Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/fergupa/claude_pluginsnpx agentmods add commands/fergupa/claude_plugins/explore-dataWrote 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.
[](https://agentmods.dev/commands/fergupa/claude_plugins/explore-data)<a href="https://agentmods.dev/commands/fergupa/claude_plugins/explore-data"><img src="https://agentmods.dev/badge/commands/fergupa/claude_plugins/explore-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/fergupa/claude_plugins/explore-data"><img src="https://agentmods.dev/badge/commands/fergupa/claude_plugins/explore-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00014 | $0.01017 |
| Opus 5.5 | $0.00006 | $0.00407 |
| Sonnet 5.5 | $0.00003 | $0.00203 |
| Haiku 4.5 | $0.00001 | $0.00102 |
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 1mo 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.
How it starts
The opening of the file, as written. The whole thing — 116 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/explore-data - Profile and Explore a Dataset
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.
Usage
/explore-data <table_name or file>
Workflow
1. Access the Data
If a data warehouse MCP server is connected:
- Resolve the table name (handle schema prefixes, suggest matches if ambiguous)
- Query table metadata: column names, types, descriptions if available
- Run profiling queries against the live data
If a file is provided (CSV, Excel, Parquet, JSON):
- Read the file and load into a working dataset
- Infer column types from the data
If neither:
- Ask the user to provide a table name (with their warehouse connected) or upload a file
- If they describe a table schema, provide guidance on what profiling queries to run
2. Generate Data Profile
Run the following profiling checks:
Table-level metrics:
- Total row count
- Column count and types breakdown
- Approximate table size (if available from metadata)
- Date range coverage (min/max of date columns)
Column-level metrics for each column:
- Data type (and whether it matches expected type)
- Null count and null rate (%)
- Distinct count and cardinality (distinct / total)
- For numeric columns: min, max, mean, median, stddev, percentiles (p25, p50, p75, p95, p99)
- For string columns: min/max length, most common values (top 10), empty string count
- For date/timestamp columns: min, max, distribution by time period
- For boolean columns: true/false/null distribution
Present the profile as a clean summary table, grouped by column type (dimensions, metrics, dates, IDs).
3. Identify Data Quality Issues
Flag potential problems:
- High null rates: Columns with >5% nulls (warn), >20% nulls (alert)
- Low cardinality surprises: Columns that should be high-cardinality but aren't (e.g., a "user_id" with only 50 distinct values)
- High cardinality surprises: Columns that should be categorical but have too many distinct values
- Suspicious values: Negative amounts where only positive expected, future dates in historical data, obviously placeholder values (e.g., "N/A", "TBD", "test", "999999")
- Duplicate detection: Check if there's a natural key and whether it has duplicates
- Distribution skew: Extremely skewed numeric distributions that could affect averages
- Encoding issues: Mixed case in categorical fields, trailing whitespace, inconsistent formats
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.
- 1mo ago First seen · 116 lines · 14 tokens per session scan A 8a3b5f17d07e
explore-data is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 8mo ago), licensed Apache-2.0. It adds 14 tokens to every session and 1,017 once invoked, about $0.0001 per session on Opus 5.5. 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.
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