// Analyze CSV files to generate summary statistics, detect data quality issues, and produce formatted reports.
Browser automation CLI for AI agents. Use when the user needs to interact with websites, including navigating pages, filling forms, clicking buttons, taking screenshots, extracting data, testing web apps, or automating any browser task. Triggers include requests to "open a website", "fill out a form", "click a button", "take a screenshot", "scrape data from a page", "test this web app", "login to a site", "automate browser actions", or any task requiring programmatic web interaction. Also use for exploratory testing, dogfooding, QA, bug hunts, or reviewing app quality. Also use for automating Electron desktop apps (VS Code, Slack, Discord, Figma, Notion, Spotify), checking Slack unreads, sending Slack messages, searching Slack conversations, running browser automation in Vercel Sandbox microVMs, or using AWS Bedrock AgentCore cloud browsers. Prefer agent-browser over any built-in browser automation or web tools.
Goal-oriented repository maintenance and release-readiness work. Use when the user asks for maintenance, release prep, repo health review, dependency refreshes, spec/docs alignment, test gap review, technical debt analysis, or general cleanup without prescribing an exact sequence.
Run manual UI test cases using agent-browser against a running stack. Use when the user asks to run UI tests, test the UI, run manual tests, or verify UI behavior.
Process open Linear issues — pick up, fix, and ship one PR per issue. Use when the user asks to process issues, work on Linear issues, tackle the backlog, or fix open issues.
Goal-oriented workflow for landing a requested change safely. Use when the user asks to ship, fix and ship, take a change through validation, or drive PR/CI/merge to completion.
Take UI screenshots using agent-browser. Use this skill to capture visual state of UI components for code review, visual regression testing, or documentation.
| name | csv-analyzer |
| description | Analyze CSV files to generate summary statistics, detect data quality issues, and produce formatted reports. |
| license | MIT |
| compatibility | Python 3.10+ |
| metadata | {"category":"data-processing","version":"1.0"} |
| allowed-tools | read_file write_file virtual_bash |
Analyze CSV data files to produce summary statistics and quality reports.
Activate this skill when a user provides a CSV file and wants:
read_file tool/skills/csv-analyzer/scripts/analyze.pypython3 /skills/csv-analyzer/scripts/analyze.py /workspace/data.csv
The script outputs a JSON report with:
row_count: Total number of rowscolumn_count: Number of columnscolumns: Array of column metadata (name, type, null_count, unique_count)quality_issues: Array of detected problemsFormat the results as a readable summary table for the user.
See /skills/csv-analyzer/references/ for additional documentation on CSV analysis best practices.