analyze-data

analyze-data is a command for coding agents from abinauv/business-consulting. It costs 8 tokens per session (398 once invoked), scanned A, original, MIT.

A command for examining a dataset and producing practical analytical findings. It covers data quality, summary statistics, trends, comparisons, customer patterns, and charts.

In plain words
What is it for?
Use it to inspect datasets, calculate descriptive statistics, analyze time series or transactions, compare groups, detect anomalies, and create insight-focused visualizations.
Why use it?
It provides a structured way to move from raw data to findings and exposes missing values, unusual records, and other quality problems. The requested charts are intended to make important patterns easier to see.

Command

Part of the business-consulting plugin — 16 skills, 24 commands shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/abinauv/business-consulting/analyze-data
Clone the repo
git clone --depth 1 https://github.com/abinauv/business-consulting

Or install business-consulting, the plugin that ships this one along with the rest of its 16 skills, 24 commands.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyze-data

README.md
[![agentmods](https://agentmods.dev/badge/commands/abinauv/business-consulting/analyze-data.svg)](https://agentmods.dev/commands/abinauv/business-consulting/analyze-data)
Your own site
<a href="https://agentmods.dev/commands/abinauv/business-consulting/analyze-data"><img src="https://agentmods.dev/badge/commands/abinauv/business-consulting/analyze-data.svg" alt="Measured on agentmods" height="20"></a>
Per session 8 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 398 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00008 $0.00398
Opus 5 $0.00004 $0.00199
Sonnet 5 $0.00002 $0.00080
Haiku 4.5 $0.00001 $0.00040

Measured 5d ago against content hash d0c106473c44, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyze-data scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

commands/analyze-data.md · 55 lines

What it actually says

Data Analysis

Analyze the data described in $ARGUMENTS and extract actionable insights.

Instructions

Step 1: Data Understanding

  • Load and examine the data
  • Report: row count, column count, data types, date range
  • Identify missing values, outliers, and data quality issues
  • Document any cleaning or transformation steps taken

Step 2: Descriptive Statistics

  • Summary statistics for all numeric columns (mean, median, std, min, max, quartiles)
  • Distribution analysis for key variables
  • Identify skewness, outliers, and anomalies

Step 3: Key Analysis (choose what's relevant)

If time-series data:

  • Trend analysis (YoY, MoM growth rates)
  • Seasonality patterns
  • Anomaly detection
  • Moving averages

If customer/transaction data:

  • Pareto analysis (which 20% drives 80%?)
  • Segmentation (RFM or other relevant cuts)
  • Cohort analysis (if longitudinal)

If comparative data:

  • Benchmarking and gap analysis
  • Correlation analysis
  • Driver decomposition

Step 4: Visualization

Create 3-5 charts that tell the story. Each chart must have:

  • An action title (states the insight, not the topic)
  • Clean formatting following consulting standards
  • Source note

Step 5: Key Findings

Present 5-7 findings, each as:

  • [Bolded insight]: Supporting evidence with specific numbers and "so what" implication for the business.

Step 6: Recommendations

Based on the data, provide 3-5 actionable recommendations ranked by expected impact.

Use Python (pandas, matplotlib/seaborn) for analysis when a data file is provided. Save all charts as PNG files.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 5d ago First seen · 55 lines · 8 tokens per session scan A d0c106473c44

Subscribe to this mod's changes

analyze-data is a command published in the GitHub repository abinauv/business-consulting (26 stars, last pushed 6mo ago), licensed MIT. It adds 8 tokens to every session and 398 once invoked, about $0.0000 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

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