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 yushui2022/MathModel-Skill --skill data-cleaning-and-visualizationgit clone --depth 1 https://github.com/yushui2022/MathModel-SkillWrote 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/yushui2022/mathmodel-skill/data-cleaning-and-visualization)<a href="https://agentmods.dev/skills/yushui2022/mathmodel-skill/data-cleaning-and-visualization"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/data-cleaning-and-visualization/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/yushui2022/mathmodel-skill/data-cleaning-and-visualization"><img src="https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/data-cleaning-and-visualization.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 | $0.00094 | $0.03418 |
| Opus 5.5 | $0.00038 | $0.01367 |
| Sonnet 5.5 | $0.00019 | $0.00684 |
| Haiku 4.5 | $0.00009 | $0.00342 |
Grade A, and why
data-cleaning-and-visualization 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 6d 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 — 145 lines — stays where its author put it; the contents beside it link to each section on GitHub.
数据清洗与可视化 (Data Cleaning and Visualization)
Resolve <SKILL_ROOT> to the parent directory of this loaded skill folder, using its actual absolute path. Run commands from the contest project root, never from the installed skill directory. The placeholder is not a shell environment variable.
全局流程协作约束（长对话防漂移）
-
S3 自动图表都是
needs_review的探索草图，不是实验结论。不得根据原始字段直接宣称预测效果、敏感性或模型优劣；敏感性图必须先运行参数扰动实验。 -
核对字段、单位和图形类型后，由赛题建模脚本基于真实结果重画正式图表，并登记到对应运行的
output_artifacts。正式索引只收录这些证据；保留探索文件和失败日志，不要改状态冒充正式结果。 -
本 skill 不得作为孤立入口。用户要求完整论文、生成 Word、继续流程或不确定阶段时，先回到
paper-workflow-orchestrator判断当前 S0-S8 阶段。 -
启动或继续本 skill 的正式任务前，必须运行：
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --skill data-cleaning-and-visualization -
如果输出
[WORKFLOW FAIL]或报告status != "PASS"，停止本 skill，按paper_output/qa/workflow_guard_report.json的失败项回补前置阶段，不得凭记忆继续。 -
本 skill 只写入自己契约范围内的
paper_output/产物；完成后必须回到paper-workflow-orchestrator判断下一步，并用context-memory-keeper记录已完成产物、阻塞项和下一步。 -
长对话中如果上下文变长、阶段不确定或用户分开调用 skill，先运行：
python "<SKILL_ROOT>/paper-workflow-orchestrator/scripts/workflow_guard.py" --status再读取
paper_output/qa/workflow_guard_report.json、paper_output/preflight_report.json、paper_output/input_manifest.json、paper_output/results/run_manifest.json和本 skill 的上游 JSON 契约，按报告里的recommended_skill与next_action继续。 -
继续流程前，必须把
paper_output/context/workflow_memory.json视为长期断点记录；若其中的current_step、next_step、recommended_skill与workflow_guard.py --status不一致，以 guard 报告为准。 -
每次完成本 skill 的产物后，先回到
paper-workflow-orchestrator或运行workflow_guard.py --status，再更新 workflow memory：python "<SKILL_ROOT>/context-memory-keeper/scripts/update_workflow_memory.py"更新后读取
paper_output/context/workflow_memory.json/.md，确认下一步和推荐 skill 已记录。
执行契约
- 上游输入：优先读取
paper_output/input_manifest.json、paper_output/step1/problem_analysis.json与paper_output/plan/model_route.json；正式流程只处理 manifest 中标为raw_data且usable_for_modeling=true的附件。 - 必须输出：
paper_output/data_cleaned/load_report.json、paper_output/plan/data_plan.json、paper_output/plan/visualization_plan.json、paper_output/figure_index.json；有可处理数据时同步输出paper_output/data_cleaned/与paper_output/figures/。 - 下游交接：
quality-assurance-auditor审计数据/图表证据；S7 写作计划直接引用figure_index.json、表格索引和结果契约。tasks.json仅供 legacy/quickstart。 - 推荐下一步：完成数据和图表计划后回到
paper-workflow-orchestrator，按 S4-S6 运行模型和证据审计，再由正式主笔写作。 - 失败回退：若没有可处理数据文件，仍尽量根据题意和模型路线生成计划文件；不得把模板图表直接当作最终真实结果。
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 355 B
- references/data-integrity.md 3.0 KB
- scripts/build_data_visualization_plan.py 21 KB runs code
- scripts/clean_data.py 7.9 KB runs code
- scripts/data_inputs.py 5.0 KB runs code
- scripts/generate_paper_figures_from_plan.py 5.0 KB runs code
- scripts/paper_figure_templates.py 20 KB runs code
- scripts/robust_loader.py 15 KB runs code
- scripts/run_pipeline.py 1.3 KB runs code
- scripts/visualize_data.py 4.6 KB runs code
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.
- 6d ago Changed · +1 lines · +40 tokens per session c1078a195c0b
- 8d ago Changed · +5 lines 026d519aed15
- 12d ago Changed · +1 lines d5536cf6f926
- 1mo ago Changed dec9cd296681
- 1mo ago First seen · 138 lines · 54 tokens per session scan A 79966614001f
data-cleaning-and-visualization is a skill published in the GitHub repository yushui2022/MathModel-Skill (456 stars, last pushed 3d ago), licensed MIT. It adds 94 tokens to every session and 3,418 once invoked, about $0.0004 per session on Opus 5.5. 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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