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.
npx agentmods add skills/anthropics/knowledge-work-plugins/explore-datanpx skills add anthropics/knowledge-work-plugins --skill explore-datagit clone --depth 1 https://github.com/anthropics/knowledge-work-pluginsWhat 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 | $0.00054 | $0.02678 |
| Opus 5 | $0.00027 | $0.01339 |
| Sonnet 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 2d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- explore-data — 100% identical, 0 lines differ
- explore-data-th — 89% identical, 12 lines differ
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.
- 2d ago First seen · 326 lines · 54 tokens per session scan A af7590fa6163
explore-data is a skill published in the GitHub repository anthropics/knowledge-work-plugins (23,791 stars, last pushed today), licensed Apache-2.0. It adds 54 tokens to every session and 2,678 once invoked, about $0.0003 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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