plotly-dashboard-skilllisted

# Plotly Dashboard Skill Create interactive dashboards with a single source of truth for UI and figure styling. ## Instructions 1. Capture audience, questions, and data constraints. 2. Pick a layout pattern and component library. 3. Define a theme and Plotly figure template. 4. Build the layout skeleton before callbacks. 5. Implement callbacks with clear inputs/outputs. 6. Optimize slow callbacks with caching or pre-aggregation. ## Quick Reference | Task | Action | |------|--------| | UI style guide | See `STYLE_GUIDE.md` | | Figure template | See `FIGURE_STYLE.md` | | Palettes | See `PALETTES.md` | | App architecture | See `DASH_ARCHITECTURE.md` | | Performance | See `PERFORMANCE.md` | | Copyable app patterns | See [EXAMPLES.md](EXAMPLES.md) | | Runnable smoke app | [Runnable app](examples/app.py) | | Definition of done | [QA checklist](QA_CHECKLIST.md) | ## 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 - [ ] Layout communicates hierarchy and intent - [ ] Callbacks are small and focused - [ ] p95 interaction latency acceptable - [ ] Styling is consistent across charts - [ ] `uv run --script examples/app.py --smoke` returns HTTP 200 and its measured pure-callback p95 is within the declared latency budget (300 ms by default). ## Examples ###