lens-chart
Use when asked to select chart types for analytics dashboards, choose BI visualizations, or design data displays. Examples: "best chart for sales data", "dashboard visualization for metrics", "analytics chart selection"
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
lens-chart — BI & Analytics Chart 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 chart type selection or visualization recommendations for analytics dashboards or BI contexts.
Workflow
- Identify data type and BI context from user request (sales trends, cohort analysis, funnel, KPI comparison, etc.)
- Search chart knowledge base:
python3 -m lens_agent.uiux search --domain chart --query "{data_type}" --limit 3
- Search style for BI context:
python3 -m lens_agent.uiux search --domain style --query "{context}" --limit 2
- Evaluate for BI requirements: data density, drill-down capability, real-time support, library recommendation
- Output optimized for decision-making, not decoration
Output format
┌─ BI Chart Recommendation — {data_type} ─────────────────────────────┐
│ Chart type: {chart_type} │
│ Library: {library} │
│ Data density: {density} (low / medium / high) │
│ Drill-down: {drill_down} (yes / no / limited) │
│ Real-time support: {real_time} (yes / no) │
│ Accessibility: {grade} │
├─ Decision test ─────────────────────────────────────────────────────┤
│ "Does this answer a decision?" → {yes_no}: {rationale} │
└──────────────────────────────────────────────────────────────────────┘
Anti-patterns
- Never choose decorative over data-dense visualizations for BI contexts
- Never skip the "does this answer a decision?" test — every chart must justify its inclusion
- Never skip accessibility fallback for charts graded below AA
- Never recommend real-time charts without confirming the data pipeline supports streaming
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.