Explore Data Skill

Profiles an unfamiliar dataset against finance-grade quality checks before any model is built.

Finance / Data Qualityfinancedata-quality
by AgentVoltv1.0.0Published 1y ago1 install
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Before & after

Same prompt, same agent — the only difference is the skill.

PromptCheck this revenue dataset for quality issues before analysis
Beforegeneric AI agent
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.
Afterwith Explore Data Skill
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

SKILL.md
---
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

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