csv-data-summarizer-claude-skill MCP — Cas d'usage, Installer & Démo en direct

Pourquoi l'utiliser

Fonctionnalités clés

Automatic CSV column profiling (dtype inference, null %, cardinality)
Summary statistics for numeric and categorical columns
Missing-data heatmap + per-column null counts
Quick visualizations: histograms for numerics, bar charts for categoricals
Python + pandas, runs in the local environment

Démo en direct

Aperçu en pratique

csv-data-summarizer-claude-skill.replay ▶ prêt

0/0

Installer

Choisissez votre client

~/Library/Application Support/Claude/claude_desktop_config.json · Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "csv-data-summarizer-claude-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill",
        "~/.claude/skills/csv-data-summarizer-claude-skill"
      ],
      "_inferred": true
    }
  }
}

Ouvrez Claude Desktop → Settings → Developer → Edit Config. Redémarrez après avoir enregistré.

~/.cursor/mcp.json · .cursor/mcp.json

{
  "mcpServers": {
    "csv-data-summarizer-claude-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill",
        "~/.claude/skills/csv-data-summarizer-claude-skill"
      ],
      "_inferred": true
    }
  }
}

Cursor utilise le même schéma mcpServers que Claude Desktop. La config projet l'emporte sur la globale.

VS Code → Cline → MCP Servers → Edit

{
  "mcpServers": {
    "csv-data-summarizer-claude-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill",
        "~/.claude/skills/csv-data-summarizer-claude-skill"
      ],
      "_inferred": true
    }
  }
}

Cliquez sur l'icône MCP Servers dans la barre latérale Cline, puis "Edit Configuration".

~/.codeium/windsurf/mcp_config.json

{
  "mcpServers": {
    "csv-data-summarizer-claude-skill": {
      "command": "git",
      "args": [
        "clone",
        "https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill",
        "~/.claude/skills/csv-data-summarizer-claude-skill"
      ],
      "_inferred": true
    }
  }
}

Même format que Claude Desktop. Redémarrez Windsurf pour appliquer.

~/.continue/config.json

{
  "mcpServers": [
    {
      "name": "csv-data-summarizer-claude-skill",
      "command": "git",
      "args": [
        "clone",
        "https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill",
        "~/.claude/skills/csv-data-summarizer-claude-skill"
      ]
    }
  ]
}

Continue utilise un tableau d'objets serveur plutôt qu'une map.

~/.config/zed/settings.json

{
  "context_servers": {
    "csv-data-summarizer-claude-skill": {
      "command": {
        "path": "git",
        "args": [
          "clone",
          "https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill",
          "~/.claude/skills/csv-data-summarizer-claude-skill"
        ]
      }
    }
  }
}

Ajoutez dans context_servers. Zed recharge à chaud à la sauvegarde.

claude mcp add csv-data-summarizer-claude-skill -- git clone https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill ~/.claude/skills/csv-data-summarizer-claude-skill

Une seule ligne. Vérifiez avec claude mcp list. Supprimez avec claude mcp remove.

Cas d'usage

Usages concrets : 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

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

Prérequis

Python 3.10+ with pandas and matplotlib — pip install pandas matplotlib
Skill cloned — git clone https://github.com/coffeefuelbump/csv-data-summarizer-claude-skill ~/.claude/skills/csv-data-summarizer-claude-skill

Déroulement

Point Claude at the file

Summarize data/sales_2024.csv — columns, nulls, and basic stats.✓ Copié

→ Per-column dtype, null %, min/max or top values
Ask for missing-data diagnosis

Which columns have systematic nulls? Any row-level patterns?✓ Copié

→ Specific columns flagged, not vague 'some nulls'
Request quick visuals

Save histograms for all numeric columns into ./charts/.✓ Copié

→ PNG files on disk

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

Pièges

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

Combiner avec : filesystem

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

👤 Data engineers validating incoming CSVs ⏱ ~30 min intermediate

Quand l'utiliser : You receive a daily CSV and want to flag anomalies before downstream steps.

Déroulement

Baseline the expected shape

Record the summary for last week's file as the baseline.✓ Copié

→ Baseline JSON saved
Compare today against baseline

Compare today's CSV to the baseline — flag null-rate shifts >5%, new/missing columns, dtype changes.✓ Copié

→ Diff report with specific anomalies
Fail loudly if bad

If any flags are critical, exit non-zero so CI fails.✓ Copié

→ Non-zero exit + readable reason

Résultat : Bad data caught before it pollutes the warehouse.

Pièges

Threshold too sensitive — noisy failures — Calibrate on 30 days of baselines before enforcing

Combiner avec : github

Combinaisons

Associez-le à d'autres MCPs pour un effet X10

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.✓ Copié

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.✓ Copié

Outils

Ce que ce MCP expose

Outil	Entrées	Quand appeler	Coût
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

Coût et limites

Coût d'exécution

Quota d'API: none
Tokens par appel: ~2–5k tokens per CSV (plus the data sample Claude reads)
Monétaire: free
Astuce: Pass nrows and a narrowed column list — don't feed Claude the whole 50MB file

Sécurité

Permissions, secrets, portée

Stockage des identifiants : none

Sortie de données : none — local pandas only

If the CSV contains PII, treat the generated charts and summaries as equally sensitive

Dépannage

Erreurs courantes et correctifs

ModuleNotFoundError: pandas

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

Vérifier : 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.

Vérifier : file -I path/to.csv

Charts look off for skewed data

Ask for log-scale or clipped histograms

Alternatives

csv-data-summarizer-claude-skill vs autres

Alternative	Quand l'utiliser	Compromis
jupyter	You want full interactive analysis, not just a profile	Heavier; needs a kernel
excel	The data is better in Excel (pivot tables, formulas)	Harder to automate

Plus

Ressources

📖 Lire le README officiel sur GitHub

🐙 Voir les issues ouvertes

🔍 Parcourir les 400+ serveurs MCP et Skills