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CSV/Excel Data Wrangler Skill

Clean, filter, join, pivot, and export CSV/XLSX data reliably with reproducible steps. Transform messy spreadsheets into production-ready datasets with pandas. Handle encoding issues, data type conversion, missing values, duplicates, and complex merges.

HarnessClaude CodeCodexWindsurfGeminiCursorCLI
Level:advancedType:generalVerified:draft
Review first review before installing

Open the source and read safety notes before installing.

Citation facts

Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.

Source URLs
https://pandas.pydata.org/, https://github.com/JSONbored/awesome-claude/blob/main/content/skills/csv-excel-data-wrangler.mdx
Package URL
/downloads/skills/csv-excel-data-wrangler.zip
Package SHA256
7a075dd66f661d1f928dff7924159040bd6ab2c898f782cbb2862ae79658178e
Safety notes
Installs Python packages (pip install pandas openpyxl) and runs scripts that read and write data files, overwriting outputs at the target path; review transformations before running on important data.
Privacy notes
Claude Pro / Code Interpreter workflows require uploading CSV or Excel files to the Claude conversation for remote processing; local pandas runs read files on your machine. Generated outputs, logs, and conversation history can contain values from your source data — review before sharing.
Platform compatibility
claude-code (native-skill), codex (native-skill), windsurf (native-skill), gemini (native-skill), cursor (adapter), cli (manual-context)
Author
JSONbored
Claim status
unclaimed
Last verified
2025-10-15

Decision playbook

Ready to evaluate for your workflow

Signals are comparatively strong, but you should still validate source, privacy posture, and package provenance for your environment.

Compare context
Selected

0

Current score

96

Baseline

Delta

No baseline selected

No major trust-signal divergence detected in the current selection.

Source and provenance checks

Complete

Confirm ownership and provenance before trusting install instructions.

  • Source link availableRequired

    Open the canonical repository and verify ownership.

    Done
  • Source provenance statusRequired

    Marked as first-party.

    Done
  • Metadata reviewed

    Registry metadata indicates a reviewed listing.

    Done

Safety and privacy checks

Complete

Validate risk disclosures before installation or API wiring.

  • Safety notes presentRequired

    Review the listed safety guidance before running commands.

    Done
  • Privacy notes presentRequired

    Review data handling notes before connecting accounts or secrets.

    Done
  • Trust level risk gateRequired

    Trust level does not block evaluation.

    Done

Package and install checks

Complete

Check package metadata and artifact integrity signals.

  • Install payload available

    Install or copy payload is available for review.

    Done
  • Package verification flag

    Package marked verified.

    Done
  • Checksum metadata

    SHA-256 hash is present.

    Done

Compare-driven decision checks

Needs review

Use compare context to validate trade-offs before adoption.

  • Compare tray has multiple entries

    Add at least one more entry to compare trust differences.

    Pending
  • Baseline comparison available

    No baseline peer selected yet.

    Pending
  • Diverging trust signals identified

    No major trust-signal divergence found.

    Pending

Setup at a glance

Package install

Copy-ready — paste the snippet to get started.

Install command

Provided

Config snippet

Not provided

Copy snippet

Provided

Prerequisites

6 to clear

Platforms

6 listed

Difficulty

94/100

Adoption plan

Balanced adoption plan

Current risk score 0/100. Use staged verification before broader rollout.

Risk 0

Pre-adoption checks

Validate source and review signals before any execution.

  • Confirm source provenanceRequired

    Source URL/provenance metadata is present.

    Done
  • Confirm metadata review state

    Listing has review metadata.

    Done
  • Verify install payload

    Install/config payload exists and can be inspected.

    Done

Security checks

Confirm safety, privacy, and package integrity signals.

  • Review safety notesRequired

    Safety notes are present.

    Done
  • Review privacy notesRequired

    Privacy notes are present.

    Done
  • Verify package integrity metadata

    Package verification/checksum metadata is available.

    Done

Rollout

Adopt in controlled steps based on the selected plan.

  • Run in isolated sandbox firstRequired

    Use a constrained sandbox and observe behavior across multiple tasks.

    Pending
  • Roll out graduallyRequired

    Roll out to a small cohort before wider usage.

    Pending
  • Set monitoring and fallback

    Define rollback path and monitor errors after adoption.

