exec-visualization

A guide for creating charts, graphs, plots, dashboards, and diagrams from data. It explains how to choose a suitable visual form and how to label and compare data clearly.

In plain words
What is it for?
Use it to create PNG or SVG charts, select chart types for comparisons or trends, define axes and legends, and prepare data visualizations for documents or websites.
Why use it?
It helps turn numerical relationships into visuals that are easier to understand. It also avoids misleading choices such as unclear scales or using color as the only way to identify series.

Skill for Claude CodeCodex

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/anhnguyen0905/codex-mcp/exec-visualization
Any agent
npx skills add anhnguyen0905/codex-mcp --skill exec-visualization
Clone the repo
git clone --depth 1 https://github.com/anhnguyen0905/codex-mcp

Made for: Claude Code, Codex.

Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 619 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. 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 $0.00034 $0.00619
Opus 5 $0.00017 $0.00309
Sonnet 5 $0.00007 $0.00124
Haiku 4.5 $0.00003 $0.00062

Measured 2d ago against content hash fbce051082bf, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade B, and why

exec-visualization scanned grade B with 1 finding 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.

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.

Reads agent configuration directoriesmediumAgent snooping

.claude/, .codex/, .gemini/ hold keys, settings and other credentials a mod has no legitimate need for.

Register it with the Codex CLI in `~/.codex/config.toml`:
skills/exec-visualization/SKILL.md · 63 lines

How it starts

The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.

flint-chart Visualization Standards (embed into Codex prompts)

Standards block

flint-chart visualization (mandatory):
- Primary: If flint-chart MCP tools are available in the session, use them and request PNG/SVG
  output.
- Fallback: Otherwise, follow the flint-chart-author skill spec or run the local
  `npx flint-chart` CLI to compile and render.
- Last resort: Use Python matplotlib only when flint-chart cannot express the chart, and state the
  fallback reason in one line.
- Output: Default to PNG/SVG for documents and deliverables as a drop-in for matplotlib PNGs. Use
  backend specs such as Vega-Lite or ECharts JSON only for web or interactive targets that need them.
- Form: Match the data's job: magnitude -> bar; change over time -> line; part of whole -> stacked
  bar (pie sparingly); correlation -> scatter; single headline -> stat tile or hero number, not
  necessarily a chart.
- Axes: Use one y-scale only; never use a dual-axis chart. Put different-scale measures in two
  charts or index them to a common base.
- Series: For two or more series, always include a legend and never encode identity by color alone.
- Defaults: Let flint derive the palette, marks, and layout from the data; do not hand-tune colors.
- Integrity: Never fabricate data points.

Verification block

Verification before finishing (mandatory):
- Confirm the chart file actually rendered: a non-empty PNG/SVG exists on disk, not just a spec.
- Confirm the chart form matches the data's job, axes and labels are correct, and no data was
  invented.
- If Python was used, confirm its one-line fallback reason is present.

Setup — flint-chart-mcp

Install or run the server with npx -y flint-chart-mcp.

Register it with the Codex CLI in ~/.codex/config.toml:

[mcp_servers.flint-chart]
command = "npx"
args = ["-y", "flint-chart-mcp"]

Register it with Claude using claude mcp add flint-chart -- npx -y flint-chart-mcp.

The fallback agent skill lives at agent-skills/flint-chart-author/ in the microsoft/flint-chart repository. The flint-chart Python port is preview-only and not released; do not instruct users to install it with pip.

Read the full file on GitHub · 63 lines

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 · 63 lines · 34 tokens per session scan B fbce051082bf

Subscribe to this mod's changes

exec-visualization is a skill published in the GitHub repository anhnguyen0905/codex-mcp (3 stars, last pushed 10d ago), licensed MIT. It adds 34 tokens to every session and 619 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (reads agent configuration directories). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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