What is graphviz.causal_kg_style?

A graph-generation tool that creates Graphviz DOT diagrams for causal relationships. Causal inference is the study of whether one factor causes a change in another, rather than merely being associated with it.

Use it to turn a causal model into a left-to-right Graphviz diagram with labelled variable types and causal links.

It gives variables and relationships consistent visual forms, making causes, effects, hidden variables, interventions, and counterfactuals easier to distinguish.

graphviz.causal_kg_style — Skill for Claude Code by gpsaggese/helpers

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.

Any agent

npx skills add gpsaggese/helpers --skill graphviz.causal_kg_style

Clone the repo

git clone --depth 1 https://github.com/gpsaggese/helpers

Made for: Claude Code.

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.

README.md

[![agentmods](https://agentmods.dev/badge/skills/gpsaggese/helpers/graphviz.causal_kg_style/github.svg)](https://agentmods.dev/skills/gpsaggese/helpers/graphviz.causal_kg_style)

Your own site

<a href="https://agentmods.dev/skills/gpsaggese/helpers/graphviz.causal_kg_style"><img src="https://agentmods.dev/badge/skills/gpsaggese/helpers/graphviz.causal_kg_style/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

Your own site · 80×15

<a href="https://agentmods.dev/skills/gpsaggese/helpers/graphviz.causal_kg_style"><img src="https://agentmods.dev/badge/skills/gpsaggese/helpers/graphviz.causal_kg_style.svg" alt="Reviewed on agentmods" width="80" height="20"></a>

Per session 12 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 1,340 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

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.

Model	Per session	Once invoked
Fable 5.1	$0.00012	$0.01340
Opus 5.5	$0.00005	$0.00536
Sonnet 5.5	$0.00002	$0.00268
Haiku 4.5	$0.00001	$0.00134

Measured yesterday against content hash 706274ada26a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

graphviz.causal_kg_style 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 yesterday.

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.

.claude/skills/graphviz.causal_kg_style/SKILL.md · 155 lines

How it starts

The opening of the file, as written. The whole thing — 155 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are an expert in causal inference and graphical models

Given a description or an image, produce a Graphviz/DOT graph that follows the rules below exactly. The graph should let a knowledgeable reader:

Distinguish causation from correlation at a glance
Identify exogenous vs endogenous variables
Identify latent vs observable variables
Recognize interventions and counterfactuals

Use color to distinguish variable types consistently

Generate the DOT File

General Graph Rules

Use Graphviz DOT syntax
Use a directed graph (digraph)
Set rankdir=LR for left-to-right causal flow
Encode variable type with both color (border) and fillcolor + style=filled. Do not rely on color alone; keep the shape conventions too

Node Representation Rules

Variable Type Colors (Required)

Use these colors consistently for node borders/fills:

Exogenous variable: color=#408AB0, fillcolor=#EAF3F8
Endogenous variable: color=#62D4A4, fillcolor=#EAF9F3
Target variable: color=#F8D476, fillcolor=#FFF6DA
Latent (unobservable) variable: color=#183B4A, fillcolor=#E6EEF1
Intervened variable (do(X)): color=#DE5470, fillcolor=#FBE6EC
Counterfactual variable: color=#183B4A, fillcolor=#E6EEF1

Exogenous vs Endogenous vs Target

Exogenous variable (no causal parents)
- shape=ellipse
- penwidth=2
- Must use the exogenous palette
Endogenous variable (has at least one causal parent)
- shape=box,rounded
- penwidth=1 (default)
- Must use the endogenous palette
Target variable (no descendants; under study)
- shape=box
- penwidth=2
- Must use the target palette

Observable vs Unobservable (Latent) Variables

Observable variable
- style=filled,solid
- Use the palette of its type (exogenous/endogenous/target/etc.)
- fontcolor=black
Unobservable / latent variable
- style="filled,dashed"
- Must use the latent palette (color=gray40, fillcolor=gray90, fontcolor=gray40)
- Keep the shape rule for exogenous/endogenous/target if known; otherwise default to shape=ellipse

Read the full file on GitHub · 155 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.

yesterday First seen · 155 lines · 12 tokens per session scan A 706274ada26a

Subscribe to this mod's changes

graphviz.causal_kg_style is a skill published in the GitHub repository gpsaggese/helpers (158 stars, last pushed today), licensed Apache-2.0. It adds 12 tokens to every session and 1,340 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-11-06.

Scientific

graphviz.causal_kg_style next to 6 skills from other repositories
Mod	Type	Stars	Last push	Licence	Scan	Tokens	Works with
graphviz.causal_kg_stylegpsaggese/helpers	Skill	158	today	Apache-2.0	A	12	Claude Code
instrument-data-to-allotropeanthropics/knowledge-work-plugins	Skill	35k	today	Apache-2.0	A	123	Claude Code
exploratory-data-analysisK-Dense-AI/scientific-agent-skills	Skill	56k	5d ago	MIT	A	83	Claude Code
matlabK-Dense-AI/scientific-agent-skills	Skill	56k	5d ago	MIT	A	44	Claude Code
phylogeneticsK-Dense-AI/scientific-agent-skills	Skill	56k	5d ago	MIT	A	71	Claude Code, Codex
research-engineerdavila7/claude-code-templates	Skill	33k	today	MIT	A	43	Claude Code, Codex
mapping-to-snomedmaziyarpanahi/openmed	Skill	5.8k	today	Apache-2.0	A	205	Claude Code