Explore Data Skill
Profiles an unfamiliar dataset against finance-grade quality checks before any model is built.
Before & after
Same prompt, same agent — the only difference is the skill.
I can look at your revenue data. Typically you'd want to check for missing values, duplicates, and outliers. Do you have the data in a spreadsheet or database? What date range covers it? How frequently does it get updated? Once I can see the structure, I can help you spot any obvious problems, though a full data quality audit would take some time and detailed inspection.
DATA QUALITY PROFILE: Revenue Dataset → DATASET OVERVIEW Rows: 24,847 transactions Columns: 12 (date, customer_id, amount, product, region, discount, etc.) Date range: Jan 1, 2025 – Aug 31, 2025 (9 months complete) File format: CSV, 3.2 MB → MISSING DATA ISSUES (data cannot support all analyses) customer_id: 0 missing (100% complete) amount: 18 missing rows (0.07% – acceptable for most analyses) discount: 1,247 missing (5.02% – HIGH, cannot calculate true LTV) region: 0 missing Trap: Discount field missing biases analysis toward full-price customers only Impact: Revenue analysis okay; margin analysis unreliable → NEGATIVE VALUES & OUTLIERS Negative amounts: 234 refunds (legitimate, separate analysis needed) Amounts > $100k: 3 rows (verify not data entry errors) Amounts < $1: 67 rows (mostly legitimate small add-ons) Recommendation: Flag large transactions for manual spot-check (verify real) → DUPLICATES & FORMATTING Exact duplicates: 0 found Near-duplicates: 12 rows (same customer, same date, same amount – likely double-posted) Date format issues: All dates parsed correctly (no malformed entries) Fix: Remove 12 near-duplicates (customer_id + date + amount combo must be unique) → DISTRIBUTION QUALITY Revenue by month: Concentrated in June-July (seasonal spike, not data error) Revenue by region: North accounts for 58% (concentration okay if real) Top 10 customers: $2.8M of $18.4M total (27% concentration – typical for B2B) → RECOMMENDED ANALYSES (data quality supports these) ✓ Monthly revenue trend analysis (complete data) ✓ Regional performance comparison (all regions present) ✓ Customer cohort retention (customer_id clean) ✗ Margin by product (discount field too sparse) ✗ Discount impact study (5% missing creates bias) → DATA PREP ACTIONS First: Remove 12 near-duplicates (creates single source of truth) Then: Impute discount? No – remove rows where discount = null for margin analysis Document: Margin analysis only on 23,600 records (discount present) Finalize: Dataset is ready for revenue and customer analysis; margin analysis limited scope
About this skill
name: explore-data-finance description: Use when Profiles an unfamiliar dataset against finance-grade quality checks before any model is built.
Explore Data Skill
Reads a dataset's structure, classifies metrics/dimensions/dates, and profiles it for the issues that quietly damage reporting (missing values, duplicates, negative balances, broken formatting), then recommends what analysis the data can actually support.
What you get
- Available in the official data plugin
- works on Excel, CSV, JSON, parquet, and live warehouse tables.
Customize your output
- Point it at your own workbooks/datasets and adapt the output structure to your reporting standards.
Example output
A discovery audit that caught negative revenue and concentration risk in an enterprise dataset.
Best for
Analysts, FP&A, and data teams.
SKILL.md preview
---
name: explore-data-finance
description: Use when profiling an unfamiliar dataset against finance-grade quality checks before building any model or report on top of it.
version: 1.0.0
category: Finance / Data Quality
author: AgentVolt
license: proprietary
tags:
- finance
- data-quality
- flagship
---
# Explore Data Skill
Profiles a new dataset the way a finance analyst would before trusting it, classifying its structure and surfacing the quality issues that quietly corrupt downstream reporting.
## When to use
… (sign up to view the full skill)More finance skills
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