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 xobotyi/cc-foundry --skill datavizgit clone --depth 1 https://github.com/xobotyi/cc-foundryWrote 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/xobotyi/cc-foundry/dataviz)<a href="https://agentmods.dev/skills/xobotyi/cc-foundry/dataviz"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/dataviz/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/xobotyi/cc-foundry/dataviz"><img src="https://agentmods.dev/badge/skills/xobotyi/cc-foundry/dataviz.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 | $0.00148 | $0.02812 |
| Opus 5.5 | $0.00059 | $0.01125 |
| Sonnet 5.5 | $0.00030 | $0.00562 |
| Haiku 4.5 | $0.00015 | $0.00281 |
Grade B, and why
dataviz scanned grade B with 1 finding 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.
Strips warnings and disclaimersmediumAnti-refusal
Omitting safety caveats hides risk from the user and is a common jailbreak preamble.
- Point and fix; don't lecture. How it starts
The opening of the file, as written. The whole thing — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Visualization for Dashboards
A dashboard is a tool for fast, accurate judgement under operational stress. Every visual choice supports that judgement or undermines it.
Scope
- In scope: chart selection, encoding accuracy, color, layout and hierarchy, time-series conventions, observability frameworks (RED, USE, Four Golden Signals, SLO/SLI), anti-patterns, review criteria.
- Out of scope:
- Grafana JSON, panel options, transformations, variables, library panels —
dashboardssibling owns mechanics. - Alert rule definitions —
alertingsibling. This skill covers SLO/SLI dashboard structure, not rule firing. - Query syntax —
promql/metricsql/logsqlsiblings.
- Grafana JSON, panel options, transformations, variables, library panels —
References
- Perception and encoding — [
${CLAUDE_SKILL_DIR}/references/perception.md] Cleveland & McGill hierarchy, pre-attentive attributes, Tufte data-ink and chart junk, Stephen Few counterpoint. - Color — [
${CLAUDE_SKILL_DIR}/references/color.md] Semantic conventions, sequential/diverging/categorical palettes, Viridis, colorblind safety, WCAG contrast, dark/light themes, Grafana scheme mapping. - Layout — [
${CLAUDE_SKILL_DIR}/references/layout.md] F/Z patterns, tiering, grid discipline, progressive disclosure, RED/USE/Four Golden Signals, SLO/SLI dashboard structure. - Time-series specifics — [
${CLAUDE_SKILL_DIR}/references/time-series.md] Aspect ratio, baseline, interpolation, aggregation, dual y-axes, stacking.
Encoding hierarchy
Cleveland & McGill (1984), most → least accurate:
- Position on a common scale
- Position on non-aligned scales (small multiples)
- Length (bar charts)
- Angle / slope (pie slices)
- Area (bubble plots, treemaps)
- Volume (3D)
- Color saturation / luminance
- Color hue (categorical only)
Pick encodings as high as the data shape allows. Color hue distinguishes categories; never magnitude.
Chart-type selection
Flows from data shape and the question being answered. dashboards skill has the panel-type catalog; this skill has the
rationale.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 228 lines · 148 tokens per session scan B 50c09d71aeb1
dataviz is a skill published in the GitHub repository xobotyi/cc-foundry (22 stars, last pushed 7d ago), licensed MIT. It adds 148 tokens to every session and 2,812 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it B with 1 finding (strips warnings and disclaimers). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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