dogfood

A structured way to explore and test a web application for bugs, usability problems, and other quality issues. It records how each problem can be reproduced and produces a report.

In plain words
What is it for?
Use it to test a website or web app, optionally sign in, focus on a chosen area, capture evidence, and document reproducible findings.
Why use it?
It makes exploratory testing systematic instead of relying on a few manual clicks or vague observations.

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/pleaseai/claude-code-plugins/dogfood
Any agent
npx skills add pleaseai/claude-code-plugins --skill dogfood
Clone the repo
git clone --depth 1 https://github.com/pleaseai/claude-code-plugins

Made for: Claude Code, Codex.

Per session 100 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,404 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 100% copy Near-identical to another mod 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 $0.00100 $0.02404
Opus 5 $0.00050 $0.01202
Sonnet 5 $0.00020 $0.00481
Haiku 4.5 $0.00010 $0.00240

Measured 2d ago against content hash c86db6b33c8f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

dogfood 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.

Origin

This is a copy

100% identical to dogfood — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

plugins/agent-browser/.agents/skills/dogfood/SKILL.md · 221 lines

How it starts

The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Parameter Default Example override
Target URL (required) vercel.com, http://localhost:3000
Session name Slugified domain (e.g., vercel.com -> vercel-com) --session my-session
Output directory ./dogfood-output/ Output directory: /tmp/qa
Scope Full app Focus on the billing page
Authentication None Sign in to [email protected]

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

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

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

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

Start a named session:

agent-browser --session {SESSION} open {TARGET_URL}
agent-browser --session {SESSION} wait --load networkidle

2. Authenticate

If the app requires login:

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
agent-browser --session {SESSION} wait --load networkidle

Read the full file on GitHub · 221 lines

Files

What ships with it

2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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 · 221 lines · 100 tokens per session scan A c86db6b33c8f

Subscribe to this mod's changes

dogfood is a skill published in the GitHub repository pleaseai/claude-code-plugins (13 stars, last pushed 7d ago), licensed MIT. It adds 100 tokens to every session and 2,404 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to dogfood, differing in 0 lines, and is treated as a copy.

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