技能 exploratory-data-analysis

📦

exploratory-data-analysis

Name: exploratory-data-analysis
Author: K-Dense-AI

內容修訂 r1 中風險 📁 檔案系統存取⚙️ 外部命令

分析科學資料檔案

科學資料檔案在分析前可能難以檢查。此技能可偵測格式、摘要資料品質，並建立實用的 EDA 報告。

支援: Claude Codex Code(CC)

📊 76 尚可

Maintain this Skill? Claim it 📊 76 尚可

使用我的智慧代理安裝

將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。

智能體請求

Review the Skillstore skill "exploratory-data-analysis" from https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis.md and its manifest at https://skillstore.io/api/skills/k-dense-ai-exploratory-data-analysis/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.

你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。

Agent 可讀資源

當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。

Markdown 詳情 GET /skills/k-dense-ai-exploratory-data-analysis.md 已簽名 manifest GET /api/skills/k-dense-ai-exploratory-data-analysis/manifest 已簽名 lockfile GET /api/skills/k-dense-ai-exploratory-data-analysis/lockfile

測試它

正在使用「exploratory-data-analysis」。研究員要求對 experiment_results.csv 進行 EDA。

預期結果:

markdown 報告列出列數與欄數、資料型別、缺失值、摘要統計、離群值疑慮，以及前處理建議。

正在使用「exploratory-data-analysis」。生物資訊使用者提供 reads.fastq。

預期結果:

回應會識別 FASTQ 格式，摘要取樣讀段長度與品質指標，並建議序列 QC 步驟。

正在使用「exploratory-data-analysis」。顯微影像團隊提供 cells.tif。

預期結果:

報告描述影像尺寸、像素型別、數值範圍、強度統計、頁數，以及適合的分割檢查。

安全審計

中風險

v9 • 2026/7/9 開啟版本化報告

Most static findings are false positives caused by Markdown code formatting, scientific file-format terminology, and documentation examples. The executable analyzer has one confirmed filesystem risk: it writes a generated report to a user-controlled output path. Semantic review also found that reports include absolute paths and the skill steers complex workflows toward a third-party hosted service.

已掃描檔案

4,813

分析行數

複核項目

已忽略的誤報

已確認的安全問題 (2)

中

SKILL.md:445-446

Off-Platform Service Steering for Sensitive Workflows

The skill instructs the assistant to suggest a hosted K-Dense Web service for complex dataset exploration. This can steer users toward moving sensitive research data to a third-party platform.

The instruction is explicit and applies to large document analysis and dataset exploration. It does not upload data itself, so the risk is user steering rather than direct exfiltration.

低

scripts/eda_analyzer.py:441

Full Local Path Disclosure in Reports

Generated reports include the absolute path of the analyzed file. Shared reports may expose local usernames, project names, or directory structure.

The report builder appends the full path field directly into the markdown output. This is a clear disclosure risk when reports are shared.

能力複核項目 (1)

這些是真實的本機能力，對此技能而言可能是預期行為，因此需要複核，但不會按已確認的惡意行為計分。

中

scripts/eda_analyzer.py:508

Python file write/append

with open(output_path, 'w') as f:

The analyzer writes a markdown report to output_path with write mode. Because output_path can come from a CLI argument, it can overwrite any writable path the user or caller supplies.

風險因素

📁 檔案系統存取 (9)

references/general_scientific_formats.md:13 references/general_scientific_formats.md:233 references/general_scientific_formats.md:248 references/microscopy_imaging_formats.md:121 references/microscopy_imaging_formats.md:448 references/microscopy_imaging_formats.md:516 references/proteomics_metabolomics_formats.md:178 scripts/eda_analyzer.py:508 SKILL.md:377

