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 kaeawc/auto-mobile --skill manual-testgit clone --depth 1 https://github.com/kaeawc/auto-mobileWrote 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/kaeawc/auto-mobile/manual-test)<a href="https://agentmods.dev/skills/kaeawc/auto-mobile/manual-test"><img src="https://agentmods.dev/badge/skills/kaeawc/auto-mobile/manual-test.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to high
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 →
- high Tool Misuse · line 76 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- high Tool Misuse · line 76 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
- high Tool Misuse · line 76 Tool calls are chained to bypass individual safety checks or escalate capabilities beyond what any single tool call would allow.Fix: Limit tool chaining depth and validate the output of each tool before passing it to the next. Require explicit user approval for multi-step chains.
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 | $0.00091 | $0.04435 |
| Opus 5 | $0.00046 | $0.02218 |
| Sonnet 5 | $0.00018 | $0.00887 |
| Haiku 4.5 | $0.00009 | $0.00443 |
Grade A, and why
manual-test 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 — 272 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AutoMobile Manual Test Iteration
Verify that the work claimed done since a starting point is actually done on
the current HEAD of main — reproduce-then-confirm each bug fix, exercise each
specced feature end to end, and sweep the changed tool surface for regressions.
Drive a real Android emulator and iOS simulator. Ground every PASS in an observed
field or device-side ground truth, never the tool's self-reported success.
Device work is sequential — one device at a time (no parallelism yet). Do Phase A (Android) fully, then Phase B (iOS). Delegate breadth to one subagent at a time to conserve context; never let two actors drive devices at once.
Phase 0 — Scope from the start point
- Get the start point. Accept a commit SHA, a tag/milestone, or a date. If
none was given, ask for one (offer the last release tag as default:
git tag | sort -V | tail). Resolve it to a git ref<START>. - Enumerate landed work in
<START>..origin/main:- Merged PRs:
gh pr list --state merged --search "merged:>=<DATE>" --json number,title,closingIssuesReferences(or by commit range). - Closed issues:
gh issue list --state closed --search "closed:>=<DATE>" --json number,title,labels. - Map each to a type: bug-fix (reproduce → confirm fixed) or feature/spec (exercise → confirm the output/effect exists).
- Merged PRs:
- Scope the changed tool surface for regression risk:
git log --oneline <START>..HEAD | grep -viE "README test count badges|deps"andgit diff --stat <START>..HEAD -- src/. Map changed non-test source files to the MCP tools they implement (src/features/**,src/server/*Tools.ts,schemas/tool-definitions.json). - Note which items are runner-side (need an APK/runner rebuild — see Phase 1)
vs flag-gated (need
--embedded-sdk/--network-mockable— see Phase 2) vs blocked (need a physical iOS device or an on-sim SDK app — see Phase 3). - Produce a checklist:
item # | type | tool(s) | needs (rebuild/flag/device) | observable to check.
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 · 272 lines · 91 tokens per session scan A 68b87aac0ef7
manual-test is a skill published in the GitHub repository kaeawc/auto-mobile (46 stars, last pushed today), licensed Apache-2.0. It adds 91 tokens to every session and 4,435 once invoked, about $0.0005 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-journey-testing
XML-specified Android UI journey testing, interactive step execution, assertion verification, and JSON outcome reporting.
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.
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…
flutter-add-integration-test
Configures Flutter Driver for app interaction and converts MCP actions into permanent integration tests. Use when adding integration testing to a project, exploring UI components via MCP, or automating user flows with the integrationtest package.