mcpbeat

Data Analysis

lingzhi227/data-analysis

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.

5k tokens
context cost
the whole folder, loaded on every use
4
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
256
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository 
npx skills add https://github.com/lingzhi227/agent-research-skills --skill data-analysis

The instruction itself

14 sections, as written by the author

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:

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

Step 2: 4-Round Code Review

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

Related Skills

  • Upstream: experiment-code, experiment-design
  • Downstream: table-generation, figure-generation, backward-traceability
  • See also: math-reasoning

How to use it

Copy the folder

Take lingzhi227/data-analysis from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.