charted AGENTS.md

charted AGENTS.md is an instructions file for Codex, OpenCode from marzukia/charted. It costs 2,996 tokens per session, scanned A, original, MIT.

Instructions for using Charted, a Python library that creates charts as SVG images. They cover common chart types such as bar, line, pie, scatter, and heatmap charts.

In plain words
What is it for?
Creating or editing Python code that generates SVG charts, including sales charts, time-series plots, distributions, and project schedules.
Why use it?
They give an agent the correct API patterns and data shapes for creating charts, reducing mistakes when using the library.

Instructions file for CodexOpenCode

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 instructions/marzukia/charted/agents-md
Clone the repo
git clone --depth 1 https://github.com/marzukia/charted

Made for: Codex, OpenCode.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for charted AGENTS.md

README.md
[![agentmods](https://agentmods.dev/badge/instructions/marzukia/charted/agents-md.svg)](https://agentmods.dev/instructions/marzukia/charted/agents-md)
Your own site
<a href="https://agentmods.dev/instructions/marzukia/charted/agents-md"><img src="https://agentmods.dev/badge/instructions/marzukia/charted/agents-md.svg" alt="Measured on agentmods" height="20"></a>
Per session 2,996 This file is loaded in full into every session.
When invoked 2,996 The same file — it is already loaded in full.
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 $0.02996 $0.02996
Opus 5 $0.01498 $0.01498
Sonnet 5 $0.00599 $0.00599
Haiku 4.5 $0.00300 $0.00300

Measured 4d ago against content hash 5fc384bb83bb, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

charted AGENTS.md 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 4d 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.

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.

AGENTS.md · 352 lines

How it starts

The opening of the file, as written. The whole thing — 352 lines — stays where its author put it; the contents beside it link to each section on GitHub.

AGENTS.md: Charted (Python SVG Chart Library)

Quick API Reference

import charted
from charted import (
    BarChart, ColumnChart, LineChart, ScatterChart,
    PieChart, RadarChart, AreaChart, BoxPlot, Histogram,
    HeatmapChart, GanttChart,
)

# Bar (horizontal)
BarChart(data=[10, 20, 30], labels=["A", "B", "C"], title="Sales")

# Column (vertical)
ColumnChart(data=[10, 20, 30], labels=["A", "B", "C"])

# Line
LineChart(data=[[10, 20, 30]], labels=["Jan", "Feb", "Mar"])

# Scatter (note: uses x_data/y_data, NOT data=)
ScatterChart(x_data=[1, 2, 3], y_data=[10, 20, 30])

# Pie
PieChart(data=[35, 28, 18], labels=["A", "B", "C"])

# Radar
RadarChart(data=[85, 90, 75, 88, 92], labels=["Spd", "Str", "Def", "Tech", "Sta"])

# Area
AreaChart(data=[10, 20, 15, 25], labels=["Q1", "Q2", "Q3", "Q4"])

# Box Plot: each item in data is a raw distribution list
BoxPlot(data=[[1,2,3,4,5,6,7], [2,4,6,8,10]], labels=["A", "B"])

# Histogram: single flat list, bins param
Histogram(data=[1.2, 2.3, 2.5, 3.1, 4.0], bins=5)

# Heatmap: 2D matrix
HeatmapChart(data=[[1,2,3],[4,5,6],[7,8,9]], labels=["R1","R2","R3"])

# Gantt
GanttChart(tasks=[{"name": "Task 1", "start": 0, "end": 5}])

Common Patterns

Single series

chart = BarChart(data=[120, 180, 210], labels=["Q1", "Q2", "Q3"], title="Revenue")
chart.save("chart.svg")

Multi-series

chart = ColumnChart(
    data=[[12, 22, 30], [-8, -15, -20]],
    labels=["Q1", "Q2", "Q3"],
    series_names=["Revenue", "Costs"],
)

Stacked vs side-by-side

# Column: stacked by default (y_stacked=True). Disable:
ColumnChart(data=[[1,2],[3,4]], labels=["A","B"], y_stacked=False)

# Bar: side-by-side by default. Stack with:
BarChart(data=[[1,2],[3,4]], labels=["A","B"], x_stacked=True)

Dark theme

chart = BarChart(data=[1,2,3], labels=["A","B","C"], theme="dark")

Custom dimensions

chart = LineChart(data=[[1,2,3]], labels=["A","B","C"], width=800, height=400)

Read the full file on GitHub · 352 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. 4d ago First seen · 352 lines · 2,996 tokens per session scan A 5fc384bb83bb

Subscribe to this mod's changes

charted AGENTS.md is an instructions file published in the GitHub repository marzukia/charted (11 stars, last pushed 1mo ago), licensed MIT. It adds 2,996 tokens to every session, about $0.0150 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.

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