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DataPython

Thorlabs Blender Optical Path

Thorlabs Blender Optical Path is a Data skill for Claude Code, published by k-telux in OpticalModeler.

212 stars2 forkson k-telux/OpticalModelerAdded 2026/08/11Repository updated 2026/07/26

View source on GitHub Download repo ZIP

agent-skillsblenderoptical-designoptical-engineeringphotonicsscientific-visualizationthorlabs

Install in seconds

Install Thorlabs Blender Optical Path

Copy Thorlabs Blender Optical Path into your Claude Code skills folder. Run the command in your terminal, or review the source on GitHub before installing.

terminal

npx degit https://github.com/k-telux/OpticalModeler/tree/main/skills/thorlabs-blender-optical-path ~/.claude/skills/thorlabs-blender-optical-path

Requires Node.js. Downloads this skill only — not the rest of the repository — into your Claude Code skills folder.

Without Node.js

git clone https://github.com/k-telux/OpticalModeler.git

Clones the whole repository, then copy the skill’s own directory into your skills folder yourself.

In this catalog

Source file: skills/thorlabs-blender-optical-path/SKILL.md in k-telux/OpticalModeler
Installs to: ~/.claude/skills/thorlabs-blender-optical-path
Collection: One of 3 skills cataloged from this repository
Category: Data — 668 skills

What Thorlabs Blender Optical Path does

Converts 2D optical schematics into physically accurate Blender scenes using manufacturer CAD, enabling auditable visualization, optical topology analysis, and publication-ready renders for photonics experiments.

Thorlabs Blender Optical Path is cataloged under Data on DirSkills. Thorlabs Blender Optical Path comes from a repository tagged agent-skills, blender, optical-design, optical-engineering and photonics.

Documentation

README

Excerpt

Thorlabs Blender Optical Path

Convert a 2D optical schematic into a physically explainable, independently auditable Blender scene.

Load the relevant references

Read physical-gates.md before geometry, CAD placement, or mechanical review.
Read evidence-contract.md before declaring any PASS or preparing a release.
Read history-derived-rules.md when revising an existing scene or when old fixes may have regressed.
Read project-case-study.md for the complete G1/G2 2D-to-3D example.

Authority and revision rules

This is the opening of the README. Read the full README on GitHub.

Frequently asked about Thorlabs Blender Optical Path

What else does k-telux publish alongside Thorlabs Blender Optical Path?
Thorlabs Blender Optical Path is one of 3 skills that DirSkills catalogs from k-telux/OpticalModeler, the repository it ships in. Its siblings there include Thorlabs Blender Optical Path and Thorlabs Optical Path. Each one is a separate skill with its own page in this directory, installs the same way Thorlabs Blender Optical Path does, and is maintained by k-telux in that same repository. The rest of the collection is listed on the k-telux/OpticalModeler page.
How does Thorlabs Blender Optical Path compare to other Data skills?
Thorlabs Blender Optical Path ranks #614 by stars among the 668 Data skills in this catalog. The most-starred ones next to it are Benchmark Methodology, Jupyter Notebook and Solana. DirSkills ranks by the star count of the repository each skill ships in, so that order reflects how popular those repositories are rather than any review of Thorlabs Blender Optical Path against them. Open each page to compare what they document and how they install.

More from k-telux/OpticalModeler

Thorlabs Blender Optical Path is one of 3 skills cataloged on DirSkills from k-telux/OpticalModeler.

See all 3 skills →

Thorlabs Blender Optical Path

Convert 2D optical schematics into physically plausible Blender optical benches with Thorlabs components, including audit, evidence, and Nature-style rendering.

Thorlabs Optical Path

Converts 2D optical schematics into physically valid 3D optical tables in Blender using Thorlabs components, with fail-closed auditing and Nature-style rendering for scientific visualization.

More Data skills

Thorlabs Blender Optical Path is one of 668 Data skills cataloged on DirSkills, ranked #614 by stars among them.

Browse all Data skills →

Benchmark Methodology

Benchmark Methodology scores competitors across nine weighted dimensions using explicit 1–5 rubrics and a tension plot. Use it after competitive-platform-analysis to produce comparable, evidence-anchored profile cards for a competitive report.

Jupyter Notebook

Jupyter Notebook provides a stateful Python REPL via a live Jupyter kernel (hamelnb) so variables persist across executions. Use it for iterative data exploration, API inspection, and complex code development.

Solana

Solana queries wallets, token holdings, transactions, NFTs, whale transfers, and network stats on the Solana blockchain with USD pricing via CoinGecko. Use it when you need to inspect an address, verify a transaction, check token metadata or prices, or monitor network health.