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/writing-bench-task-judge/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/writing-bench-task-judge)<a href="https://agentmods.dev/skills/purewhiter/mobilegym/writing-bench-task-judge"><img src="https://agentmods.dev/badge/skills/purewhiter/mobilegym/writing-bench-task-judge/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/writing-bench-task-judge"><img src="https://agentmods.dev/badge/skills/purewhiter/mobilegym/writing-bench-task-judge.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 | $0.00068 | $0.04584 |
| Opus 5 | $0.00034 | $0.02292 |
| Sonnet 5 | $0.00014 | $0.00917 |
| Haiku 4.5 | $0.00007 | $0.00458 |
Grade A, and why
writing-bench-task-judge 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 12d 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 — 255 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing bench_env Task Judges
Overview
A judge decides whether the Agent completed the task. Two failure modes dominate:
- Soundness hole — an obviously wrong path is judged
passed=True(keyword hit, unstable trace field). - Completeness hole — a reasonable completion path is judged
passed=False(bound to specific wording or UI step).
Neither is caught by type checks — both are caught by applying the CRUD model rigorously.
Authoritative refs:
bench_env/docs/task/TASK_AUTHORING_GUIDE.md— task design + CRUD judge model (read §2 in full)bench_env/docs/task/TASK_CODE_SPEC.md— code rules (file responsibilities, naming, defensive-coding ban, time APIs, CriteriaTask)bench_env/docs/REFERENCE.md—JudgeInput/JudgeResultfield &expected_changespath syntax lookupbench_env/docs/task/GROUNDED_MODE.md— only if task usesanswer_fields/ grounded eval
Step 1 — Classify the task as CRUD
Before writing any check, name the operation out loud:
| Operation | Triggering verbs | Required check strategy |
|---|---|---|
| Create (增) | 添加/创建/发送/收藏/点赞/新建 | diff: init vs current, match in new items |
| Delete (删) | 删除/移除/取消/取关/下架 | diff: target id in init_ids - curr_ids |
| Modify (改) | 修改/切换/改名/设置为/拨动 | lookup in current; use init to resolve identity |
| Query (查) | 告诉我/是多少/哪个/什么时候 | read from init, compare to input.answer |
Each CRUD type has ONE correct strategy — not a choice. Using lookup-on-current for Delete/Create creates false positives (sampler bug → passes as Agent action).
Step 2 — Survey the App module for reusable abstractions
Before writing any judge, Read app.py end-to-end with the Read tool (split into sections only if the file exceeds the tool's limit). Scan every def line plus its docstring / leading comment, plus any module-level helper functions above the class. Do not substitute grep for this. Grep silently misses:
- module-level helpers (
_note_text,_pick_keywords_from_note) that sit above the class @staticmethod/@property/@classmethodmethods that your prefix guess won't anticipate- helpers whose names don't follow the shape you grepped for (
latest_note_by_titleis a find-by-name primitive but doesn't start withfind_;visible_notesis a filtered view but doesn't start withfiltered_) _xxxprivate methods that contain logic you could extract
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.
- 12d ago First seen · 255 lines · 68 tokens per session scan A 46d72eb770b4
writing-bench-task-judge is a skill published in the GitHub repository Purewhiter/mobilegym (787 stars, last pushed 14d ago), licensed Apache-2.0. It adds 68 tokens to every session and 4,584 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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