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/saolalab/clawforce/visualizationnpx skills add saolalab/clawforce --skill visualizationgit clone --depth 1 https://github.com/saolalab/clawforceWhat 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 | $0.00016 | $0.01481 |
| Opus 5 | $0.00008 | $0.00740 |
| Sonnet 5 | $0.00003 | $0.00296 |
| Haiku 4.5 | $0.00002 | $0.00148 |
Grade A, and why
Data Visualization 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 — 232 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization Skill
Chart Selection Guide
Bar Chart
Use when:
- Comparing categories
- Showing rankings
- Displaying discrete data
Best practices:
- Sort bars by value (descending) unless natural order exists
- Use horizontal bars for long category names
- Limit to ~10 categories for readability
- Use consistent colors
Example: Product sales by category, user signups by region
Line Chart
Use when:
- Showing trends over time
- Displaying continuous data
- Comparing multiple series
Best practices:
- Use time on x-axis
- Limit to 3-5 lines for clarity
- Use distinct colors/styles
- Mark important events/annotations
Example: Daily active users over time, revenue trends
Scatter Plot
Use when:
- Showing relationships between two variables
- Identifying correlations
- Finding outliers
Best practices:
- Use appropriate axis scales
- Add trend line if relationship exists
- Color-code by category if relevant
- Label outliers
Example: Price vs. demand, user engagement vs. retention
Heatmap
Use when:
- Showing patterns in two dimensions
- Comparing categories across time
- Displaying correlation matrices
Best practices:
- Use intuitive color scale (low to high)
- Include values in cells if space allows
- Sort rows/columns meaningfully
- Use diverging colors for centered data
Example: User activity by hour/day, correlation matrix
Pie Chart
Use when:
- Showing parts of a whole
- Few categories (3-5 max)
Best practices:
- Limit to 5-7 slices
- Sort slices by size
- Use distinct colors
- Consider bar chart alternative
Example: Market share, revenue by segment
Histogram
Use when:
- Showing distribution of continuous data
- Identifying skewness
- Understanding data shape
Best practices:
- Choose appropriate bin size
- Use consistent bin widths
- Label axes clearly
- Overlay distribution curve if helpful
Example: User age distribution, transaction amount distribution
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 · 232 lines · 16 tokens per session scan A 6e09b5c30d5d
Data Visualization is a skill published in the GitHub repository saolalab/clawforce (38 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 16 tokens to every session and 1,481 once invoked, about $0.0001 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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