A registry of agent skills with test results.

Clean messy CSV files: fix headers, trim whitespace, normalise dates and drop duplicate rows before analysis.

Cleans messy CSV files into a normalized schema. Use this when the user has a .csv with malformed rows, inconsistent headers, or junk lines and wants a tidy spreadsheet out. Not for Excel-specific formatting.

Handle CSV files from construction software exports. Auto-detect delimiters, encodings, and clean messy data.

Clean and standardize messy tabular data (CSV, spreadsheet paste, system exports) into an analysis-ready dataset — consistent dates and names, typed columns, duplicates identified, missing values handled explicitly. Use when the user says "clean this data", "standardize this CSV", "dedupe this list", or pastes a table with inconsistent formatting.

Complete spreadsheet methodology — data cleanup, transformation, analysis, dashboards, automation, and reporting. Works with CSV, Excel, Google Sheets, or any tabular data. Use when the user needs to clean messy data, build reports, create dashboards, automate recurring spreadsheet tasks, or transform data between formats.

Quick techniques for cleaning CSV files - deduplication, normalization, encoding fixes, merging, and common data quality repairs using command-line tools and scripting. Use when the user asks about csv data cleaner, related techniques, best practices, or needs guidance in this domain. Do NOT use when the request is outside the scope of csv data cleaner or requires a different specialized skill.

Cleans messy tabular datasets in pandas end-to-end — fixing dtypes, parsing dates and numbers, standardizing text, handling missing values, removing duplicates, detecting and treating outliers, and reshaping wide/long into tidy data. Use this skill when the user asks to "clean this CSV/Excel/dataframe", "fix data types", "handle missing values / NaNs", "remove duplicates", "deal with outliers", "standardize column names or categories", "parse dates", "melt/pivot/reshape", or to build a reproduci

Clean a messy dataset methodically — the profiling pass that finds what's actually wrong (dupes, format drift, phantom spaces, mixed types), the fix order that doesn't corrupt while correcting, and the log that makes the cleaning defensible. Use when asked clean this export, why is my pivot double-counting, these names don't match between sheets, or prep this data for analysis. Produces the profile of what's wrong, the ordered cleaning plan, the join-key repairs, and the cleaning log.

Extracts tables and headers from CSV files and validates them against a schema. Use when the user mentions CSV parsing, header validation, or tabular data cleanup.

Clean and transform messy data for analysis in Python, R, or Stata

Normalizes and improves the consistency of prospect-list data. Use when the user provides a messy prospect or lead export and wants standardized records, flagged issues, and a cleanup summary.

Cleans up CRM data from HubSpot, Salesforce, Pipedrive, Zoho, Close, Attio, or any CRM CSV export. Finds duplicate contacts and companies with fuzzy matching (not just exact email), normalizes emails, phones to E.164, names, domains, states, and countries, flags stale, invalid, role, and test records, finds broken contact-company associations, and produces a reviewable merge plan plus import-ready files without touching the live CRM. Use this skill whenever the user wants to dedupe, find or merg

Clean and transform messy data in Stata with reproducible workflows

Deduplicate, normalize, and enrich CRM contacts and companies. Use when a user needs to clean CRM data, find duplicate contacts, standardize phone numbers or emails, merge duplicate records, audit data quality, or enrich contacts with external sources like Clearbit or Apollo. Works with HubSpot, Salesforce, Pipedrive, or any CRM with CSV export. Instruction-only skill — no scripts or code execution. All operations are performed via CRM platform APIs or CSV export/import workflows.

Read, transform, analyze, and generate Excel and CSV files. Use when a user asks to open a spreadsheet, process Excel data, merge CSVs, create pivot tables, clean up data, convert between Excel and CSV, add formulas, filter rows, or generate reports from tabular data. Handles .xlsx, .xls, and .csv.

10 data wrangling skills. Trigger: messy data, format conversion, missing values, data reshaping. Design: pipeline-oriented recipes for common data cleaning and transformation tasks.

Parse, transform, and analyze CSV files with advanced data manipulation capabilities.

Merge multiple CSV/Excel files with intelligent column matching, data deduplication, and conflict resolution. Handles different schemas, formats, and combines data sources. Use when users need to merge spreadsheets, combine data exports, or consolidate multiple files into one.

Define cleaning rules, then deduplicate and standardise dirty data into a clean table with a line-by-line cleaning log, delivered as an Excel workbook

Formats spreadsheet exports into tidy CSV files ready to be shared.