Knowledge Work Plugins is an open-source collection of Claude extensions organized around roles such as productivity, sales, and customer support. Each plugin combines role-specific guidance, connectors, commands, and sub-agents so knowledge workers can use Claude with their team’s tools and processes. The catalogue entries are examples of, or workflows from, this plugin collection.
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/anthropics/knowledge-work-pluginsnpx agentmods add skills/anthropics/knowledge-work-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/skills/anthropics/knowledge-work-plugins/explore-data)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/explore-data"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-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/skills/anthropics/knowledge-work-plugins/explore-data"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/explore-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 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.
Copies of this mod
4 near-identical copies found in the catalogue:
- explore-data — 100% identical, 0 lines differ
- explore-data — 97% identical, 9 lines differ
- explore-data — 95% identical, 7 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.
- 1mo 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 (29,887 stars, last pushed yesterday), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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