    Pending

Evidence readiness

Evidence readiness matrix · balanced

Required evidence gates are covered (6/6 signals complete).

Risk 0

Source provenance

Present

Source repository/provenance is listed.

Required in this preset

Metadata review

Present

Review metadata is present.

Required in this preset

Safety notes

Present

Safety notes are present.

Required in this preset

Privacy notes

Present

Privacy notes are present.

Optional in this preset

Package integrity

Present

Package integrity metadata is present.

Optional in this preset

Install payload

Present

Install payload is available.

Required in this preset

Required evidence gates are covered for this preset.

Decision timeline

Decision timeline · balanced

6/6 steps complete with no blocking gaps for this preset.

Risk 0

triage

Confirm source provenanceRequired

Source/provenance metadata is available.

Done

triage

Check metadata review statusRequired

Review metadata is available.

Done

verify

Review safety notesRequired

Safety notes are available.

Done

verify

Review privacy notes

Privacy notes are available.

Done

verify

Validate package integrity metadata

Package integrity metadata is available.

Done

rollout

Verify install payload and commandsRequired

Install payload is available.

Done

No required blockers for this timeline preset.

Prerequisite readiness

Prerequisite readiness

6 prerequisites to line up before setup.

0/6 ready
Install & runtime1General5

Safety & privacy surface

Safety & privacy surface

1 safety and 1 privacy notes across 2 risk areas. Review closely: network access.

2 areas
  • SafetyLocal filesInstalls Python packages (pip install pandas openpyxl) and runs scripts that read and write data files, overwriting outputs at the target path; review transformations before running on important data.
  • PrivacyNetwork accessClaude Pro / Code Interpreter workflows require uploading CSV or Excel files to the Claude conversation for remote processing; local pandas runs read files on your machine. Generated outputs, logs, and conversation history can contain values from your source data — review before sharing.

Safety notes

  • Installs Python packages (pip install pandas openpyxl) and runs scripts that read and write data files, overwriting outputs at the target path; review transformations before running on important data.

Privacy notes

  • Claude Pro / Code Interpreter workflows require uploading CSV or Excel files to the Claude conversation for remote processing; local pandas runs read files on your machine. Generated outputs, logs, and conversation history can contain values from your source data — review before sharing.

Prerequisites

  • Python 3.11+
  • pandas
  • openpyxl
  • pyarrow (optional for Parquet)
  • File system read/write access for input CSV/Excel files and output processed files
  • Sufficient memory for data processing (minimum 2GB RAM recommended for large files, use chunked processing for files >100MB)

Schema details

Install type
package
Reading time
4 min
Difficulty score
94
Troubleshooting
Yes
Breaking changes
No
Package metadata
Package verified
Yes
SHA-256
7a075dd66f661d1f928dff7924159040bd6ab2c898f782cbb2862ae79658178e
Skill and platform metadata
Skill type
general
Skill level
advanced
Verification
draft
Verified at
2025-10-15
Retrieval sources
https://pandas.pydata.org/
Tested platforms
ClaudeCodexOpenClawCursorWindsurfGemini
PlatformSupportInstall path
claude-codeNative.claude/skills/<skill-name>/SKILL.md
codexNative.agents/skills/<skill-name>/SKILL.md
windsurfNative.windsurf/skills/<skill-name>/SKILL.md
geminiNative.gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md
cursorAdapter.cursor/rules/<skill-name>.mdc
cliManualAGENTS.md or tool-specific context file
Full copyable content
import pandas as pd

customers = pd.read_csv('customers.csv', dtype=str)
orders = pd.read_excel('orders.xlsx')

# Normalize and dedupe
customers['email'] = customers['email'].str.strip().str.lower()
customers = customers.drop_duplicates(subset=['email'])

# Join and summarize
df = orders.merge(customers, on='customer_id', how='left')
sales_by_region = df.groupby('region', dropna=False)['total'].sum().reset_index()

sales_by_region.to_excel('sales_by_region.xlsx', index=False)

About this resource

What This Skill Enables

Claude can clean, transform, analyze, and merge CSV and Excel files with pandas. Upload messy spreadsheets and get production-ready data pipelines, statistical summaries, and formatted exports.

Compatibility

Native

  • Claude Code / Claude: native skill usage via SKILL.md.
  • Codex/OpenAI workflows: compatible with Agent Skills-style SKILL.md content as reusable workflow instructions.

