agentmods.dev

chart-visualization

A guide for choosing and creating charts, diagrams, dashboards, and other visual summaries from research data.

In plain words
What is it for?
Use it to select a suitable visual format, prepare data, build the artifact, and check its quality and accessibility.
Why use it?
It helps turn structured data into readable visuals while keeping labels, units, sources, and transformations clear.

Skill for Claude CodeCodex

22 1mo ago A scan 66 tokens original MIT
View the source file iFurySt/aifi
Install

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.

agentmods
npx agentmods add skills/ifuryst/aifi/chart-visualization
Any agent
npx skills add iFurySt/aifi --skill chart-visualization
Clone the repo
git clone --depth 1 https://github.com/iFurySt/aifi

Made for: Claude Code, Codex.

Per session 66 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,143 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00066 $0.01143
Opus 5 $0.00033 $0.00571
Sonnet 5 $0.00013 $0.00229
Haiku 4.5 $0.00007 $0.00114

Measured 2d ago against content hash d8b5e16d60b4, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

chart-visualization 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 2d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/render_examples.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/chart-visualization/SKILL.md · 104 lines

How it starts

The opening of the file, as written. The whole thing — 104 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.
  3. Load only the implementation reference needed for the selected environment: investment HTML, static SVG, Python plotting, browser HTML, diagram text, or Sankey flow.
  4. Normalize the data before drawing. Keep source labels, units, time ranges, and transformations visible in the artifact or companion notes.
  5. Generate the smallest useful artifact first, then iterate on labeling, ordering, annotations, and accessibility.
  6. Validate the output with references/quality-gates.md before returning it.
  7. 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.

Read the full file on GitHub · 104 lines

Files

What ships with it

19 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.

Changes

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.

  1. 2d ago First seen · 104 lines · 66 tokens per session scan A d8b5e16d60b4

Subscribe to this mod's changes

chart-visualization is a skill published in the GitHub repository iFurySt/aifi (22 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,143 once invoked, about $0.0003 per session on Opus 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 2026-08-30.

Analytics and metricsResearchai-agentsdecision-supportevidence-based-researchfinancial-researchfintechinvestment-research
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