Four CN Lite tasks have missing or mismatched source data (tasks 33, 207, 380, and 381)

まず、tasks 33、207、380、381 の manifests と file_dep_graphs を、Issue に記載された実際の Workspace ファイルと比較します。影響を受ける Prompts と Rubric-Grounding audits を確認し、そのうえで各 task に修正済みの Assets が必要なのか、改訂された Contract が必要なのかを判断します。各必須結果が task から見える Inputs から導出可能になり、影響を受ける Rubrics が再生成されれば完了です。

Summary

Four tasks in the Chinese Workspace-Bench Lite dataset appear to have missing or mismatched source data:

• opendatabox-workspace-bench-33
• opendatabox-workspace-bench-207
• opendatabox-workspace-bench-380
• opendatabox-workspace-bench-381

I checked:

1. The task metadata, data_manifest, and file_dep_graph
2. The actual files distributed with each Lite task
3. The complete filename index of filesys_cn.zip from Workspace-Bench/Workspace-Bench-Workspaces (28,023 entries)
4. The provided rubric-grounding audit

The issues are described below.

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1. opendatabox-workspace-bench-33: hospital-grade data is unavailable

The task asks the agent to calculate the numbers of level-1, level-2, and level-3 hospitals in eastern, central, and western China.

The supplied inputs are:

• 1-12 2023年各地区按床位数分组的社区卫生服务中心(站)数.xlsx
• 1-2 2023年各地区医疗卫生机构数.xlsx
• 1-3 2023年各类医疗卫生机构数.xlsx

The files contain:

• Regional and province-level hospital totals
• Hospital counts by institution type
• Community health center/station bed-count groups
• National institution classifications

They do not contain regional or province-level hospital-grade fields.

A search of the complete Operations Manager workspace found no hospital-grade distribution workbook. The closest file is:

• 医疗分析/医院收支/4-12 2023年三级公立医院收入与支出.xlsx

That workbook contains national financial indicators by hospital grade, not regional or province-level hospital-grade counts.

As a result:

• The eastern, central, and western hospital totals are grounded.
• The community health center/station statistics are grounded.
• The required level-1, level-2, and level-3 counts for the three regions are not independently derivable.
• The Beijing and Shanghai hospital totals are grounded, but their grade-specific counts are not.

The current audit marks 10 of 19 rubrics as grounded and 9 as ungrounded.

Suggested maintainer action

Either:

1. Add the intended regional hospital-grade source workbook to the Operations Manager workspace and the task manifest; or
2. Remove the hospital-grade calculations and regenerate the affected rubrics using the available institution-type and bed-group data.

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2. opendatabox-workspace-bench-207: required scoring-model workbook is missing

The task explicitly says:

基于5-通用人才画像（模型）.xlsx评价四份简历，并且生成人才评价.xlsx到桌面

However, the task manifest contains only four resume files:

• 张浩然简历.docx
• 王佳宁简历.docx
• 赵思远简历.docx
• 李雨辰简历.docx

The required 5-通用人才画像（模型）.xlsx is absent from both the task manifest and file_dep_graph.

A full search of the Logistics Manager workspace found no file named:

• 5-通用人才画像（模型）.xlsx
• 5-通用人才画像(模型).xlsx

It also found no filename containing 通用人才画像.

The workspace contains only these related templates:

• 人才画像/模板/2-人才画像矩阵图.xlsx
• 人才画像/模板/3-人才画像_履历模型_.xlsx
• 人才画像/模板/4-人才画像_冰山模型_.xlsx
• 人才画像/模板/6-核心岗位人才画像_模版_.docx

None can safely be assumed to be the missing model.

Without the model workbook, an agent cannot independently recover:

• The five scoring dimensions and their exact weights
• The grade thresholds
• The intended scoring formulas
• Exact candidate scores such as 82, 85, 88, and 92

Resume-derived facts remain grounded, but the scoring-model results do not. The current audit marks 10 of 20 rubrics as grounded and 10 as
ungrounded.

Suggested maintainer action

Recover and add 5-通用人才画像（模型）.xlsx to:

• The Logistics Manager workspace
• data_manifest
• file_dep_graph
• Any generated setup/input manifest

If the original workbook contains expected candidate answers, please publish a sanitized template containing only task-visible criteria,
weights, formulas, and grade thresholds, and regenerate rubrics that depend on hidden scores.

