graphviz.generate_legend

graphviz.generate_legend is a skill for Claude Code from causify-ai/helpers. It costs 12 tokens per session (1,408 once invoked), scanned A, original, Apache-2.0.

A Graphviz template for adding a legend to causal knowledge graphs. It explains what different node shapes, colours, and line styles mean.

In plain words
What is it for?
Use it when documenting causal relationships, such as environmental factors, mechanisms, signals, health indicators, and outcomes.
Why use it?
It helps readers interpret a graph without guessing what each visual style represents.

Skill for Claude Code

Written for Claude Code: installed under .claude/. Also seen: model in frontmatter.

Good fit Use it when documenting causal relationships, such as environmental factors, mechanisms, signals, health indicators, and outcomes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/causify-ai/helpers/graphviz.generate_legend
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 causify-ai/helpers --skill graphviz.generate_legend
Clone the repo
git clone --depth 1 https://github.com/causify-ai/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.

agentmods badge for graphviz.generate_legend

README.md
[![agentmods](https://agentmods.dev/badge/skills/causify-ai/helpers/graphviz.generate_legend/github.svg)](https://agentmods.dev/skills/causify-ai/helpers/graphviz.generate_legend)
Your own site
<a href="https://agentmods.dev/skills/causify-ai/helpers/graphviz.generate_legend"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/graphviz.generate_legend/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.

agentmods 80×15 button for graphviz.generate_legend

Your own site · 80×15
<a href="https://agentmods.dev/skills/causify-ai/helpers/graphviz.generate_legend"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/graphviz.generate_legend.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 1,408 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
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.
How audits are shown
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 5.1 $0.00012 $0.01408
Opus 5.5 $0.00005 $0.00563
Sonnet 5.5 $0.00002 $0.00282
Haiku 4.5 $0.00001 $0.00141

Measured 1mo ago against content hash f087ff1759d2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

graphviz.generate_legend 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 1mo 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.

.claude/skills/graphviz.generate_legend/SKILL.md · 130 lines

How it starts

The opening of the file, as written. The whole thing — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Goal

  • Generate a Graphviz legend for causal knowledge graphs, covering node types and edge styles

Templates

Node Legend

digraph WindTurbineCKG {
  rankdir=LR;
  splines=true;
  nodesep=0.8;
  ranksep=1.2;
  bgcolor="white";

subgraph cluster_legend {
  label="Legend: Node Types";
  fontsize=11;
  fontname="Helvetica";
  color=gray70;
  style="rounded,dashed";
  bgcolor="white";

  // Put every legend node on the SAME rank (same vertical alignment)
  { rank=same;
    leg_exog; leg_op; leg_latent; leg_obs; leg_health; leg_out;
  }

  leg_exog   [label="Exogenous /\nEnvironmental", shape=ellipse, style=filled, fontcolor=black, color=royalblue4, fillcolor=lightsteelblue1];
  leg_op     [label="Operational /\nMechanism", shape=box, style="rounded,filled,solid", fontcolor=black, color=darkgreen, fillcolor=palegreen1];
  leg_latent [label="Latent /\nHidden State", shape=box, style="filled,dashed", fontcolor=gray40, color=gray40, fillcolor=gray90];
  leg_obs    [label="Observable\nSignal", shape=ellipse, style=filled, fontcolor=black, color=darkgreen, fillcolor=palegreen1];
  leg_health [label="Health\nIndicator", shape=ellipse, style=filled, fontcolor=black, color=gray40, fillcolor=gray90];
  leg_out    [label="Outcome /\nTarget", shape=box, penwidth=2, style=filled, fontcolor=black, color=darkorange3, fillcolor=moccasin];

  // Keep left-to-right ordering without changing ranks
  leg_exog   -> leg_op     [style=invis, weight=10];
  leg_op     -> leg_latent [style=invis, weight=10];
  leg_latent -> leg_obs    [style=invis, weight=10];
  leg_obs    -> leg_health [style=invis, weight=10];
  leg_health -> leg_out    [style=invis, weight=10];
}

}

Edge Legend

digraph legend {
    graph [rankdir=TB, nodesep=0.5, ranksep=0.7];
    node [shape=point, width=0, height=0, margin=0];
    edge [dir=forward];

    // Row 1: Direct causation
    {
        rank=same;
        a1 [label="", style=invis];
        b1 [label="", style=invis];
        t1 [shape=plaintext, style=solid, label="Direct causation", fontsize=16, fontname="Arial"];
        a1 -> b1 [style=solid, color=black, penwidth=3, arrowsize=1.2, minlen=3];
        b1 -> t1 [style=invis, minlen=1];
    }

    // Row 2: Uncertain/hypothesized causation
    {
        rank=same;
        a2 [label="", style=invis];
        b2 [label="", style=invis];
        t2 [shape=plaintext, style=solid, label="Uncertain / hypothesized causation", fontsize=16, fontname="Arial"];
        a2 -> b2 [style=dotted, color=black, penwidth=2, arrowsize=1.0, minlen=3];
        b2 -> t2 [style=invis, minlen=1];
    }

    // Row 3: Correlation/association
    {
        rank=same;
        a3 [label="", style=invis];
        b3 [label="", style=invis];
        t3 [shape=plaintext, style=solid, label="Correlation / association (non-causal)", fontsize=16, fontname="Arial"];
        a3 -> b3 [style=dotted, color=gray50, penwidth=2, arrowhead=none, minlen=3];
        b3 -> t3 [style=invis, minlen=1];
    }

    // Row 4: Positive effect
    {
        rank=same;
        a4 [label="", style=invis];
        b4 [label="", style=invis];
        t4 [shape=plaintext, style=solid, label="Positive effect (+ / ++ / +++)", fontsize=16, fontname="Arial"];
        a4 -> b4 [style=solid, color="#228B22", penwidth=3, arrowsize=1.2, minlen=3];
        b4 -> t4 [style=invis, minlen=1];
    }

    // Row 5: Negative effect
    {
        rank=same;
        a5 [label="", style=invis];
        b5 [label="", style=invis];
        t5 [shape=plaintext, style=solid, label="Negative effect (- / -- / ---)", fontsize=16, fontname="Arial"];
        a5 -> b5 [style=solid, color="#DC143C", penwidth=3, arrowsize=1.2, minlen=3];
        b5 -> t5 [style=invis, minlen=1];
    }

    // Row 6: Strength encoding - using <-> instead of Unicode
    {
        rank=same;
        a6 [label="", style=invis];
        b6 [label="", style=invis];
        t6 [shape=plaintext, style=solid, label="Strength encoding (weak <-> strong)", fontsize=16, fontname="Arial"];
        a6 -> b6 [style=solid, color="#228B22", penwidth=3, arrowsize=1.2, minlen=3];
        b6 -> t6 [style=invis, minlen=1];
    }

    // Force vertical ordering and left alignment
    a1 -> a2 -> a3 -> a4 -> a5 -> a6 [style=invis];
    b1 -> b2 -> b3 -> b4 -> b5 -> b6 [style=invis];
}

Read the full file on GitHub · 130 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. 1mo ago Changed · +12 lines · -4 tokens per session f087ff1759d2
  2. 1mo ago First seen · 118 lines · 16 tokens per session scan A a6bcb5b85e91

Subscribe to this mod's changes

graphviz.generate_legend is a skill published in the GitHub repository causify-ai/helpers (150 stars, last pushed yesterday), licensed Apache-2.0. It adds 12 tokens to every session and 1,408 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 2026-09-03.

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