csv-analyst

스타14

포크3

업데이트2026년 6월 13일 13:43

Analyzes CSV or spreadsheet data with statistics, charts, joins, and clear findings. Use when the user uploads CSV/XLSX data or asks for data analysis, cohort analysis, charts, or spreadsheet insight. Do NOT trigger for document-only research.

설치

Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.

출처

cheatcode-ai

cheatcode-ai/cheatcode

GitHub 저장소 열기 Creator 저장소 보기

다운로드

CSV Analyst

Analyze tabular data reproducibly. Profile first, then answer the user's business question with code-backed findings, charts, caveats, and exportable artifacts.

Quick Start

Locate the file with fs_list or ask for it if missing.
Use data_analyze_csv or focused Python via runCode to profile the file.
Inspect row count, columns, missingness, inferred types, and suspicious values.
Write focused, reproducible analysis code in /workspace/analysis/.
Generate charts only when they answer the question.

Analysis Workflow

Task	Default method
Basic profile	row count, column count, missingness, examples
Type cleanup	parse dates, numerics, booleans; preserve raw columns when risky
Trend analysis	group by date periods, include sample sizes
Cohorts	define cohort date explicitly, include retention denominators
Outliers	show detection rule and top examples
Joins	validate key uniqueness before joining

Rules

Never infer units silently. Ask if the unit changes the answer.
Keep generated code reproducible and saved in the sandbox.
Prefer simple charts: bar, line, scatter, heatmap. Avoid decorative charts.
Report data quality issues before conclusions.
When data is too small or biased, say so clearly.

QA

Re-run the final analysis code after edits.
Check chart labels, units, legends, and date sorting.
Include the exact file paths for generated artifacts.
Verify totals against the original row count after filters.

Deliverables

Findings with caveats
Reproducible analysis code
Charts or tables under /workspace/analysis/

References

reference.md - profiling checklist, chart selection, and common CSV failure modes.

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name	csv-analyst
description	Analyzes CSV or spreadsheet data with statistics, charts, joins, and clear findings. Use when the user uploads CSV/XLSX data or asks for data analysis, cohort analysis, charts, or spreadsheet insight. Do NOT trigger for document-only research.
category	Data & Media
tags	data, csv, analysis, charts
license	MIT
compatibility	Requires sandbox Python and data tools.

CSV Analyst

Analyze tabular data reproducibly. Profile first, then answer the user's business question with code-backed findings, charts, caveats, and exportable artifacts.

Quick Start

Locate the file with fs_list or ask for it if missing.
Use data_analyze_csv or focused Python via runCode to profile the file.
Inspect row count, columns, missingness, inferred types, and suspicious values.
Write focused, reproducible analysis code in /workspace/analysis/.
Generate charts only when they answer the question.

Analysis Workflow

Task	Default method
Basic profile	row count, column count, missingness, examples
Type cleanup	parse dates, numerics, booleans; preserve raw columns when risky
Trend analysis	group by date periods, include sample sizes
Cohorts	define cohort date explicitly, include retention denominators
Outliers	show detection rule and top examples
Joins	validate key uniqueness before joining

Rules

Never infer units silently. Ask if the unit changes the answer.
Keep generated code reproducible and saved in the sandbox.
Prefer simple charts: bar, line, scatter, heatmap. Avoid decorative charts.
Report data quality issues before conclusions.
When data is too small or biased, say so clearly.

QA

Re-run the final analysis code after edits.
Check chart labels, units, legends, and date sorting.
Include the exact file paths for generated artifacts.
Verify totals against the original row count after filters.

Deliverables

Findings with caveats
Reproducible analysis code
Charts or tables under /workspace/analysis/

References

reference.md - profiling checklist, chart selection, and common CSV failure modes.