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3. opendatabox-workspace-bench-380: prompt and source assets describe different business scenarios

The prompt claims the following assets are provided:

• activity_photos/
• impact_chart.csv
• feedback_wordcloud.png
• future_plan.md
• recruitment_poster.png

None of these paths exists in the complete Chinese workspace image.

The actual task manifest contains:

• post_1.json
• market_analysis_1.json
• user_feedback_category_202601.md
• strategic_plan_1.md
• training_module_1.md

These files describe corporate marketing operations rather than a volunteer association:

• post_1.json is an Instagram marketing post and contains only a remote example.com image URL.
• market_analysis_1.json describes market size, growth, and competitors.
• user_feedback_category_202601.md contains product, UX, and customer-service feedback.
• strategic_plan_1.md contains customer-growth, product, and revenue targets.
• training_module_1.md is a Marketing Operations training course.

Importantly, the files are not unrelated to the rubrics. Most exact rubric values come directly from these actual marketing files, including:

• Reach of 87,170,000
• 2,070,100 new followers
• A 3.51% engagement rate
• Market size 318990B, growth 6%, and CAGR 11%
• Feedback counts and percentages
• The 2024–2026 revenue targets
• The four-hour online Marketing Operations course

The current audit therefore marks 21 of 25 rubrics as grounded.

The defect is a semantic mismatch among:

• A volunteer-association prompt
• Corporate marketing inputs
• Rubrics primarily generated from the marketing inputs

Renaming the current files would not resolve the mismatch: a marketing post is not an activity-photo collection, and a marketing training
module is not a recruitment poster.

Suggested maintainer action

Either:

1. Add the five intended volunteer-association assets and regenerate all content rubrics from them; or
2. Rewrite the prompt as a corporate marketing annual-review task, retaining the current inputs and grounded rubric values while removing the
   unsupported volunteer, activity-photo, word-cloud, and recruitment-poster requirements.

Given the existing rubric content, the second option may require fewer changes.

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4. opendatabox-workspace-bench-381: filename, year, format, data-grain, and scoring-model mismatch

The prompt names five 2025 CSV files:

• hospital_finance_2025.csv
• liabilities_2025.csv
• drug_production_2025.csv
• medical_expenses_2025.csv
• chronic_disease_2025.csv

None exists in the complete Chinese workspace image.

The actual task inputs are five Chinese-named 2023 XLSX workbooks:

• 4-12 2023年三级公立医院收入与支出.xlsx
• 4-6 2023年各类医疗卫生机构资产与负债.xlsx
• 4-4-2 2023年全国药品生产流通.xlsx
• 4-20 2023年各地区公立医院门诊和住院病人次均医药费用.xlsx
• 9-10 2023年调查地区15岁及以上居民慢性病患病率.xlsx

This is not only a filename mismatch. The data grains are incompatible with the requested province-level correlation analysis:

• Medical expenses: 31 province-level regions
• Drug production/distribution: 31 province-level regions
• Chronic disease: national, urban/rural, and broad eastern/central/western aggregates only
• Hospital income and expenditure: hospital-grade aggregates, not provinces
• Assets and liabilities: institution-type aggregates, not provinces

Therefore, the inputs cannot be merged into a 31-region dataset containing both financial-health and chronic-disease variables.

The task also does not define:

• The financial-health score
• The balance/surplus ratio formula
• Risk-score weights
• Low/medium/high-risk thresholds
• How the exact 5/23/3 risk split should be produced

There are also apparent rubric inconsistencies:

• A value of 86.06% appears more consistent with an expense-to-income ratio than a conventional surplus ratio. A conventional surplus ratio
  would instead be approximately 13.94%.
• If drug-production licenses are used as the industry-scale variable, the correlation with outpatient cost is approximately r = +0.108,
  not negative.
• Using drug-distribution-business licenses instead gives approximately r = -0.137, showing that the required negative conclusion depends
  on an unspecified metric choice.

The current audit marks only 3 of 20 rubrics as grounded and 17 as ungrounded.

Suggested maintainer action

This task likely needs to be regenerated rather than fixed through filename aliases alone.

Either:

1. Add the intended 2025 province-level CSV datasets, explicitly define all formulas and risk thresholds, and regenerate the rubrics; or
2. Rewrite the task around the available 2023 XLSX workbooks, restrict analysis to supported aggregation levels, define all derived metrics,
   and regenerate the correlation and risk rubrics.

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Expected outcome

Please either correct the affected source assets and manifests or revise the task contracts and regenerate rubrics so that every required
result can be independently derived from task-visible inputs.