ifuryst/aifi · Archived

chart-visualization

Create analytical charts, data visualizations, diagrams, dashboards, and Sankey or flow visuals from research data.

First seen Aug 8, 2026

Installation

$ npx skills add ifuryst/aifi --skill chart-visualization

Summary

  • Create analytical charts, data visualizations, diagrams, dashboards, and Sankey or flow visuals from research data.
  • Use when a task asks to draw, plot, graph, visualize, chart, compare, summarize data visually, build an analysis figure, create an interactive chart, or choose the right visualization method.

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Not declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 22
License LICENSE
Default branch main
Open issues 0
Status Archived

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 5,133 B
  • docs SUMMARY.md 334 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 3 installs

SKILL.md

Chart Visualization

Use this skill to turn structured or semi-structured data into legible visual artifacts for AIFi research. The skill supports static figures, browser-native HTML components, interactive charts, analysis dashboards, diagrams, and Sankey-style flow views.

Workflow

  1. Clarify the audience, artifact target, data source, and output format.
  2. Read references/chart-selection.md to choose the chart family and data

contract. For investment research, also read references/investment-html-gallery.md.

  1. Load only the implementation reference needed for the selected environment:

investment HTML, static SVG, Python plotting, browser HTML, diagram text, or Sankey flow.

  1. Normalize the data before drawing. Keep source labels, units, time ranges,

and transformations visible in the artifact or companion notes.

  1. Generate the smallest useful artifact first, then iterate on labeling,

ordering, annotations, and accessibility.

  1. Validate the output with references/quality-gates.md before returning it.
  2. Store reusable outputs under the relevant research/targets/<target>/

artifact folder when the chart belongs to investment research.

Reference TOC

  • references/chart-selection.md: chart chooser, data contracts, and common

analytical intents.

  • references/investment-html-gallery.md: investment chart taxonomy, HTML-first

design rules, and which example file to copy for each chart family.

  • references/html-examples/market-timeseries.html: line, area, indexed

performance, cumulative return, drawdown, rolling metric, and volume panels.

  • references/html-examples/trading-microstructure.html: candlestick, OHLC,

depth, order-flow, footprint, tick, Renko, Heikin Ashi, and Point & Figure layouts.

  • references/html-examples/composition-and-allocation.html: stacked bars,

100% stacked bars, donut, treemap, sunburst, icicle, Marimekko, and asset allocation views.

  • references/html-examples/risk-distribution.html: histogram, box plot,

violin, KDE, QQ plot, VaR, stress test, tracking error, and ratio ranking views.

  • references/html-examples/portfolio-optimization.html: risk-return scatter,

efficient frontier, factor exposure, correlation matrix, covariance matrix, alpha/beta, and risk attribution views.

  • references/html-examples/attribution-scenario.html: waterfall, bridge,

tornado, Monte Carlo, fan chart, scenario tree, and decision tree views.

  • references/html-examples/macro-rates-dashboard.html: yield curve, spread,

CPI/PPI, GDP, PMI, Fed dot plot, seasonality, cycle, and map-style macro panels.

  • references/html-examples/venture-saas-dashboard.html: KPI cards, cap table,

financial model table, cohort, unit economics, burn multiple, magic number, Rule of 40, TAM/SAM/SOM, adoption curve, power law, Pareto, and Lorenz views.

  • references/html-examples/flow-network-systems.html: funnel, Sankey, chord,

network graph, causal graph, Bayesian network, knowledge graph, agent workflow, and multi-agent collaboration views.

  • references/static-svg.md: dependency-free SVG generation and when to use

scripts/render_examples.py.

  • references/python-analysis.md: matplotlib, seaborn, pandas, and Plotly

guidance for local or notebook-style analysis environments.

  • references/web-interactive.md: Plotly, ECharts, React chart libraries, and

self-contained HTML export patterns.

  • references/sankey-flow.md: Sankey, alluvial, funnel, and flow-map data

shapes plus layout checks.

  • references/diagram-text.md: Mermaid, Graphviz, Vega-Lite, and text-first

visual specs for agents that cannot render images directly.

  • references/quality-gates.md: artifact validation, accessibility, source

labeling, and delivery checklist.

Method Selection

  • Need guaranteed local execution with no packages: use static-svg.md.
  • Need a polished investment research artifact that opens directly in a

browser: use investment-html-gallery.md and the matching references/html-examples/*.html file.

  • Need statistical analysis or print-quality PNG/PDF: use

python-analysis.md.

  • Need interactive hover, zoom, filters, or browser delivery: use

web-interactive.md.

  • Need money, users, cohorts, costs, or energy moving between categories: use

sankey-flow.md.

  • Need architecture, process, causal, or relationship diagrams: use

diagram-text.md.

Output

Return:

  • artifact path(s)
  • chart type and implementation method
  • data source and transformation notes
  • validation performed
  • known limitations or follow-up data needed

Quality Gate

Before finishing, read references/quality-gates.md and confirm that labels, units, source dates, color accessibility, rendering, and artifact paths are all handled.