figure.create_tikz_from_description

figure.create_tikz_from_description is a skill for Claude Code from causify-ai/helpers. It costs 11 tokens per session (658 once invoked), scanned A, original, Apache-2.0.

A workflow for turning an image or written description into TikZ LaTeX code. TikZ is a LaTeX tool for creating diagrams and technical figures from text-based instructions.

In plain words
What is it for?
Use it to create or recreate publication-quality diagrams, plots, visualizations, and LaTeX figures, then render and refine them.
Why use it?
It helps produce editable, reproducible figures instead of redrawing diagrams manually.

Skill for Claude Code

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

Needs its repository: it runs a file that does not travel with it, so clone the repository first. The line is > ./helpers_root/dev_scripts_helpers/documentation/dockerized_tikz_to_bitmap.py \.

Good fit Use it to create or recreate publication-quality diagrams, plots, visualizations, and LaTeX figures, then render and refine them.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.

Clone the repo
git clone --depth 1 https://github.com/causify-ai/helpers
agentmods
npx agentmods add skills/causify-ai/helpers/figure.create_tikz_from_description

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 figure.create_tikz_from_description

README.md
[![agentmods](https://agentmods.dev/badge/skills/causify-ai/helpers/figure.create_tikz_from_description.svg)](https://agentmods.dev/skills/causify-ai/helpers/figure.create_tikz_from_description)
Your own site
<a href="https://agentmods.dev/skills/causify-ai/helpers/figure.create_tikz_from_description"><img src="https://agentmods.dev/badge/skills/causify-ai/helpers/figure.create_tikz_from_description.svg" alt="Measured on agentmods" height="20"></a>
Per session 11 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 658 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 $0.00011 $0.00658
Opus 5 $0.00005 $0.00329
Sonnet 5 $0.00002 $0.00132
Haiku 4.5 $0.00001 $0.00066

Measured 8d ago against content hash 9bcb4d42c0c0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

figure.create_tikz_from_description 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 8d 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/figure.create_tikz_from_description/SKILL.md · 96 lines

How it starts

The opening of the file, as written. The whole thing — 96 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

  • Convert images or textual descriptions into publication-quality TikZ LaTeX code Generate a compilable LaTeX file, render it to PNG, and iteratively refine the output to match the input precisely

When to Use

Use this skill when you need to:

  • Create publication-quality diagrams, plots, or visualizations
  • Convert hand-drawn sketches or existing images into reproducible TikZ code
  • Generate diagrams for inclusion in LaTeX documents

When NOT to Use

Do not use this skill for:

  • Complex photographs requiring photorealistic rendering
  • Plots from large datasets (use dedicated plotting libraries instead)
  • Diagrams requiring advanced 3D visualization

Workflow

Step 1: Generate TikZ Code

Generate valid LaTeX code using the TikZ package. Wrap the code in a complete minimal working example:

\documentclass{standalone}
\usepackage{tikz}
\begin{document}
\begin{tikzpicture}
...
\end{tikzpicture}
\end{document}

Preserve layout accurately

  • If converting from an image <image>, reproduce the layout precisely
  • Preserve proportions, relative positions, and symmetry
  • Use coordinates and scaling where appropriate
  • Approximate complex curves with TikZ paths when needed

Use appropriate TikZ features

  • Nodes for labeled elements
  • draw, fill, shade for shapes
  • Arrows and edge styles for connections
  • Rounded corners for blocks: [rounded corners=1cm]
  • positioning and calc libraries if helpful

Keep code clean and readable

  • Use indentation for nested structures
  • Define reusable styles for repeated elements

Handle ambiguity

  • Make reasonable assumptions about unclear inputs
  • Prioritize clarity and visual correctness over perfection

Step 2: Save the File

  • Save the generated LaTeX code to ./tikz_figure.tex in the current directory (not in .claude/). Output only valid TikZ code without markdown formatting or explanations

Step 3: Render to Image

  • Generate a PNG image using the rendering script:
    > ./helpers_root/dev_scripts_helpers/documentation/dockerized_tikz_to_bitmap.py \
        -i tikz_figure.tex \
        -o output.png
    

Read the full file on GitHub · 96 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. 8d ago First seen · 96 lines · 11 tokens per session scan A 9bcb4d42c0c0

Subscribe to this mod's changes

figure.create_tikz_from_description is a skill published in the GitHub repository causify-ai/helpers (145 stars, last pushed yesterday), licensed Apache-2.0. It adds 11 tokens to every session and 658 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens