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/create-vizWrote 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/create-viz)<a href="https://agentmods.dev/skills/anthropics/knowledge-work-plugins/create-viz"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/create-viz/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/create-viz"><img src="https://agentmods.dev/badge/skills/anthropics/knowledge-work-plugins/create-viz.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 | $0.00055 | $0.01202 |
| Opus 5.5 | $0.00022 | $0.00481 |
| Sonnet 5.5 | $0.00011 | $0.00240 |
| Haiku 4.5 | $0.00006 | $0.00120 |
Grade A, and why
create-viz 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:
- create-viz — 100% identical, 0 lines differ
- create-viz — 97% identical, 9 lines differ
- create-viz — 95% identical, 7 lines differ
- create-viz-th — 92% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/create-viz - Create Visualizations
If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md.
Create publication-quality data visualizations using Python. Generates charts from data with best practices for clarity, accuracy, and design.
Usage
/create-viz <data source> [chart type] [additional instructions]
Workflow
1. Understand the Request
Determine:
- Data source: Query results, pasted data, CSV/Excel file, or data to be queried
- Chart type: Explicitly requested or needs to be recommended
- Purpose: Exploration, presentation, report, dashboard component
- Audience: Technical team, executives, external stakeholders
2. Get the Data
If data warehouse is connected and data needs querying:
- Write and execute the query
- Load results into a pandas DataFrame
If data is pasted or uploaded:
- Parse the data into a pandas DataFrame
- Clean and prepare as needed (type conversions, null handling)
If data is from a previous analysis in the conversation:
- Reference the existing data
3. Select Chart Type
If the user didn't specify a chart type, recommend one based on the data and question:
| Data Relationship | Recommended Chart |
|---|---|
| Trend over time | Line chart |
| Comparison across categories | Bar chart (horizontal if many categories) |
| Part-to-whole composition | Stacked bar or area chart (avoid pie charts unless <6 categories) |
| Distribution of values | Histogram or box plot |
| Correlation between two variables | Scatter plot |
| Two-variable comparison over time | Dual-axis line or grouped bar |
| Geographic data | Choropleth map |
| Ranking | Horizontal bar chart |
| Flow or process | Sankey diagram |
| Matrix of relationships | Heatmap |
Explain the recommendation briefly if the user didn't specify.
4. Generate the Visualization
Write Python code using one of these libraries based on the need:
- matplotlib + seaborn: Best for static, publication-quality charts. Default choice.
- plotly: Best for interactive charts or when the user requests interactivity.
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 · 155 lines · 55 tokens per session scan A 3b13a9c2c9d2
create-viz is a skill published in the GitHub repository anthropics/knowledge-work-plugins (29,887 stars, last pushed yesterday), licensed Apache-2.0. It adds 55 tokens to every session and 1,202 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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