prism-chart
Use when asked to implement a chart, select a visualization type, or build a data display component. Examples: "implement chart for time series", "best visualization for comparison data", "chart component for analytics"
Allowed Tools
Provided by Plugin
tonone
Engineering + Product + Operations + Legal + Design + Data Science + Security Operations + Developer Experience + Infrastructure Specialist + AI Operations team — 100 agents as Claude Code specialists. Infrastructure, DevOps, backend, security, ML/AI, mobile, UX, analytics, growth, revenue, content, PR, customer success, finance, people, operations, support, contracts, compliance, IP, governance, regulatory, color systems, typography, motion, accessibility, design tokens, forecasting, feature engineering, model training, drift monitoring, vector search, LLM fine-tuning, pen testing, detection engineering, incident response, zero trust, API docs, SDK design, developer onboarding, Kubernetes, Terraform, FinOps, service mesh, edge computing, caching, queuing, multi-cloud, chaos engineering, model deployment, LLM evaluation, AI observability, guardrails, prompt engineering, embeddings, ranking, and more.
Installation
This skill is included in the tonone plugin:
/plugin install tonone@claude-code-plugins-plusClick to copy
Instructions
prism-chart — Chart & Visualization Selection
Follow the output format defined in docs/output-kit.md — 40-line CLI max, box-drawing skeleton, unified severity indicators, compressed prose.
When to use
User needs a chart implementation, visualization type recommendation, or data display component.
Workflow
- Identify data type from user request (time series, comparison, distribution, composition, relationship, etc.)
- Search chart knowledge base:
python3 -m prism_agent.uiux search --domain chart --query "{data_type}" --limit 3
- Evaluate results for: data volume threshold, accessibility grade, interaction level
- Output recommendation with library choice and accessibility fallback
Output format
┌─ Chart Recommendation — {data_type} ────────────────────────────────┐
│ Chart type: {chart_type} │
│ Library: {library} (Chart.js / Recharts / D3 / Plotly) │
│ Accessibility: {grade} (AA / A / Below AA) │
│ Interaction level: {level} (static / hover / drill-down) │
│ Data volume: {threshold} (max recommended data points) │
├─ Color guidance ────────────────────────────────────────────────────┤
│ {color_guidance} │
├─ Accessibility fallback ────────────────────────────────────────────┤
│ {fallback_description} │
└──────────────────────────────────────────────────────────────────────┘
Anti-patterns
- Never ignore data volume threshold — recommend aggregation if data exceeds it
- Never skip accessibility fallback for charts graded below AA
- Never choose a chart type based on visual appeal over data clarity
- Never recommend a library without confirming it is compatible with the detected stack
Delivery
If output exceeds the 40-line CLI budget, invoke /atlas-report with the full findings. The HTML report is the output. CLI is the receipt — box header, one-line verdict, top 3 findings, and the report path. Never dump analysis to CLI.