Manual Adaptation

  • Gemini CLI: native skill usage via .gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md where supported.
  • Cursor: use the generated .cursor/rules/*.mdc adapter for project rules.
  • OpenClaw and similar agents: use the same skill content as a reusable prompt/workflow file when native skill import is unavailable.

Prerequisites

Required:

  • Claude Pro subscription
  • Code Interpreter feature enabled
  • CSV or Excel file uploaded to conversation

What Claude handles:

  • Installing pandas, openpyxl, and data processing libraries
  • Detecting file encodings and formats
  • Type inference and conversion
  • Memory-efficient processing of large files

How to Use This Skill

Quick Data Cleaning

Prompt: "Clean this CSV file: remove duplicates, fix missing values, standardize column names, and export as clean.csv"

Claude will:

  1. Load and analyze the file structure
  2. Identify data quality issues
  3. Apply cleaning transformations
  4. Export cleaned version

Data Merging & Joining

Prompt: "Merge customers.csv and orders.csv on customer_id. Show me the combined data and export as customer_orders.xlsx"

Claude will:

  1. Load both files
  2. Detect join keys
  3. Perform the merge (inner/left/right/outer)
  4. Validate results
  5. Export formatted Excel file

Data Analysis & Summaries

Prompt: "Analyze this sales data: show me summary statistics, identify top products, calculate monthly trends, and create a pivot table by region."

Claude will:

  1. Generate descriptive statistics
  2. Perform aggregations
  3. Create pivot tables
  4. Calculate trends
  5. Present insights

Format Conversion

Prompt: "Convert this Excel workbook to CSV files, one per sheet, with UTF-8 encoding."

Claude will:

  1. Read all Excel sheets
  2. Export each as separate CSV
  3. Handle encoding properly
  4. Preserve data types where possible

Common Workflows

CRM Data Cleanup

"Clean this customer export:
1. Remove duplicate emails (keep most recent)
2. Standardize phone numbers to (NNN) NNN-NNNN format
3. Fill missing company names with 'Unknown'
4. Split full_name into first_name and last_name
5. Export as customers_clean.xlsx"

Sales Report Generation

"Analyze this sales data:
1. Calculate total revenue by product category
2. Identify top 10 customers by revenue
3. Show month-over-month growth
4. Create a pivot table: rows=salesperson, columns=month, values=revenue
5. Export summary as sales_report.xlsx with formatted numbers"

Data Validation

"Validate this CSV:
1. Check for duplicate IDs
2. Identify rows with missing required fields (name, email, phone)
3. Flag invalid email formats
4. Report data quality issues
5. Export clean rows and error rows separately"

Multi-File Consolidation

"Combine all CSV files I upload into one master file:
1. Ensure columns match (add missing ones)
2. Add a 'source_file' column
3. Remove duplicates across all files
4. Sort by date column
5. Export as consolidated_data.csv"

Tips for Best Results

  1. Be Specific About Columns: Name the exact columns you want to work with
  2. Describe Your Data: Mention what each column represents for better context
  3. Specify Output Format: Tell Claude exactly how you want the result formatted
  4. Handle Missing Data: Be explicit about how to handle nulls (drop, fill with value, forward-fill, etc.)
  5. Large Files: For files >100MB, ask Claude to process in chunks or sample first
  6. Date Formats: Specify your expected date format (MM/DD/YYYY vs DD/MM/YYYY)
  7. Encoding Issues: If you see garbled text, ask Claude to try different encodings (UTF-8, latin-1, etc.)

Advanced Operations

Complex Transformations

  • Unpivoting (melt) wide data to long format
  • Creating calculated columns with business logic
  • Grouping and aggregating with custom functions
  • Handling multi-index data
  • Time series resampling and rolling windows

Data Quality Checks

  • Outlier detection and reporting
  • Referential integrity validation
  • Format consistency checks
  • Statistical anomaly detection

Troubleshooting

Issue: File encoding errors or garbled characters Solution: Ask Claude to detect encoding or try: "Read this with UTF-8-SIG encoding" or "Try latin-1 encoding"

Issue: Memory errors on large files Solution: "Process this file in 10,000 row chunks" or "Sample 10% of rows first to test"

