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 skills add CodeInfinity1/Stram --skill data-visualizationgit clone --depth 1 https://github.com/CodeInfinity1/StramWrote 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/codeinfinity1/stram/data-visualization)<a href="https://agentmods.dev/skills/codeinfinity1/stram/data-visualization"><img src="https://agentmods.dev/badge/skills/codeinfinity1/stram/data-visualization/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/codeinfinity1/stram/data-visualization"><img src="https://agentmods.dev/badge/skills/codeinfinity1/stram/data-visualization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00032 | $0.00431 |
| Opus 5.5 | $0.00013 | $0.00172 |
| Sonnet 5.5 | $0.00006 | $0.00086 |
| Haiku 4.5 | $0.00003 | $0.00043 |
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 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.
What it actually says
Data Visualization
Purpose
Make data and systems easier to understand through appropriate visuals. Use native canvas/interpreter artifacts and verify that visuals match the underlying evidence.
When To Use
Use for charts, diagrams, architecture flows, KPI visuals, explanatory graphics, and visual summaries.
Inputs And Evidence
- Data, desired message, audience, chart type constraints, and output format.
- Source files, analysis results, and visual artifact paths.
Tool Map
chart_artifact_createchart_artifact_inspectcsv_dataset_profilepython_interpreterpython_interpreter_artifactcanvas_a2ui_createcanvas_a2ui_renderwrite_notebusiness-reporting
Workflow
- Identify what the visual should communicate.
- Choose chart/diagram type based on data and audience.
- Generate SVG charts with
chart_artifact_createwhen structured data is available. - Label axes, units, sources, and caveats.
- Inspect generated artifacts with
chart_artifact_inspect. - Explain the finding without overstating certainty.
Native Implementation Boundaries
- Use Stram interpreter and canvas tools.
- Do not import external reference diagram or Anthropic canvas skill code.
- Bar/line SVG chart generation and inspection are native; add richer chart generators as native tools if needed.
Safety And Approval
- Do not visualize confidential data for public use without approval.
- Avoid misleading scales or cherry-picked data.
- Keep generated artifacts inside allowed paths.
Verification
- Check visual values against data.
- Confirm artifact path and render output.
- State if the visual is a sketch, draft, or verified chart.
Failure Modes
- Decorative chart that hides the answer.
- Wrong aggregation or axis.
- Claiming artifact exists without checking.
References
- Shortlist item:
data-visualization.
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 · 69 lines · 32 tokens per session scan A 52f1e7f1e065
data-visualization is a skill published in the GitHub repository CodeInfinity1/Stram (11 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 431 once invoked, about $0.0001 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 2026-09-03.
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