Scientific Toolkit Skill

CommunityPopular

zLanqing

scientific-toolkit-skill

Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation, optimization, publication figures, sensor/time-series data, citation lookup, and common scientific libraries. Use when the user asks for MATLAB code, scientific Python, data analysis, plots, simulations, formulas, statistics, machine learning, optical/physical/materials computation, or reproducible research workflows.

Overview

Publisher	zLanqing
Repository	`codex-claude-academic-skills`
Skill name	`scientific-toolkit-skill`
Stars	4K
Forks	222
Bundled files	218
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
218 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by zLanqing on GitHub. Read the source before you install it.

Installation

Install the Scientific Toolkit Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git /tmp/codex-claude-academic-skills
mkdir -p .claude/skills
cp -r /tmp/codex-claude-academic-skills/scientific-toolkit-skill .claude/skills/scientific-toolkit-skill

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Scientific Toolkit Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Scientific Toolkit Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Scientific Toolkit Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Scientific Toolkit Skill

Scope

Use this skill for科研计算 and software-assisted research:

MATLAB/Octave scripts, debugging, refactoring, signal/image processing, FFT, filtering, matrix computation, simulation, and figure export.
Python scientific workflows with NumPy, SciPy, pandas, matplotlib, seaborn, scikit-learn, statsmodels, SymPy, and related tools.
Statistics, exploratory data analysis, sensor/time-series forecasting, optimization, discrete-event simulation, quantum optics/open quantum systems, materials data, and graph/network analysis.
Literature lookup, citation metadata, BibTeX, and reference verification when it supports coding or research analysis.

Use research-writing-skill for manuscript prose. Use office-academic-skill for Word/PPT deliverables.

Domain Defaults

The user's field is光电信息科学与工程. Prefer examples and checks relevant to:

Optics, optoelectronics, optical communication, optical sensing, fiber sensing, BOTDR/BOTDA, BGS, SPM, dispersion, noise, and deconvolution.
Signal processing, image processing, spectroscopy, detector data, sensor time series, calibration, and uncertainty.
MATLAB simulation and reproducible figure generation for论文/答辩.

Do not fabricate physical parameters, material constants, software menu operations, experimental results, or paper conclusions. When uncertain, ask for the source file or mark the assumption.

General Workflow

Read the provided code, data, README, docs, and project instructions before changing anything.
Identify variables, dimensions, units, input/output paths, random seeds, and expected figures.
Make small, verifiable changes and avoid unrelated refactors.
Prefer mature libraries over hand-rolled numerical methods.
Run a script-level or test-level verification when possible.
Report environment, commands, output paths, generated figures, and known limitations.

MATLAB And Figures

Preserve the original code structure when possible.
Add concise comments for physical meaning, units, assumptions, or formula sources.
Centralize key parameters and avoid hardcoded absolute paths.
Add rng for stochastic simulations when reproducibility matters.
For publication figures, export both high-resolution .png and vector .svg when feasible.
Check axes, units, legends, sampling rate, line width, font, color, and image resolution.

For MATLAB/Octave details, use references/scientific-skills/matlab/SKILL.md.

Python Scientific Modules

Load only the relevant bundled reference:

Plotting and publication figures: matplotlib, seaborn, scientific-visualization.
Statistics and time series: statistical-analysis, statsmodels, timesfm-forecasting.
Machine learning: scikit-learn.
Symbolic math and formulas: sympy.
Exploratory data analysis: exploratory-data-analysis.
Optimization: pymoo.
Simulation: simpy.
Quantum optics/open quantum systems: qutip.
Materials/crystal/band/DOS workflows: pymatgen.
Graphs/networks/citation graphs: networkx.
FITS or astronomical/optical imaging style data: astropy.
Spreadsheet/PDF utilities: xlsx, pdf.
Literature/citation support: paper-lookup, citation-management, literature-review.

Some bundled references mention optional installs such as uv pip install ... or optional API keys for higher rate limits. Do not install packages, use cloud APIs, or send user data to external services unless the current task requires it and the user agrees.

Safety Rules

Never expose or commit API keys, tokens, private data, or unpublished paper content.
Do not overwrite original data, code, Word/PPT, or figures. Write versioned outputs.
Do not delete or recursively clean user files without explicit confirmation.
For external lookups, prefer open APIs and official documentation; clearly distinguish live lookup results from local inference.

Verification

For code:

Run the relevant script or a minimal example.
Check generated files exist and are readable.
Inspect plots for axes, units, legends, and plausible dimensions.

For research analysis:

State software versions when known.
List input files and commands.
Mark assumptions and uncertain parameters.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

and 140 more files.

Frequently asked questions

What does the Scientific Toolkit Skill AI skill do?

Why use Scientific Toolkit Skill on TypingMind?

Because you install it once and use it with any model. Scientific Toolkit Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Scientific Toolkit Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/zLanqing/codex-claude-academic-skills/tree/main/scientific-toolkit-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Scientific Toolkit Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Scientific Toolkit Skill?

As many as you like. As long as a model supports skills, you can use Scientific Toolkit Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Scientific Toolkit Skill AI skill free?

Yes. It is published on GitHub by zLanqing under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Scientific Toolkit Skill

Scope

Domain Defaults

General Workflow

MATLAB And Figures

Python Scientific Modules

Safety Rules

Verification

Bundled files

Frequently asked questions

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