agentmods.dev

data-cleaning-and-visualization

data-cleaning-and-visualization is a skill for Claude Code, Codex from yushui2022/MathModel-Skill. It costs 54 tokens per session (2,964 once invoked), scanned A, original, MIT.

A workflow for automatically cleaning contest or scraped data and producing charts. Cleaning can include handling missing values, unusual values, and inconsistent formats.

In plain words
What is it for?
Use it to clean raw datasets and create visualisations for data analysis.
Why use it?
It reduces the manual work of preparing messy raw data before analysis and makes the results easier to inspect visually.

Skill for Claude CodeCodex

not rated 350 +28 changed yesterday A scan 54 tokens original MIT
View the source file yushui2022/MathModel-Skill
Install

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.

agentmods
npx agentmods add skills/yushui2022/mathmodel-skill/data-cleaning-and-visualization
Any agent
npx skills add yushui2022/MathModel-Skill --skill data-cleaning-and-visualization
Clone the repo
git clone --depth 1 https://github.com/yushui2022/MathModel-Skill

Made for: Claude Code, Codex.

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 data-cleaning-and-visualization

README.md
[![agentmods](https://agentmods.dev/badge/skills/yushui2022/mathmodel-skill/data-cleaning-and-visualization.svg)](https://agentmods.dev/skills/yushui2022/mathmodel-skill/data-cleaning-and-visualization)
Your own site
<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="Measured on agentmods" height="20"></a>
Per session 54 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,964 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00054 $0.02964
Opus 5 $0.00027 $0.01482
Sonnet 5 $0.00011 $0.00593
Haiku 4.5 $0.00005 $0.00296

Measured yesterday against content hash dec9cd296681, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

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 yesterday.

The scan reads SKILL.md. This mod also ships 7 executable files (scripts/build_data_visualization_plan.py, scripts/clean_data.py, scripts/generate_paper_figures_from_plan.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

packages/claude/.claude/skills/data-cleaning-and-visualization/SKILL.md · 138 lines

How it starts

The opening of the file, as written. The whole thing — 138 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)

全局流程协作约束（长对话防漂移）

  • 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 .claude/skills/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 .claude/skills/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 .claude/skills/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进入 quality-assurance-auditor 生成任务清单；完整论文目标应回到 paper-workflow-orchestrator 判断后续阶段。
  • 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。

Read the full file on GitHub · 138 lines

Files

What ships with it

7 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.

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. yesterday Changed dec9cd296681
  2. 6d ago First seen · 138 lines · 54 tokens per session scan A 79966614001f

Subscribe to this mod's changes

data-cleaning-and-visualization is a skill published in the GitHub repository yushui2022/MathModel-Skill (350 stars, last pushed today), licensed MIT. It adds 54 tokens to every session and 2,964 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.

Analytics and metricsScientific
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