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explore-data

explore-data is a skill for Claude Code from ChrisGVE/localdata-mcp. It costs 30 tokens per session (641 once invoked), scanned A, original, Apache-2.0.

A data exploration workflow connects to a dataset, examines its tables and columns, samples records, checks data quality, and suggests suitable analyses.

In plain words
What is it for?
Use it to inspect schemas, column types, sample values, table statistics, null rates, uniqueness, consistency, and possible next analyses.
Why use it?
It gives you an initial map of unfamiliar data and highlights missing, inconsistent, or otherwise problematic fields before deeper work begins.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the localdata-mcp plugin — 18 skills, 11 agents, 1 MCP server shipped together

not rated 4repo +1 24d ago A scan 30 tokens original Apache-2.0

Good fit Use it to inspect schemas, column types, sample values, table statistics, null rates, uniqueness, consistency, and possible next analyses.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chrisgve/localdata-mcp/explore-data
View source ↗ ChrisGVE/localdata-mcp
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.

Any agent
npx skills add ChrisGVE/localdata-mcp --skill explore-data
Clone the repo
git clone --depth 1 https://github.com/ChrisGVE/localdata-mcp

Made for: Claude Code.

Or install localdata-mcp, the plugin that ships this one along with the rest of its 18 skills, 11 agents, 1 MCP server.

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 explore-data

README.md
[![agentmods](https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/explore-data.svg)](https://agentmods.dev/skills/chrisgve/localdata-mcp/explore-data)
Your own site
<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/explore-data"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/explore-data.svg" alt="Measured on agentmods" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 641 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00030 $0.00641
Opus 5 $0.00015 $0.00320
Sonnet 5 $0.00006 $0.00128
Haiku 4.5 $0.00003 $0.00064

Measured 8d ago against content hash bce53d0ee982, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

explore-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 8d 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.

skills/exploration/explore-data/SKILL.md · 42 lines

How it starts

The opening of the file, as written. The whole thing — 42 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Explore Data

Connect to a dataset, profile its structure and quality, and recommend what analyses to run next.

Steps

  1. Connect to the data source. Call connect_database with the path or connection string from $ARGUMENTS. Note the assigned database name in the response.

  2. Describe the schema. Call describe_database with the database name. Record the list of tables, column names, and column types. Identify which columns are numeric, categorical, datetime, and text.

  3. Sample rows from each table. For each table (or the first 3 if many), call execute_query with SELECT * FROM <table> LIMIT 10. Inspect the returned rows to understand value ranges, formats, and potential join keys.

  4. Describe key tables. For the most important tables (largest or most-referenced), call describe_table to get detailed column statistics including cardinality, null counts, and value distributions.

  5. Run a quality report. Call get_data_quality_report with the database name. Review completeness, uniqueness, and consistency scores. Flag columns with high null rates (above 20%) or low cardinality that may need attention.

  6. Summarize findings. Present a structured summary:

    • Number of tables, total rows, and columns
    • Data types breakdown (numeric, categorical, datetime, text)
    • Quality issues found (nulls, duplicates, inconsistencies)
    • Key relationships between tables (shared column names)

  7. Recommend next analyses. Based on data characteristics, suggest specific next steps:

    • Data quality concerns: run /localdata-mcp:data-quality for a thorough audit
    • Numeric pairs with potential relationships: suggest /localdata-mcp:analyze-correlations
    • Datetime column with a metric: suggest /localdata-mcp:forecast
    • Many numeric features: suggest /localdata-mcp:cluster-analysis or /localdata-mcp:dimensionality-reduction
    • Target variable present: suggest /localdata-mcp:regression
    • Treatment/control groups: suggest /localdata-mcp:ab-test
    • Coordinate or location columns: suggest /localdata-mcp:geospatial
    • Graph or network file: suggest /localdata-mcp:graph-data-explore
    • Needs external context (benchmarks, demographics): suggest /localdata-mcp:find-reference-data
    • Process or quality monitoring data: suggest /localdata-mcp:process-control
    • Unusual observations suspected: suggest /localdata-mcp:anomaly-detection

Read the full file on GitHub · 42 lines

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. 8d ago First seen · 42 lines · 30 tokens per session scan A bce53d0ee982

Subscribe to this mod's changes

explore-data is a skill published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 24d ago), licensed Apache-2.0. It adds 30 tokens to every session and 641 once invoked, about $0.0002 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 2026-08-31.

Analytics and metricsClaude Codeai-toolsclaude-desktopdata-analyticsfastmcpmcp
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