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/sun2443/designer-skillsnpx agentmods add skills/sun2443/designer-skills/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/skills/sun2443/designer-skills/explore-data)<a href="https://agentmods.dev/skills/sun2443/designer-skills/explore-data"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/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/skills/sun2443/designer-skills/explore-data"><img src="https://agentmods.dev/badge/skills/sun2443/designer-skills/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 | $0.00054 | $0.02678 |
| Opus 5.5 | $0.00022 | $0.01071 |
| Sonnet 5.5 | $0.00011 | $0.00536 |
| Haiku 4.5 | $0.00005 | $0.00268 |
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 13d 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.
This is a copy
100% identical to explore-data — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 326 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. Understand Structure
Before analyzing any data, understand its structure:
Table-level questions:
- How many rows and columns?
- What is the grain (one row per what)?
- What is the primary key? Is it unique?
- When was the data last updated?
- How far back does the data go?
Column classification — categorize each column as one of:
- Identifier: Unique keys, foreign keys, entity IDs
- Dimension: Categorical attributes for grouping/filtering (status, type, region, category)
- Metric: Quantitative values for measurement (revenue, count, duration, score)
- Temporal: Dates and timestamps (created_at, updated_at, event_date)
- Text: Free-form text fields (description, notes, name)
- Boolean: True/false flags
- Structural: JSON, arrays, nested structures
3. 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)
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.
- 13d ago First seen · 326 lines · 54 tokens per session scan A af7590fa6163
explore-data is a skill published in the GitHub repository sun2443/designer-skills (0 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 54 tokens to every session and 2,678 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to explore-data, differing in 0 lines, and is treated as a copy.
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