dogfood▌
callstackincubator/agent-device · updated Apr 8, 2026
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Systematically explore a mobile app, find issues, and produce a report with full reproduction evidence for every finding.
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.
| Parameter | Default | Example override |
|---|---|---|
| Target app | (required) | Settings, com.example.app, deep link URL |
| Platform | Infer from user context; otherwise ask (ios or android) |
--platform ios |
| Session name | Slugified app/platform (for example settings-ios) |
--session my-session |
| Output directory | ./dogfood-output/ |
Output directory: /tmp/mobile-qa |
| Scope | Full app | Focus on onboarding and profile |
| Authentication | None | Sign in to [email protected] |
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
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:
agent-device --session {SESSION} open {TARGET_APP} --platform {PLATFORM}
agent-device --session {SESSION} snapshot -i
If login is required:
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:
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 -iafter UI transitions to avoid stale refs. - Periodically capture
logs pathand inspect the app log when behavior looks suspicious.
Useful commands per screen:
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:
- Start recording:
agent-device --session {SESSION} record start {OUTPUT_DIR}/videos/issue-{NNN}-repro.mp4
- Reproduce with visible pacing. Capture each step:
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
- Capture final broken state:
sleep 2
agent-device --session {SESSION} screenshot {OUTPUT_DIR}/screenshots/issue-{NNN}-result.png
- Stop recording:
agent-device --session {SESSION} record stop
- Append issue immediately to report with numbered steps and screenshot references.
Static/on-load issues
Single screenshot is sufficient; no video required:
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:
- Reconcile summary severity counts in
report.md. - Close session:
agent-device --session {SESSION} close
- 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
fillfor clear-then-type semantics; usetypefor 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
| Reference | When to Read |
|---|---|
| references/issue-taxonomy.md | Start of session; severity/categories/checklist |
Templates
| Template | Purpose |
|---|---|
| templates/dogfood-report-template.md | Copy into output directory as the report file |
How to use dogfood on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add dogfood
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches dogfood from GitHub repository callstackincubator/agent-device and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate dogfood. Access the skill through slash commands (e.g., /dogfood) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.8★★★★★25 reviews- ★★★★★Camila Liu· Dec 24, 2024
I recommend dogfood for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Naina Diallo· Dec 4, 2024
Solid pick for teams standardizing on skills: dogfood is focused, and the summary matches what you get after install.
- ★★★★★Luis Yang· Nov 15, 2024
Useful defaults in dogfood — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Yash Thakker· Nov 3, 2024
Solid pick for teams standardizing on skills: dogfood is focused, and the summary matches what you get after install.
- ★★★★★Dhruvi Jain· Oct 22, 2024
dogfood is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Luis Martin· Oct 6, 2024
Registry listing for dogfood matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Oshnikdeep· Sep 25, 2024
dogfood fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Neel Zhang· Sep 21, 2024
We added dogfood from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Ganesh Mohane· Aug 16, 2024
dogfood has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Neel Rahman· Aug 12, 2024
dogfood reduced setup friction for our internal harness; good balance of opinion and flexibility.
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