Dogfood logo

Dogfood

OrganizationPopular
callstackincubator
dogfood

Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", or "test this app" on mobile. Produces a structured report with reproducible evidence: screenshots, optional repro videos, and detailed steps for every issue.

Overview

Publishercallstackincubator
Repositoryagent-skills
Skill namedogfood
Stars
1.7K
Forks
118
Bundled files
2
LicenseMIT
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 callstackincubator 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.
    https://github.com/callstackincubator/agent-skills/tree/main/plugins/vendored/.agents/skills/dogfood
  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/callstackincubator/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/plugins/vendored/.agents/skills/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 (agent-device)

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

Setup

Only the Target app is required. Everything else has sensible defaults.

ParameterDefaultExample override
Target app(required)Settings, com.example.app, deep link URL
PlatformInfer from user context; otherwise ask (ios or android)--platform ios
Session nameSlugified app/platform (for example settings-ios)--session my-session
Output directory./dogfood-output/Output directory: /tmp/mobile-qa
ScopeFull appFocus on onboarding and profile
AuthenticationNoneSign in to user@example.com

If the user gives enough context to start, begin immediately with defaults. Ask follow-up only when a required detail is missing (for example platform or credentials).

Prefer direct agent-device binary when available.

Workflow

1. Initialize    Set up session, output dirs, report file
2. Launch/Auth   Open app and sign in if needed
3. Orient        Capture initial snapshot and map navigation
4. Explore       Systematically test flows and states
5. Document      Record reproducible evidence per issue
6. Wrap up       Reconcile summary, close session

1. Initialize

bash
mkdir -p {OUTPUT_DIR}/screenshots {OUTPUT_DIR}/videos
cp {SKILL_DIR}/templates/dogfood-report-template.md {OUTPUT_DIR}/report.md

2. Launch/Auth

Start a named session and launch target app:

bash
agent-device --session {SESSION} open {TARGET_APP} --platform {PLATFORM}
agent-device --session {SESSION} snapshot -i

If login is required:

bash
agent-device --session {SESSION} snapshot -i
agent-device --session {SESSION} fill @e1 "{EMAIL}"
agent-device --session {SESSION} fill @e2 "{PASSWORD}"
agent-device --session {SESSION} press @e3
agent-device --session {SESSION} wait 1000
agent-device --session {SESSION} snapshot -i

For OTP/email codes: ask the user, wait for input, then continue.

3. Orient

Capture initial evidence and navigation anchors:

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

Map top-level navigation, tabs, and key workflows before deep testing.

4. Explore

Read references/issue-taxonomy.md for severity/category calibration.

Strategy:

  • Move through each major app area (tabs, drawers, settings pages).
  • Test core journeys end-to-end (create, edit, delete, submit, recover).
  • Validate edge states (empty/error/loading/offline/permissions denied).
  • Use diff snapshot -i after UI transitions to avoid stale refs.
  • Periodically capture logs path and inspect the app log when behavior looks suspicious.

Useful commands per screen:

bash
agent-device --session {SESSION} snapshot -i
agent-device --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/{screen-name}.png
agent-device --session {SESSION} appstate
agent-device --session {SESSION} logs path

5. Document Issues (Repro-First)

Explore and document in one pass. When you find an issue, stop and fully capture evidence before continuing.

Interactive/behavioral issues

Use video + step screenshots:

  1. Start recording:
bash
agent-device --session {SESSION} record start {OUTPUT_DIR}/videos/issue-{NNN}-repro.mp4
  1. Reproduce with visible pacing. Capture each step:
bash
agent-device --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-step-1.png
sleep 1
# perform action
sleep 1
agent-device --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-step-2.png
  1. Capture final broken state:
bash
sleep 2
agent-device --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-result.png
  1. Stop recording:
bash
agent-device --session {SESSION} record stop
  1. Append issue immediately to report with numbered steps and screenshot references.
Static/on-load issues

Single screenshot is sufficient; no video required:

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

Set Repro Video to N/A in the report.

6. Wrap Up

Target 5-10 well-evidenced issues, then finish:

  1. Reconcile summary severity counts in report.md.
  2. Close session:
bash
agent-device --session {SESSION} close
  1. Report total issues, severity breakdown, and highest-risk findings.

Guidance

  • Repro quality matters more than issue count.
  • Use refs (@eN) for fast exploration, selectors for deterministic replay assertions when needed.
  • Re-snapshot after any mutation (navigation, modal, list update, form submit).
  • Use fill for clear-then-type semantics; use type for incremental typing behavior checks.
  • Keep logs optional and targeted: enable/read app logs only when useful for diagnosis.
  • Never read source code of the app under test; findings must come from observed runtime behavior.
  • Write each issue immediately to avoid losing evidence.
  • Never delete screenshots/videos/report artifacts during a session.

References

ReferenceWhen to Read
references/issue-taxonomy.mdStart of session; severity/categories/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 mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", or "test this app" on mobile. Produces a structured report with reproducible evidence: screenshots, optional repro videos, and detailed steps for every issue.

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/callstackincubator/agent-skills/tree/main/plugins/vendored/.agents/skills/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 callstackincubator under the MIT 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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