Callstack Agent Skills is a collection of instructions that gives AI coding assistants practical knowledge for building React Native applications. Developers use it for app development, performance, navigation, testing, CI, device automation, and migrations to React Native. The catalogue entries are the project's skills, rules, and instructions for compatible coding assistants.
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 callstackincubator/agent-skills --skill dogfoodgit clone --depth 1 https://github.com/callstackincubator/agent-skillsWrote 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/callstackincubator/agent-skills/dogfood)<a href="https://agentmods.dev/skills/callstackincubator/agent-skills/dogfood"><img src="https://agentmods.dev/badge/skills/callstackincubator/agent-skills/dogfood.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium MCP Rug Pull · line 4 npx commands without a version suffix (e.g. @1.0.0) create a rug-pull risk if the upstream server is compromised and publishes a malicious update.Fix: Pin the version: npx @scope/[email protected]
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 | $0.00087 | $0.01446 |
| Opus 5 | $0.00044 | $0.00723 |
| Sonnet 5 | $0.00017 | $0.00289 |
| Haiku 4.5 | $0.00009 | $0.00145 |
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 8d 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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.
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.
- 8d ago First seen · 184 lines · 87 tokens per session scan A 5448d2ac8263
dogfood is a skill published in the GitHub repository callstackincubator/agent-skills (1,637 stars, last pushed 1mo ago), licensed MIT. It adds 87 tokens to every session and 1,446 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 2026-08-30.
Other skills, from other repositories
android_ui_verification
Automated end-to-end UI testing and verification on an Android Emulator using ADB.
solopi-ai
A command-line framework for testing Android apps and devices with SoloPi, including on-device or cloud AI decision models. It manages devices, test cases, recorded interactions, replays, performance history, and evidence.
agent-device
Automates Apple-platform apps (iOS, tvOS, macOS), Android devices, and Amazon Vega OS TV apps in Vega Virtual Devices. Use when navigating apps, taking snapshots/screenshots where supported, driving TV remotes, tapping, typing, scrolling, extracting UI info, collecting evidence, or planning agent-device CLI commands.
ios-simulator
Verify and debug native, React Native, Expo, or Flutter apps on an iOS Simulator with agent-device. Use when an agent needs to launch an app, inspect its live UI, tap, type, scroll, validate a code change, collect failure evidence, or reproduce a workflow on an iPhone or iPad Simulator.
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.
eas-simulator
EAS service (paid). Run and control a user's app on a remote iOS/Android simulator hosted on EAS cloud. Read before running any eas simulator: commands - it has the current syntax for this experimental API. Use whenever the user needs a simulator they can't run locally - 'run my app on a cloud simulator', 'use eas…