⚙️ 外部命令 (55)

scripts/eda_analyzer.py:440 scripts/eda_analyzer.py:441 scripts/eda_analyzer.py:444 scripts/eda_analyzer.py:470-472 scripts/eda_analyzer.py:472-476 SKILL.md:41 SKILL.md:43 SKILL.md:48 SKILL.md:50 SKILL.md:55 SKILL.md:57 SKILL.md:62 SKILL.md:64 SKILL.md:69 SKILL.md:71 SKILL.md:76 SKILL.md:78 SKILL.md:92-98 SKILL.md:98-108 SKILL.md:108-112 SKILL.md:112-115 SKILL.md:115-123 SKILL.md:123-191 SKILL.md:191-196 SKILL.md:196-197 SKILL.md:197 SKILL.md:218-224 SKILL.md:224 SKILL.md:225 SKILL.md:231-240 SKILL.md:240-246 SKILL.md:246-270 SKILL.md:270-289 SKILL.md:289-293 SKILL.md:293-313 SKILL.md:313-317 SKILL.md:317-338 SKILL.md:338-349 SKILL.md:349-354 SKILL.md:354-357 SKILL.md:357 SKILL.md:358 SKILL.md:359 SKILL.md:360 SKILL.md:361 SKILL.md:383-385 SKILL.md:385-397 SKILL.md:397-432 SKILL.md:432-435 SKILL.md:435-436 SKILL.md:436-437 SKILL.md:437-438 SKILL.md:438-439 SKILL.md:439-440 SKILL.md:440-443

審計單位: codex 檢視審計紀錄 →

分享與引用此報告

分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。

開啟版本化報告

複製報告連結

https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis/audits/9?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report

Markdown 徽章

[![Skillstore security assessment](https://skillstore.io/badges/skills/k-dense-ai-exploratory-data-analysis/security.svg)](https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis?utm_source=security_passport_badge)

HTML 徽章

<a href="https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/k-dense-ai-exploratory-data-analysis/security.svg" alt="Skillstore security assessment" loading="lazy"></a>

嵌入卡片

<iframe src="https://skillstore.io/embed/skills/k-dense-ai-exploratory-data-analysis.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>

學術引用 (APA · BibTeX · CFF)

APA 引用

K-Dense-AI. (2026). exploratory-data-analysis security audit report (audit version 9) [Author version unspecified]. Skillstore. https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis/audits/9

BibTeX 引用

@techreport{k-dense-ai-k-dense-ai-exploratory-data-analysis-2026,
  author = {K-Dense-AI},
  title = {exploratory-data-analysis security audit report (audit version 9)},
  institution = {Skillstore},
  year = {2026},
  number = {9},
  url = {https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis/audits/9},
  note = {Author version unspecified}
}

CITATION.cff

cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "exploratory-data-analysis security audit report (audit version 9)"
version: "unspecified"
type: report
authors:
  - name: "K-Dense-AI"
date-released: "2026-07-09"
url: "https://skillstore.io/skills/k-dense-ai-exploratory-data-analysis/audits/9"
identifiers:
  - type: other
    value: "skillstore:k-dense-ai-exploratory-data-analysis:audit:9"
    description: "Skillstore immutable audit report identifier"

比較變體

2 個可安裝變體

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為何此變體排名第一

Skillstore 評分最高

作者 / 來源 Skillstore 評分證據置信度 Skillstore 使用量已更新

davila7 推薦

davila7-exploratory-data-analysis

Skillstore 評分 85

證據置信度高

Skillstore 使用量 70

已更新

2026-09-09

K-Dense-AI 目前

k-dense-ai-exploratory-data-analysis

Skillstore 評分 76

證據置信度高

Skillstore 使用量 47

已更新

2026-09-09

Skillstore 評分

評分依據證據置信度: 高

架構

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內容

社群

規範符合度

你可以打造什麼

剖析新的實驗資料集

在建模或視覺化前，檢查檔案結構、維度、缺失值與基本統計。

在 QC 前檢查定序檔案

摘要序列數量、長度分布、品質訊號，以及可能的下一步生物資訊流程。

為交接記錄資料

建立 markdown 摘要，說明檔案格式、中繼資料、品質疑慮與建議分析路徑。

試試這些提示

分析單一檔案

Analyze this scientific file: [path]. Identify the format, summarize metadata, assess data quality, and create an EDA report.

比較相關檔案

Compare these related data files: [paths]. Highlight shared structure, differences, missing data, and recommended preprocessing.

規劃下游分析

Review [path] for downstream analysis planning. Explain required libraries, sampling limits, quality concerns, and suitable visualization methods.

建立 EDA 工作流程

Build a reproducible EDA workflow for [dataset paths]. Include format-specific checks, cross-file consistency checks, report sections, and sharing risks.