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.
curl -O https://raw.githubusercontent.com/Purewhiter/mobilegym/main/.claude/skills/designing-bench-task/SKILL.mdgit clone --depth 1 https://github.com/Purewhiter/mobilegymWrote 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/purewhiter/mobilegym/designing-bench-task)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00057 | $0.00868 |
| Opus 5 | $0.00028 | $0.00434 |
| Sonnet 5 | $0.00011 | $0.00174 |
| Haiku 4.5 | $0.00006 | $0.00087 |
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.
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"):
- Agent 完全做对时,最终 state / answer 长什么样?
- Agent 最常见的 1-2 种错误是什么?会不会被误判通过?(soundness)
- 有没有合理完成任务的替代路径?会不会被误判失败?(completeness)
- 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. |
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.
- 9d ago First seen · 61 lines · 57 tokens per session scan A e42a6185322b
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.
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