mobilegym: Skill for Claude Code

.claude/skills/designing-bench-task/SKILL.md

designing-bench-task is a skill for Claude Code from Purewhiter/mobilegym. It costs 57 tokens per session (868 once invoked), scanned A, original, Apache-2.0.

A planning guide for designing task suites that test mobile applications. It requires a feature audit and a check that the app has enough varied data before task code is written.

In plain words
What is it for?
Use it when creating a new benchmark task suite, adding tasks to an existing suite, or evaluating a proposal before implementing task classes.
Why use it?
It helps prevent test tasks that are too easy or cannot be verified from the app's observable state. A task suite is a collection of repeatable app challenges used to measure whether an agent completes them correctly.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is Purewhiter/mobilegym's own configuration. It tells Claude Code how to work on mobilegym itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything mobilegym configures →

Reuse

Borrowing it

Nothing to install: this file belongs to Purewhiter/mobilegym. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/Purewhiter/mobilegym/main/.claude/skills/designing-bench-task/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/Purewhiter/mobilegym

Made for: Claude Code.

Wrote 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.

agentmods badge for designing-bench-task

README.md
[![agentmods](https://agentmods.dev/badge/skills/purewhiter/mobilegym/designing-bench-task/github.svg)](https://agentmods.dev/skills/purewhiter/mobilegym/designing-bench-task)
Your own site
<a href="https://agentmods.dev/skills/purewhiter/mobilegym/designing-bench-task"><img src="https://agentmods.dev/badge/skills/purewhiter/mobilegym/designing-bench-task/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for designing-bench-task

Your own site · 80×15
<a href="https://agentmods.dev/skills/purewhiter/mobilegym/designing-bench-task"><img src="https://agentmods.dev/badge/skills/purewhiter/mobilegym/designing-bench-task.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 868 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00057 $0.00868
Opus 5 $0.00028 $0.00434
Sonnet 5 $0.00011 $0.00174
Haiku 4.5 $0.00006 $0.00087

Measured 9d ago against content hash e42a6185322b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

designing-bench-task 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 9d 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.

.claude/skills/designing-bench-task/SKILL.md · 61 lines

How it starts

The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Designing bench_env Tasks

Overview

Rushing from "here's an App" to "here are 5 task classes" produces low-difficulty suites whose judge logic can't actually verify completion. Design must precede code.

Authoritative reference: bench_env/docs/task/TASK_AUTHORING_GUIDE.md (reading §1 + §2 once is required; this skill enforces its gates).

The Gate: two artifacts before any Python

Produce both as plain text in the conversation before writing any task class. If you catch yourself opening tasks.py / defs/<TaskName>.py, stop and produce them.

1. Functional audit table (TASK_AUTHORING_GUIDE §1.1–1.2)

A table with one row per distinct feature area. Columns:

| Page/feature | Source file(s) | User-visible actions | Observable state path |

You must actually read: manifest.ts, navigation.declaration.ts, data/defaults.json, state.ts, pages/*, and the suite's app.py accessor if it exists. No skipping "because the app looks simple."

2. Data sufficiency check (TASK_AUTHORING_GUIDE §1.3)

For every function you plan to parameterize, confirm defaults.json / state.ts provides ≥3 varied entries. If it doesn't, either propose expanding defaults, or drop parameterization for that function.

Per-task: 4 judge-predict questions

For each proposed task, answer in 1-2 lines each before writing code (this is the soundness/completeness audit later enforced by TASK_AUTHORING_GUIDE §2.7 "Reliability requirements"):

  1. Agent 完全做对时,最终 state / answer 长什么样?
  2. Agent 最常见的 1-2 种错误是什么?会不会被误判通过?(soundness)
  3. 有没有合理完成任务的替代路径?会不会被误判失败?(completeness)
  4. 有无边界情况导致正确答案不唯一、或判定证据不足?

If any answer surfaces a flaw (common: initial state already equals criteria; ground truth not unique; answer requires subjective judgement), iterate the design in text — do not defer the fix to code review.

Rationalization table — STOP and do the step

Excuse Reality
"App is tiny, audit is overkill" Audit surfaces the data gap so you can close it before writing code.
"Judge predict is slow, I'll see issues when coding" Design bugs (init=goal, non-unique ground truth) are 10× cheaper to fix in text.
"These tasks are obvious, pre-sim is busywork" A task obvious enough to skip pre-sim is obvious enough to answer the 4 questions in 30 seconds.
"I'll produce both artifacts and code in one pass" Then when code inherits a design flaw, you've wasted the coding pass. Gate is gate.

Read the full file on GitHub · 61 lines

Changes

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.

  1. 9d ago First seen · 61 lines · 57 tokens per session scan A e42a6185322b

Subscribe to this mod's changes

designing-bench-task is a skill published in the GitHub repository Purewhiter/mobilegym (787 stars, last pushed 12d ago), licensed Apache-2.0. It adds 57 tokens to every session and 868 once invoked, about $0.0003 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.

Related

Other skills, from other repositories

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.

callstack/agent-device · 69 tokens

android-emulator

Verify and debug native, React Native, Expo, or Flutter apps on an Android Emulator 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 Android virtual device.

callstack/agent-device · 65 tokens

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.

callstack/agent-device · 69 tokens

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.

callstack/agent-device · 55 tokens

meter-benchmark

Measure per-stage success probability before iterating on CocoFlow templates. Supports $meter benchmark --flow .

Snowflake-Labs/cocoplus · 27 tokens

testing-flutter

Testing Flutter 3.44 / BLoC v9 / Riverpod 3 - Stratégie Complète. Use when writing tests, reviewing test coverage, or setting up testing.

TheBeardedBearSAS/claude-craft · 42 tokens