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data-analysis

该 Skill 面向实验数据统计分析：接收 CSV、JSON、pickle 或实验日志与研究目标，生成分节分析代码，按四轮提示复核代码、数据处理与表格结果，并用脚本汇总统计量、推荐检验、格式化 p 值；强调报告不确定性和结果与实际数据一致。

lingzhi227
热度 0.00
最近更新 2026-10-19 01:34:52
分类：数据依赖：代码执行 / 读写文件应用场景：数据处理 / 报告撰写 / 研究 / 代码生成 ...

安装技能

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执行命令安装此 skill： npx secure-skills-cli add lingzhi227/agent-research-skills/data-analysis
Metadata
name
data-analysis
description
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
argument-hint
["data-source"]

Data Analysis

Generate rigorous statistical analysis code with multi-round review.

Input

  • $0 — Data source (CSV, JSON, pickle, or experiment logs)
  • $1 — Research goal or hypothesis to test

References

  • 4-round code review prompts: ~/.claude/skills/data-analysis/references/review-prompts.md

Scripts

Statistical summary and comparison

python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --compare method --metric accuracy --output summary.json
python ~/.claude/skills/data-analysis/scripts/stat_summary.py --input results.csv --describe

Detects data types, recommends tests, runs comparisons, outputs effect sizes and significance stars. Requires numpy, scipy.

Format p-values

python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --values "0.001 0.05 0.23" --format stars
python ~/.claude/skills/data-analysis/scripts/format_pvalue.py --csv results.csv --column pvalue --format latex

Formats p-values with stars, LaTeX notation, or plain text. Stdlib-only.

Workflow

Step 1: Generate Analysis Code

Structure the code with these sections:

  1. # IMPORT — pandas, numpy, scipy, statsmodels, sklearn
  2. # LOAD DATA — Load from original data files
  3. # DATASET PREPARATIONS — Missing values, units, exclusion criteria
  4. # DESCRIPTIVE STATISTICS — Summary tables if needed
  5. # PREPROCESSING — Dummy variables, normalization
  6. # ANALYSIS — Statistical tests per hypothesis
  7. # SAVE ADDITIONAL RESULTS — Extra results to pickle

Step 2: 4-Round Code Review

  1. Round 1 — Code Flaws: Mathematical/statistical errors, wrong calculations, trivial tests
  2. Round 2 — Data Handling: Missing values, units, preprocessing, test choice
  3. Round 3 — Per-Table: Sensible values, measures of uncertainty, missing data
  4. Round 4 — Cross-Table: Completeness, consistency, missing variables

Step 3: Produce Results

  • Every nominal value must have uncertainty (CI, STD, or p-value)
  • Statistical tests must be appropriate for the data type
  • Results must match actual data — never hallucinate

Allowed Packages

pandas, numpy, scipy, statsmodels, sklearn, pickle

Statistical Test Selection

Data Type Test
Two groups, normal Independent t-test
Two groups, non-normal Mann-Whitney U
Paired samples Paired t-test / Wilcoxon
Multiple groups ANOVA / Kruskal-Wallis
Categorical Chi-square / Fisher's exact
Correlation Pearson / Spearman
Regression OLS / Logistic / Mixed effects

Rules

  • Always report p-values for statistical tests
  • Account for relevant confounding variables
  • Use inherent package functionality (e.g., formula = "y ~ a * b" for interactions)
  • Do not manually implement available statistical functions
  • Access dataframes using string-based column names, not integer indices

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