Issue: Wrong data types (dates as strings, numbers as text) Solution: Be explicit: "Convert created_at column to datetime" or "Cast price to float"

Issue: Merge produces unexpected results Solution: Ask Claude to show sample rows before/after merge and explain the join type used

Issue: Excel export loses formatting Solution: "Export with formatted numbers, bold headers, and auto-column-width"

Learn More

Features

  • Import/export with explicit schema control
  • Deduplicate and null-safe transformations
  • Join/merge/pivot with predictable results
  • Encoding-aware IO with UTF-8/UTF-8-SIG handling
  • Parquet round-trips for performance
  • Memory-efficient processing for large files (chunksize, Parquet)
  • Data validation and quality checks (outlier detection, referential integrity)
  • Multi-file consolidation and batch processing

Use Cases

  • Clean messy CRM exports
  • Join sales and marketing datasets
  • Generate analyst-ready summary tables
  • Data migration and format conversion workflows
  • ETL pipelines for business intelligence
  • Automated data quality reporting and validation

Source citations

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How it compares

CSV/Excel Data Wrangler Skill side by side with 3 alternatives on trust, install, platform support, and disclosed safety notes — all from reviewed registry metadata.

2 trust signals differ across this comparison (Package trust, Source provenance).

Field

Clean, filter, join, pivot, and export CSV/XLSX data reliably with reproducible steps. Transform messy spreadsheets into production-ready datasets with pandas. Handle encoding issues, data type conversion, missing values, duplicates, and complex merges.

Open dossier

Turn CSV, JSON, or Excel data into publication-ready charts with Python: load it with pandas and render bar, line, scatter, and statistical plots in matplotlib (with seaborn styling), then export high-DPI PNG or SVG.

Open dossier

ARIS is a Markdown-only skill workflow pack for autonomous ML research agents, with idea discovery, experiment planning, auto-review loops, paper writing, rebuttal, resubmission, slides, posters, Research Wiki, and cross-model reviewer workflows for Claude Code, Codex, OpenClaw, Cursor, and other agent hosts.

Open dossier

MIT-licensed BrowserAct Agent Skill pack for installing and operating the `browser-act` browser automation CLI from Claude Code, Codex, OpenClaw, Cursor, OpenCode, Windsurf, Gemini CLI, and other skills-compatible agents.

