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Agent skill

Scientific Toolkit Skill

by zLanqing in zLanqing/codex-claude-academic-skills

Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation…

MITAuto-check passedData & Analytics

Install Scientific Toolkit Skill

skills CLI
$ npx skills add zLanqing/codex-claude-academic-skills --skill scientific-toolkit-skill -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install zLanqing/codex-claude-academic-skills scientific-toolkit-skill --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/scientific-toolkit-skill .claude/skills/scientific-toolkit-skill && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
scientific-toolkit-skill
GitHub stars
4.7k
Token cost
~1.2k tokens
SKILL.md length
497 words
Files
3 (incl. references)
Skills in repo
17
Repo updated
First seen
Licence
MIT

At a glance

Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation…

  • Works in 6 steps: Read the provided code, data, README,… → Identify variables, dimensions, units,… → Make small, verifiable changes and avoid… → …
  • The user asks for MATLAB code
  • SKILL.md covers Scope, Domain Defaults, General Workflow and MATLAB And Figures, plus 3 more sections
  • Calls uv

What it does

Scientific Toolkit Skill is an agent skill from zLanqing/codex-claude-academic-skills. 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.

Its SKILL.md is about 1.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 4 other files, including reference files (for example `agents/openai.yaml`).

It sits in Data & Analytics, covering Forecasting and time series, Data visualization and Statistics. It works with Python. The repository describes itself as: 本仓库包含三个面向学术科研人员的Skills，覆盖从文献阅读、论文写作到科学计算的完整研究工作流。office-academic-skill 负责论文阅读报告与学术 PPT/Word 文档生成；research-writing-skill 提供论文写作、润色与审稿回复辅助；scientific-toolkit-skill 整合 MATLAB/Python… The licence is MIT.

When your agent uses it

  • The user asks for MATLAB code
  • Scientific Python
  • Machine learning
  • Optical/physical/materials computation

Example prompts

  • “/scientific-toolkit-skill”

Requirements

  • Python 3

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

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

What it can do on your machine

Read from SKILL.md and the folder at commit 7ed6377. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • uv

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Scientific Toolkit Skill loads about 1.2k tokens when it runs. Until then it costs about 133 tokens; SKILL.md has 497 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~133
When it runs · the whole SKILL.md, loaded when a task matches
~1.2k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from zLanqing/codex-claude-academic-skills at commit 7ed6377, republished under its MIT licence (© zLanqing). 497 words, ~1,201 tokens.

Download SKILL.mdSave it as .claude/skills/scientific-toolkit-skill/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
scientific-toolkit-skill
description
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.

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

  1. Read the provided code, data, README, docs, and project instructions before changing anything.
  2. Identify variables, dimensions, units, input/output paths, random seeds, and expected figures.
  3. Make small, verifiable changes and avoid unrelated refactors.
  4. Prefer mature libraries over hand-rolled numerical methods.
  5. Run a script-level or test-level verification when possible.
  6. 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.

Show full SKILL.md (209 more words)Show less

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.

© zLanqing, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files (references) in scientific-toolkit-skill of zLanqing/codex-claude-academic-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/scientific-skills

Open the folder on GitHubat commit 7ed6377

Compare with similar skills

Scientific Toolkit Skill next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Scientific Toolkit Skill compared with similar skills
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Data Sciencetravisjneuman/.claude101—~2.3kAutomated safety check: PassMIT
Bio Temporal Genomics Temporal GrnGPTomics/bioSkills1.2k1 repos~5kAutomated safety check: PassMIT
Python Executorcortega26/chile-hub1132 repos~1.5kAutomated safety check: PassMIT
Senior Data ScientistRaidriar7170/hermes-skilleval1255 repos~1.4kAutomated safety check: PassMIT

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Works with

Categories

Questions about Scientific Toolkit Skill

What does Scientific Toolkit Skill do?

Research computing toolkit for optoelectronic information science and engineering, MATLAB/Octave, Python scientific analysis, signal processing, image processing, statistics, simulation…. Scientific Toolkit Skill is an agent skill from zLanqing/codex-claude-academic-skills. 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.

When should I use Scientific Toolkit Skill?

Scientific Toolkit Skill fits situations like: the user asks for MATLAB code; scientific Python; machine learning; optical/physical/materials computation.

How do I install Scientific Toolkit Skill in Claude Code?

Run `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-toolkit-skill -a claude-code`. Or copy the skill folder (scientific-toolkit-skill in zLanqing/codex-claude-academic-skills) into .claude/skills/scientific-toolkit-skill in your project. Claude Code loads it when a task matches its description.

How do I install Scientific Toolkit Skill in Codex?

Run `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-toolkit-skill -a codex`. Or copy the skill folder (scientific-toolkit-skill in zLanqing/codex-claude-academic-skills) into .agents/skills/scientific-toolkit-skill in your project. Codex loads it when a task matches its description.

Can I use Scientific Toolkit Skill in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add zLanqing/codex-claude-academic-skills --skill scientific-toolkit-skill -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/scientific-toolkit-skill, .gemini/skills/scientific-toolkit-skill, .github/skills/scientific-toolkit-skill and .opencode/skills/scientific-toolkit-skill in your project.

What does Scientific Toolkit Skill need to run?

Going by SKILL.md and its folder, Scientific Toolkit Skill needs the command-line tools its instructions call (uv). Our summary lists: Python 3.

Does Scientific Toolkit Skill access the network?

SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Scientific Toolkit Skill safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Scientific Toolkit Skill use?

Scientific Toolkit Skill is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scientific Toolkit Skill use?

About 1.2k tokens (SKILL.md is roughly 4.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Scientific Toolkit Skill?

Skills that share tags, products or a category with Scientific Toolkit Skill: Data Scientist (davila7/claude-code-templates, 32k stars), Data Science (travisjneuman/.claude, 101 stars), Bio Temporal Genomics Temporal Grn (GPTomics/bioSkills, 1.2k stars) and Python Executor (cortega26/chile-hub, 113 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scientific Toolkit Skill?

zLanqing (a GitHub user) maintains it in zLanqing/codex-claude-academic-skills, which has 4,671 GitHub stars. The repository holds 17 skills in this directory. The repository was last updated on May 14, 2026.

Source: zLanqing/codex-claude-academic-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.