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 fmschulz/omics-skills --skill plotly-dashboard-skillgit clone --depth 1 https://github.com/fmschulz/omics-skillsWrote 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/fmschulz/omics-skills/plotly-dashboard-skill)<a href="https://agentmods.dev/skills/fmschulz/omics-skills/plotly-dashboard-skill"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/plotly-dashboard-skill/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/fmschulz/omics-skills/plotly-dashboard-skill"><img src="https://agentmods.dev/badge/skills/fmschulz/omics-skills/plotly-dashboard-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00030 | $0.00827 |
| Opus 5.5 | $0.00012 | $0.00331 |
| Sonnet 5.5 | $0.00006 | $0.00165 |
| Haiku 4.5 | $0.00003 | $0.00083 |
Grade A, and why
plotly-dashboard-skill 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 23d 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.
How it starts
The opening of the file, as written. The whole thing — 82 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Plotly Dashboard Skill
Build interactive Dash dashboards with a single source of truth for UI and figure styling. Visual rules (greyscale first, color only to encode information, direct labels, accessibility) come from /beautiful-data-viz; this skill applies them to Dash and adds app structure and performance.
Instructions
- Capture audience, questions, and data constraints.
- Pick a layout pattern and component library. Layout, typography, color, and the shared figure template are in references/style.md. Charts start grey; add color only for a category the reader must tell apart, the one highlighted finding or selection, or an ordered or signed quantity.
- Register the figure template once, before building any chart, so no figure is styled by hand.
- Build the layout skeleton before callbacks.
- Implement callbacks with clear inputs and outputs. Project structure, the data-callback-to-render-callback fan-out, and the advanced callback tools are in references/architecture.md.
- Optimize slow callbacks with caching, pre-aggregation, or background callbacks; the same reference covers when each applies.
- Start from examples/app.py, the runnable app this skill tests. Copy its structure rather than writing a skeleton from scratch.
Input Requirements
- Audience and key decisions
- Data sources and update cadence
- Required filters and views
- Deployment constraints
Output
- Dash app scaffold (layout + callbacks)
- Consistent theming and figure templates
- README with usage notes
Quality Gates
Run the smoke test before calling a dashboard done:
uv run --script examples/app.py --smoke --latency-budget-ms 300
It must return HTTP 200 with a measured pure-callback p95 inside the declared budget.
Visual and interaction:
- Page has a clear title and a "last updated" stamp
- Filters are grouped, have sensible defaults, and their current state is visible
- Spacing, alignment, and legend placement are consistent; legends do not cover data
- Charts carry readable titles and axis labels or units in the title
- Tooltips include units and clean formatting
- Marks are grey unless color encodes a category, the highlighted finding, or an ordered or signed quantity; colors are colorblind-safe and never the only encoding
- Empty states are handled; no blank white cards
- Mobile and tablet views are usable, with no unintended horizontal scroll
- Click-to-filter is obvious and reversible, with a clear reset action
What ships with it
3 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.
- 23d ago Changed · -1 tokens per session 7e9183f4b02d
- 1mo ago Changed · +17 lines 5e1d3bd92f3a
- 1mo ago First seen · 65 lines · 31 tokens per session scan A 2561b4a9f80c
plotly-dashboard-skill is a skill published in the GitHub repository fmschulz/omics-skills (11 stars, last pushed 23d ago), licensed MIT. It adds 30 tokens to every session and 827 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 2026-08-31.
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