What does this skill do, and when should you use it?
Dogfood is an exploratory testing skill for mobile apps on iOS and Android. It uses agent-device to launch an app, map navigation, exercise core flows, check edge states, and document observed problems. Findings can include step screenshots and optional reproduction videos. It fits teams that need runtime QA evidence, provided agent-device and a runnable target app are available.
It takes a target app as required input and infers or asks for the platform. It uses agent-device to create sessions, open the app, capture snapshots and screenshots, perform interactions such as filling fields and pressing controls, wait for state changes, inspect app state and logs, and record evidence when an issue is found. The workflow ends with a structured report containing issue totals, severity counts, highest-risk findings, and reproduction evidence.
- A mobile developer checks primary navigation and core flows before an iOS or Android release.
- A QA engineer explores end-to-end journeys such as creating, editing, submitting, deleting, or recovering data.
- A product team validates empty, loading, error, offline, and denied-permission states.
- A team needs reproducible runtime bug reports with screenshots, numbered steps, and optional video.
How do you install this skill?
- Execution may mutate the target app through create, edit, delete, submit, and login flows; the skill does not require pre-action confirmation, isolated test accounts, or data recovery planning.
- Screenshots, videos, and logs may contain personal, credential, or business data; no redaction, retention, or access-control guidance is provided.
- agent-device installation, version, device connectivity, and network prerequisites are not validated within the skill, and failure feedback and recovery steps are limited.
- No runtime evidence is supplied beyond the static files, so report quality, issue-detection rate, and iOS/Android parity require human validation.
- Shell / CLI
- Local filesystem
agent-devicenpx
The README says the repository can provide bundled Codex plugins through .agents/plugins/marketplace.json and can also be cloned for use with its skill files. This skill is located at plugins/vendored/.agents/skills/dogfood/SKILL.md; the source does not document a dedicated dogfood installation command. agent-device must be available in the execution environment.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/callstackincubator/agent-skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/plugins/vendored/.agents/skills/dogfood" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Dogfood Settings on iOS and focus on onboarding
- Test com.example.app on Android, focus on profile, sign in with [email protected]
Example trigger: "Dogfood Settings on iOS and focus on onboarding". A target app is required; specify ios or android when the platform cannot be inferred. You may also provide a session name, output directory, scope, and authentication details, for example: "Test com.example.app on Android, focus on profile, sign in with [email protected]". If an OTP or email code is required, the workflow asks the user for it.
What are this skill's strengths and limitations?
- Provides a systematic workflow covering launch, navigation, core journeys, and edge states.
- Preserves screenshots, detailed steps, and optional reproduction video for each finding.
- Supports both iOS and Android and includes session, logging, and report wrap-up guidance.
- Requires agent-device plus a runnable mobile app and device environment.
- The SKILL.md uses Bash(agent-device:*) and Bash(npx agent-device:*) permission syntax, so other clients may need adaptation.
- The source does not establish compatibility across specific iOS or Android versions, device models, or app types.
- It explicitly avoids reading the app's source code, so it cannot add static-analysis evidence.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Mobile App Dogfood Tester this page | 46 · Use with care | ★ 1.7k | 19d ago | MIT |
| EAS Cloud Simulator | 60 · Recommended | ★ 2.9k | 22d ago | MIT |
| React Native Migration Assessor | 57 · Use with care | ★ 1.7k | 19d ago | MIT |
| Expo Skill Eval | 48 · Use with care | ★ 2.9k | 22d ago | MIT |
| Expo Native UI Builder | 59 · Recommended | ★ 2.9k | 22d ago | MIT |
How did FollowSkills review this skill?
The skill scopes its declared tool access to agent-device commands and requires the user to provide OTP/email codes, showing some permission and confirmation awareness. However, it can launch apps, authenticate, execute create/edit/delete flows, inspect logs, and write screenshots, videos, and reports without specifying sensitive-data handling, external data flows, rollback, or cleanup, so points are deducted.
The workflow, commands, and report template are broadly consistent and include resnapshotting, targeted log inspection, and abnormal-state exploration. However, agent-device version/install validation is absent, and failures involving unavailable devices, authentication, missing paths, or missing templates lack diagnosable recovery guidance; the static ceiling therefore applies and points are deducted.
Triggers, platforms, required target app, authentication, scope, and output are reasonably clear for mobile exploratory testing on iOS and Android. Non-fit cases, platform-specific boundaries, Chinese-language interaction support, mainland-China network reachability, and environmental dependency constraints are not documented, so points are deducted.
The skill uses useful progressive sections and supplies an issue taxonomy plus a report template. But it lacks skill-local versioning, changelog, explicit maintenance ownership, dependency installation instructions, FAQs, and known limitations; placeholder and path assumptions are also unexplained, so points are deducted.
The skill offers an actionable mobile exploration process, reproducibility guidance, and a structured report that could be directly useful. Static evidence contains no real execution results or representative completed report, while agent-device availability and substantial human judgment remain prerequisites, so correctness, completeness, and marginal benefit are not verifiable and points are deducted.
The skill, taxonomy, and template are auditable and provide limited internal corroboration. There is no key-path test suite, CI execution evidence, completed example report, or third-party reproduction record, so evidence strength is limited and points are deducted.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →