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.
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.
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)
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:
Load and analyze the file structure
Identify data quality issues
Apply cleaning transformations
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:
Load both files
Detect join keys
Perform the merge (inner/left/right/outer)
Validate results
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:
Generate descriptive statistics
Perform aggregations
Create pivot tables
Calculate trends
Present insights
Format Conversion
Prompt: "Convert this Excel workbook to CSV files, one per sheet, with UTF-8 encoding."
Claude will:
Read all Excel sheets
Export each as separate CSV
Handle encoding properly
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
Be Specific About Columns: Name the exact columns you want to work with
Describe Your Data: Mention what each column represents for better context
Specify Output Format: Tell Claude exactly how you want the result formatted
Handle Missing Data: Be explicit about how to handle nulls (drop, fill with value, forward-fill, etc.)
Large Files: For files >100MB, ask Claude to process in chunks or sample first
Date Formats: Specify your expected date format (MM/DD/YYYY vs DD/MM/YYYY)
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"
Show that CSV/Excel Data Wrangler Skill is listed on HeyClaude. Paste this Markdown into your README — it renders the badge and links back to this page.
[](https://heyclau.de/entry/skills/csv-excel-data-wrangler)
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).
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.
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.
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.
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.
✓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.
✓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 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.
✓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.