and extract information, with screenshots as evidence.

    44k GitHub starsUsed in 1 repo~2.1k tokens
    Auto-check passed
  • Core Guide for agent-browser

    vercel-labs/agent-browser

    Official

    Core usage guide for the agent-browser CLI: the snapshot-and-ref workflow for navigating, clicking, filling forms, extracting data and running parallel sessions.

    44k GitHub starsUsed in 2 repos~9.5k tokens
    Auto-check passed
  • Official

    Records a site's browser traffic into a HAR file, then builds a standalone client or CLI that calls its internal endpoints directly with no browser.

    44k GitHub starsUsed in 1 repo~1.3k tokens
    Auto-check passed
  • WebMCP Tool Generator

    vercel-labs/agent-browser

    Official

    Builds and validates experimental WebMCP tools that expose a web page's real workflows to agents, with a manifest, init script and evals compared against accessibility-tree automation.

    44k GitHub starsUsed in 1 repo~752 tokens
    Auto-check passed

Questions about Dogfood Exploratory QA

What does Dogfood Exploratory QA do?

Explores a web app with the agent-browser CLI to find bugs and UX problems, then writes a report with screenshots, repro videos and step-by-step reproduction for each issue. You give the agent a target URL, the only required input; the session name, output directory, scope and authentication all have defaults. It sets up an output folder with screenshot and video subfolders and a report copied from a template, starts a named agent-browser session, signs in if the app needs it and saves the auth state, then takes an annotated screenshot and a snapshot to map the app's main sections before visiting them.

When should I use Dogfood Exploratory QA?

Dogfood Exploratory QA fits situations like: running an exploratory QA pass over a web app before release; hunting for bugs on a site and getting reproducible evidence; checking one area, such as a billing page, for UX problems; handing findings to the responsible teams with screenshots and videos.

How do I install Dogfood Exploratory QA in Claude Code?

Run `npx skills add vercel-labs/agent-browser --skill dogfood -a claude-code`. Or copy the skill folder (skill-data/dogfood in vercel-labs/agent-browser) into .claude/skills/dogfood in your project. Claude Code loads it when a task matches its description.

How do I install Dogfood Exploratory QA in Codex?

Run `npx skills add vercel-labs/agent-browser --skill dogfood -a codex`. Or copy the skill folder (skill-data/dogfood in vercel-labs/agent-browser) into .agents/skills/dogfood in your project. Codex loads it when a task matches its description.

Can I use Dogfood Exploratory QA in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add vercel-labs/agent-browser --skill dogfood -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/dogfood, .gemini/skills/dogfood, .github/skills/dogfood and .opencode/skills/dogfood in your project.

What does Dogfood Exploratory QA need to run?

Going by SKILL.md and its folder, Dogfood Exploratory QA needs the command-line tools its instructions call (npx). Our summary lists: The agent-browser CLI; A target URL the agent can open. Its frontmatter pre-approves these tools: Bash(agent-browser:*), Bash(npx agent-browser:*).

Does Dogfood Exploratory QA access the network?

SKILL.md contains no URLs. Its commands use npx, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Dogfood Exploratory QA safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Dogfood Exploratory QA use?

Dogfood Exploratory QA is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Dogfood Exploratory QA use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 907 tokens, read only when the agent opens those files.

What are the alternatives to Dogfood Exploratory QA?

Skills that share tags, products or a category with Dogfood Exploratory QA: Aside Browser Driver (garrytan/gstack, 136k stars), Hands On Test (ktnyt/cclsp, 675 stars), Diff-Driven Smoke Tests (Skyvern-AI/skyvern, 23k stars) and Dogfood (redf0x1/camofox-browser, 412 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Dogfood Exploratory QA?

vercel-labs (a GitHub organization, an official publisher) maintains it in vercel-labs/agent-browser, which has 43,789 GitHub stars. The repository holds 10 skills in this directory. The repository was last updated on October 11, 2026.

Source: vercel-labs/agent-browser on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.