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 commands/fergupa/claude_plugins/create-vizgit clone --depth 1 https://github.com/fergupa/claude_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 | $0.00007 | $0.01153 |
| Opus 5 | $0.00003 | $0.00576 |
| Sonnet 5 | $0.00001 | $0.00231 |
| Haiku 4.5 | $0.00001 | $0.00115 |
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 yesterday.
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 — 154 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.
- yesterday First seen · 154 lines · 7 tokens per session scan A 0c50831f19e5
create-viz is a command published in the GitHub repository fergupa/claude_plugins (2 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 7 tokens to every session and 1,153 once invoked, about $0.0000 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 2026-08-31.
Other commands, from other repositories
git
Git operations with intelligent commit messages and workflow optimization.
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
constitution
Create or update the project constitution from interactive or provided principle inputs.