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.
npx skills add causify-ai/helpers --skill graphviz.causal_kg_stylegit clone --depth 1 https://github.com/causify-ai/helpersWrote 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.
[](https://agentmods.dev/skills/causify-ai/helpers/graphviz.causal_kg_style)<a href="https://agentmods.dev/skills/causify-ai/helpers/graphviz.causal_kg_style"><img src="https://agentmods.dev/badge/skills/causify-ai/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.
<a href="https://agentmods.dev/skills/causify-ai/helpers/graphviz.causal_kg_style"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/graphviz.causal_kg_style.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 3 Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
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 | $0.00016 | $0.01235 |
| Opus 5 | $0.00008 | $0.00617 |
| Sonnet 5 | $0.00003 | $0.00247 |
| Haiku 4.5 | $0.00002 | $0.00123 |
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 10d 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.
How it starts
The opening of the file, as written. The whole thing — 146 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
I will give you a description or an image and your task is to produce a Graphviz/DOT representation of that graph that follows the rules below exactly
The resulting graph should allow a knowledgeable reader to
- 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
Step 1: Generate DOT File
General Graph Rules
- Use Graphviz DOT syntax
- Use a directed graph (
digraph) - Set
rankdir=LRfor left-to-right causal flow - Use both
color(border) andfillcolor+style=filledto encode variable type (do not rely on color alone; keep 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=ellipsepenwidth=2- Must be colored using the exogenous palette above
- Endogenous variable (has at least one causal parent)
shape=box,roundedpenwidth=1(default)- Must be colored using the endogenous palette above
- Target variable (no descendants; under study)
shape=boxpenwidth=2- Must be colored using the target palette above
Observable vs Unobservable (Latent) Variables
- Observable variable
style=filled,solid- Use the appropriate color palette for its type (exogenous/endogenous/target/etc.)
fontcolor=black
- Unobservable / latent variable
style="filled,dashed"- Must use the latent palette above (
color=gray40,fillcolor=gray90,fontcolor=gray40) - Keep the same shape rule based on exogenous/endogenous/target if known;
otherwise default to
shape=ellipse
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.
- 10d ago First seen · 146 lines · 16 tokens per session scan A 87fc0570fd29
graphviz.causal_kg_style is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed yesterday), licensed Apache-2.0. It adds 16 tokens to every session and 1,235 once invoked, about $0.0001 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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