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.
npx skills add d-o-hub/github-template-ai-agents --skill dogfoodgit clone --depth 1 https://github.com/d-o-hub/github-template-ai-agentsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/dogfood)<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/dogfood"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/dogfood/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/d-o-hub/github-template-ai-agents/dogfood"><img src="https://agentmods.dev/badge/skills/d-o-hub/github-template-ai-agents/dogfood.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00084 | $0.01536 |
| Opus 5 | $0.00042 | $0.00768 |
| Sonnet 5 | $0.00017 | $0.00307 |
| Haiku 4.5 | $0.00008 | $0.00154 |
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 9d 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.
How it starts
The opening of the file, as written. The whole thing — 170 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.
When to Use
- User asks to dogfood, QA, or exploratory test a web application
- Need to find bugs, UX issues, or other problems in a web app
- Even if they just say "test this app" or "find issues in this site"
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
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
What ships with it
3 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.
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.
- 9d ago First seen · 170 lines · 84 tokens per session scan A 7f92f26c3194
dogfood is a skill published in the GitHub repository d-o-hub/github-template-ai-agents (2 stars, last pushed yesterday), licensed MIT. It adds 84 tokens to every session and 1,536 once invoked, about $0.0004 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 2026-08-31.
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