Open dossier
Next steps
Trust
Review statusReviewedMaintainer reviewedReviewedMaintainer reviewedReviewedMaintainer reviewedReviewedMaintainer reviewed
Package trustDiffersPackage verified2025-10-15Package verified2025-10-15Package not verifiedPackage not verified
Source provenanceDiffersSource-backedNo submission linkSource-backedSource-backed
Submitter
Install riskReview firstLow riskReview firstReview first
Notes Safety Privacy Safety Privacy Safety Privacy Safety Privacy
BrandCursor logoCursor
Categoryskillsskillsskillsskills
Sourcefirst-partyfirst-partysource-backedsource-backed
AuthorJSONboredJSONboredwanshuiyinBrowserAct
Added2025-10-152025-10-152026-06-182026-06-18
Platforms
Claude CodeCodexWindsurfGeminiCursorCLI
Claude CodeCodexWindsurfGeminiCursorCLI
Claude CodeCodexWindsurfGeminiCursorCLI
Claude CodeCodexWindsurfGeminiCursorCLIVS Code
Source repo
Safety notesInstalls Python packages (pip install pandas openpyxl) and runs scripts that read and write data files, overwriting outputs at the target path; review transformations before running on important data.This skill installs Python plotting libraries (matplotlib, seaborn, pandas) or the maintainer-built package, runs visualization scripts, and writes image files (PNG/SVG) to disk, overwriting any existing file at the output path.ARIS skills can guide agents through code changes, experiment planning, experiment execution, paper drafting, rebuttal drafting, and cross-model review loops; treat those workflows as high-impact research automation rather than passive documentation. The `research-pipeline` skill supports auto-proceed modes and reviewer loops. Keep expensive runs, repository mutations, cloud/GPU jobs, and paper-submission decisions behind explicit human approval. Cross-model review through Codex MCP, Claude-review, Gemini-review, or similar reviewer adapters is a quality-control signal, not scientific proof or peer review. Generated claims, citations, tables, plots, ablations, rebuttals, and paper text need source checks, experiment audits, citation audits, and human scientific review before being relied on or submitted. Review all copied skills, scripts, MCP server configuration, and reviewer routing before installing them into a sensitive repository or giving them shell, file, web, cloud, or GPU access.BrowserAct can open pages, click, type, upload files, inspect state, capture screenshots, read page text, handle dialogs, export cookies, capture network requests, and operate logged-in browser sessions. Use BrowserAct only on sites, accounts, and data sources where the user has authorization. Do not use it to evade access controls, violate site terms, scrape disallowed data, or bypass rate limits. The entry skill declares confirmation gates for browser creation, deletion, local Chrome profile import, proxy/security changes, logins, form submissions, file uploads, and other sensitive operations; preserve those gates in agent workflows. `solve-captcha` may send the challenge image to BrowserAct's verification-assistance service according to the skill metadata; do not use it with sensitive or unauthorized pages. `remote-assist` can generate a live handoff URL for a human to take over. Treat that URL as access to the active browser session. Skill Forge can generate reusable automation skills from explored sites. Review generated scripts, selectors, network assumptions, output schemas, and site authorization before reusing them at scale.
Privacy notesClaude Pro / Code Interpreter workflows require uploading CSV or Excel files to the Claude conversation for remote processing; local pandas runs read files on your machine. Generated outputs, logs, and conversation history can contain values from your source data — review before sharing.Your data files (CSV/JSON/Excel) are read and rendered locally; nothing leaves the machine beyond the initial package downloads, but generated charts can embed values from the source data.Research automation can expose unpublished hypotheses, paper drafts, peer-review text, datasets, logs, source code, experiment traces, model outputs, reviewer comments, account names, and GPU or cloud configuration to the selected model providers and MCP tools. Cross-model review loops may send the same research artifact to multiple providers or local/remote reviewer services depending on configuration. Research Wiki, traces, generated reports, paper artifacts, and run logs can persist confidential results or private review material on disk. Do not share confidential reviews, unreleased findings, private datasets, credentials, proprietary code, or submission-sensitive artifacts with external services unless the research and account policies allow it.BrowserAct workflows can expose page content, screenshots, URLs, credentials typed into forms, cookies, browser profiles, uploaded files, downloaded files, network requests, HAR data, session names, browser descriptions, and logs. The BrowserAct skill metadata states that cookies, login sessions, page content, credentials, and browser profile data stay local, except the CAPTCHA challenge image when `solve-captcha` is invoked. Chrome-direct and profile import workflows can connect agents to existing local browser state. Treat those modes as account access, not a blank test browser. Log reports, feedback, Discord support, generated Skill Forge packages, and shared screenshots can leak private browsing or account context if submitted without review. Managed proxy, stealth browser, and API-key features create additional BrowserAct service dependencies beyond local CLI execution.
Prerequisites
  • Python 3.11+
  • pandas
  • openpyxl
  • pyarrow (optional for Parquet)
  • Python 3.11+ or Node.js 18+
  • matplotlib/seaborn or vega/vega-lite
  • Data file in supported format (CSV, JSON, Excel)
  • Code Interpreter enabled (for Python path)
  • A research project, ML paper idea, baseline repository, dataset, review packet, or experiment plan that is appropriate for agent-assisted research automation.
  • A compatible agent host that can consume Markdown skills, such as Claude Code, Codex, Cursor, OpenClaw, Antigravity, Trae, GitHub Copilot CLI, or a manual prompt workflow.
  • Model-provider credentials, MCP reviewer configuration, or local model routing only when using cross-model review or external reviewer loops.
  • Compute budget, GPU quota, experiment sandboxing, version control, and artifact directories before allowing autonomous experiment execution.
  • Python 3.12 or newer and the uv package manager for the documented CLI install path.
  • A compatible agent host that can read `SKILL.md` files and execute shell commands.
  • Chrome or Chromium for local `chrome` and `chrome-direct` browser modes.
  • A BrowserAct API key only for optional stealth browsers, stealth extraction, managed proxies, and CAPTCHA assistance.
Install
pip install pandas openpyxl
npm i vega vega-lite
git clone https://github.com/wanshuiyin/Auto-claude-code-research-in-sleep.git
uv tool install browser-act-cli --python 3.12
Config
Citations
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