Install command
Provided
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 the source and read safety notes before installing.
Source-backed facts for citing this resource, derived directly from the registry — also available as plain text for AI assistants.
Decision playbook
Signals are comparatively strong, but you should still validate source, privacy posture, and package provenance for your environment.
0
96
—
No baseline selected
No major trust-signal divergence detected in the current selection.
Confirm ownership and provenance before trusting install instructions.
Source link availableRequired
Open the canonical repository and verify ownership.
Source provenance statusRequired
Marked as first-party.
Metadata reviewed
Registry metadata indicates a reviewed listing.
Validate risk disclosures before installation or API wiring.
Safety notes presentRequired
Review the listed safety guidance before running commands.
Privacy notes presentRequired
Review data handling notes before connecting accounts or secrets.
Trust level risk gateRequired
Trust level does not block evaluation.
Check package metadata and artifact integrity signals.
Install payload available
Install or copy payload is available for review.
Package verification flag
Package marked verified.
Checksum metadata
SHA-256 hash is present.
Use compare context to validate trade-offs before adoption.
Compare tray has multiple entries
Add at least one more entry to compare trust differences.
Baseline comparison available
No baseline peer selected yet.
Diverging trust signals identified
No major trust-signal divergence found.
Setup at a glance
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
Current risk score 0/100. Use staged verification before broader rollout.
Validate source and review signals before any execution.
Confirm source provenanceRequired
Source URL/provenance metadata is present.
Confirm metadata review state
Listing has review metadata.
Verify install payload
Install/config payload exists and can be inspected.
Confirm safety, privacy, and package integrity signals.
Review safety notesRequired
Safety notes are present.
Review privacy notesRequired
Privacy notes are present.
Verify package integrity metadata
Package verification/checksum metadata is available.
Adopt in controlled steps based on the selected plan.
Run in isolated sandbox firstRequired
Use a constrained sandbox and observe behavior across multiple tasks.
Roll out graduallyRequired
Roll out to a small cohort before wider usage.
Set monitoring and fallback
Define rollback path and monitor errors after adoption.
Evidence readiness
Required evidence gates are covered (6/6 signals complete).
Source repository/provenance is listed.
Required in this preset
Review metadata is present.
Required in this preset
Safety notes are present.
Required in this preset
Privacy notes are present.
Optional in this preset
Package integrity metadata is present.
Optional in this preset
Install payload is available.
Required in this preset
Required evidence gates are covered for this preset.
Decision timeline
6/6 steps complete with no blocking gaps for this preset.
triage
Source/provenance metadata is available.
triage
Review metadata is available.
verify
Safety notes are available.
verify
Privacy notes are available.
verify
Package integrity metadata is available.
rollout
Install payload is available.
No required blockers for this timeline preset.
Prerequisite readiness
6 prerequisites to line up before setup.
Safety & privacy surface
1 safety and 1 privacy notes across 2 risk areas. Review closely: network access.
| Platform | Support | Install path |
|---|---|---|
| claude-code | Native | .claude/skills/<skill-name>/SKILL.md |
| codex | Native | .agents/skills/<skill-name>/SKILL.md |
| windsurf | Native | .windsurf/skills/<skill-name>/SKILL.md |
| gemini | Native | .gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md |
| cursor | Adapter | .cursor/rules/<skill-name>.mdc |
| cli | Manual | AGENTS.md or tool-specific context file |
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)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.
SKILL.md.SKILL.md content as reusable workflow instructions..gemini/skills/<skill-name>/SKILL.md or .agents/skills/<skill-name>/SKILL.md where supported..cursor/rules/*.mdc adapter for project rules.Required:
What Claude handles:
Prompt: "Clean this CSV file: remove duplicates, fix missing values, standardize column names, and export as clean.csv"
Claude will:
Prompt: "Merge customers.csv and orders.csv on customer_id. Show me the combined data and export as customer_orders.xlsx"
Claude will:
Prompt: "Analyze this sales data: show me summary statistics, identify top products, calculate monthly trends, and create a pivot table by region."
Claude will:
Prompt: "Convert this Excel workbook to CSV files, one per sheet, with UTF-8 encoding."
Claude will:
"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"
"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"
"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"
"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"
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"
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 status | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed | ReviewedMaintainer reviewed |
| Package trustDiffers | Package verified2025-10-15 | Package verified2025-10-15 | Package not verified | Package not verified |
| Source provenanceDiffers | Source-backed | No submission link | Source-backed | Source-backed |
| Submitter | — | — | — | — |
| Install risk | Review first | Low risk | Review first | Review first |
| Notes | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ | Safety ✓ Privacy ✓ |
| Brand | — | — | — | |
| Category | skills | skills | skills | skills |
| Source | first-party | first-party | source-backed | source-backed |
| Author | JSONbored | JSONbored | wanshuiyin | BrowserAct |
| Added | 2025-10-15 | 2025-10-15 | 2026-06-18 | 2026-06-18 |
| Platforms | Claude CodeCodexWindsurfGeminiCursorCLI | Claude CodeCodexWindsurfGeminiCursorCLI | Claude CodeCodexWindsurfGeminiCursorCLI | Claude CodeCodexWindsurfGeminiCursorCLIVS Code |
| Source repo | — | — | — | — |
| 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. | ✓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. |
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| Config | — | — | — | — |
| Citations | ||||
| Claim | Unclaimed | Unclaimed | Unclaimed | Unclaimed |
Source-backed guides for putting this to work.
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