Works with the AI tools you already use
Claude Code
Cursor
Codex CLI
Muse
Dots+22 moreClaude CodeCursorCodex CLIMuseDots
OpenClaw
Manus
Grok
Grok Bot+18 agentsData Cleaning Architect
Clean messy tabular data with a profile-first workflow, audit logs, and reproducible pandas scripts.
$9
data-cleaning-architect
Example session with this skill installed
I have a messy customers.csv with weird date formats, some duplicate names with different emails, and weird characters in the addresses. Clean it up.
- Read your context and instructions
- Compiled the data-cleaning-architect
- Generated the data export
Profiled customers.csv: Found 3 date formats and 12 fuzzy duplicates.
Proposed plan
- Normalize dates to ISO 8601.
- Dedupe names (threshold <2).
- Fix encoding (mojibake).
Generated: customers_cleaned.csv, audit_log.md, and cleaning_script.py.
Review 12 flagged duplicates in log.
data-cleaning-architect.csv
CSV · data export
Example file from a real run - the skill writes it into your workspace.
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
Manual data cleaning is error-prone, slow, and often results in silent data loss. Developers frequently waste hours writing one-off scripts to handle inconsistent date formats, encoding issues, and hidden null values without a clear audit trail.
What it does
- Generates a comprehensive data profile identifying mixed types, encoding damage, and structural anomalies before any modifications.
- Executes a disciplined cleaning pipeline including type coercion, whitespace normalization, and ISO 8601 date standardization.
- Implements a quarantine system for failed coercions rather than silently converting values to null.
- Flags fuzzy duplicates for manual review instead of performing risky auto-merges.
- Produces a reproducible Python/pandas script for recurring datasets.
Frameworks & tools
Python, Pandas, CSV, Excel (XLSX), JSON.
Why this beats prompting it yourself
Standard LLM prompts often hallucinate fixes or silently delete rows they don't understand. This skill enforces a strict profile-first workflow, maintains an audit log of every change, and ensures row-count integrity that basic prompting lacks.
Use cases
- Preparing messy CSV exports from legacy systems for database migration.
- Standardizing disparate date and currency formats for financial analysis.
- Cleaning marketing lead lists with fuzzy deduplication and email validation.
- Generating reusable cleaning scripts for monthly reporting pipelines.
Known limitations
Requires the user to resolve specific ambiguities, such as ambiguous date formats (MM/DD vs DD/MM), when no clear evidence exists in the data.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
Free skills download straight away. Paid skills unlock right after purchase.
- 2
Unzip into your skills folder
Every agent reads skills from one folder on your machine. Drop the unzipped folder in there.
- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
Skills folder by agent
Click the path to copy it. Create the folder if it does not exist yet.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill passes our trust checks below.
Security scanned
Passed our 8-point scan before listing
Fresh listing
Recently published to Agensi
30-day refund
Not a fit? Get your money back
Trust & safety
Security scanned
Verified clean 3 months ago
- Passed all security checks, Safe to install