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csv-data-summarizer-claude-skill

por coffeefuelbump · coffeefuelbump/csv-data-summarizer-claude-skill

Drop a CSV, get summary stats, missing-data flags, and quick charts — without writing pandas.

A focused Claude skill that auto-activates on CSV files: it profiles the columns (dtype, null %, uniques), prints summary statistics, flags missing-data patterns, and generates a few quick visualizations using pandas and matplotlib. Designed as a good first-look tool before deeper analysis.

▶ Ver demo 📦 Instalar 💡 Casos de uso

Por que usar

Principais recursos

Demo ao vivo

Como fica na prática

csv-data-summarizer-claude-skill.replay ▶ pronto
0/0

Instalação

Repositório do autor · Documentação oficial de Skills do Claude

Casos de uso

Usos do mundo real: csv-data-summarizer-claude-skill

How to get a first look at a new CSV without writing pandas

👤 Analysts, PMs, anyone inheriting someone else's data ⏱ ~10 min beginner

Quando usar: You got handed a CSV and need to understand what's in it before doing anything else.

Fluxo
  1. Point Claude at the file
    Summarize data/sales_2024.csv — columns, nulls, and basic stats.✓ Copiado
    → Per-column dtype, null %, min/max or top values
  2. Ask for missing-data diagnosis
    Which columns have systematic nulls? Any row-level patterns?✓ Copiado
    → Specific columns flagged, not vague 'some nulls'
  3. Request quick visuals
    Save histograms for all numeric columns into ./charts/.✓ Copiado
    → PNG files on disk

Resultado: A one-page mental model of the dataset in under 5 minutes.

Armadilhas
  • Huge CSVs blow the token budget — Have Claude read a sample (nrows=10000) for exploration, run stats on the full file in Python
  • Dtype misinference (dates read as strings) — Tell Claude which columns are dates — pandas parse_dates helps
Combine com: filesystem

Use the summarizer as a data-quality gate in a pipeline

👤 Data engineers validating incoming CSVs ⏱ ~30 min intermediate

Quando usar: You receive a daily CSV and want to flag anomalies before downstream steps.

Fluxo
  1. Baseline the expected shape
    Record the summary for last week's file as the baseline.✓ Copiado
    → Baseline JSON saved
  2. Compare today against baseline
    Compare today's CSV to the baseline — flag null-rate shifts >5%, new/missing columns, dtype changes.✓ Copiado
    → Diff report with specific anomalies
  3. Fail loudly if bad
    If any flags are critical, exit non-zero so CI fails.✓ Copiado
    → Non-zero exit + readable reason

Resultado: Bad data caught before it pollutes the warehouse.

Armadilhas
  • Threshold too sensitive — noisy failures — Calibrate on 30 days of baselines before enforcing
Combine com: github

Combinações

Combine com outros MCPs para 10× de alavancagem

csv-data-summarizer-claude-skill + filesystem

Run against a directory of CSVs and save each report next to the file

For every CSV under data/, save a matching *_profile.md file summarizing it.✓ Copiado
csv-data-summarizer-claude-skill + jupyter

Drop the profile into a notebook for further exploration

Take the profile and create a Jupyter notebook with the loaded DataFrame and starter cells.✓ Copiado

Ferramentas

O que este MCP expõe

FerramentaEntradasQuando chamarCusto
csv-profile path to CSV First step on any new CSV pandas runtime
missing-data-report profile After profiling 0
quick-charts profile + output dir When you want visuals alongside stats matplotlib runtime

Custo e limites

O que custa rodar

Cota de API
none
Tokens por chamada
~2–5k tokens per CSV (plus the data sample Claude reads)
Monetário
free
Dica
Pass nrows and a narrowed column list — don't feed Claude the whole 50MB file

Segurança

Permissões, segredos, alcance

Armazenamento de credenciais: none
Saída de dados: none — local pandas only

Solução de problemas

Erros comuns e correções

ModuleNotFoundError: pandas

pip install pandas matplotlib in the env where Claude Code runs scripts.

Verificar: python -c 'import pandas, matplotlib; print(pandas.__version__)'
UnicodeDecodeError on load

Tell Claude the encoding (e.g. 'encoding=latin-1') or normalize the file first.

Verificar: file -I path/to.csv
Charts look off for skewed data

Ask for log-scale or clipped histograms

Alternativas

csv-data-summarizer-claude-skill vs. outros

AlternativaQuando usarTroca
jupyterYou want full interactive analysis, not just a profileHeavier; needs a kernel
excelThe data is better in Excel (pivot tables, formulas)Harder to automate

Mais

Recursos

📖 Leia o README oficial no GitHub

🐙 Ver issues abertas

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