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scientific-toolkit-skill

scientific-toolkit-skill is a skill for Codex from zLanqing/codex-claude-academic-skills. It costs 96 tokens per session (998 once invoked), scanned C, original, MIT.

A research-computing toolkit for MATLAB, GNU Octave, and Python. It supports scientific data analysis, simulations, signal and image processing, statistics, optimization, figures, and citation lookup.

In plain words
What is it for?
Use it to write or debug numerical scripts, analyze sensor and time-series data, process signals and images, run simulations, optimize models, create publication figures, and verify citations.
Why use it?
It helps developers handle common research tasks without assembling separate guidance for each scientific library or workflow.

Skill for Codex

Written for Codex: agents/openai.yaml present.

not rated 3.7krepo +201 4mo ago C scan Socket: passSnyk: warn 96 tokens original MIT

Good fit Use it to write or debug numerical scripts, analyze sensor and time-series data, process signals and images, run simulations, optimize models, create publication figures, and verify citations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill
View source ↗ zLanqing/codex-claude-academic-skills
About the project

zLanqing/codex-claude-academic-skills is a collection of three skills for academic writing, editable Word and PowerPoint documents, and scientific computing with MATLAB and Python. Chinese-speaking researchers use it for literature reports, papers, presentations, data analysis, simulations, and publication figures in Claude Code or Codex. The catalogue contains the project's academic workflow skills.

zLanqing/codex-claude-academic-skills · 3,735 stars · on GitHub

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 zLanqing/codex-claude-academic-skills --skill scientific-toolkit-skill
Clone the repo
git clone --depth 1 https://github.com/zLanqing/codex-claude-academic-skills

Made for: Codex.

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 scientific-toolkit-skill

README.md
[![agentmods](https://agentmods.dev/badge/skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill/github.svg)](https://agentmods.dev/skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill)
Your own site
<a href="https://agentmods.dev/skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill"><img src="https://agentmods.dev/badge/skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill/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 scientific-toolkit-skill

Your own site · 80×15
<a href="https://agentmods.dev/skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill"><img src="https://agentmods.dev/badge/skills/zlanqing/codex-claude-academic-skills/scientific-toolkit-skill.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 96 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 998 The whole file, excluding the scripts and references it only reads on demand.
Security scan C 1 finding. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • Socket pass 19 Jun 2026
  • Snyk warn 15 May 2026
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 $0.00096 $0.00998
Opus 5 $0.00048 $0.00499
Sonnet 5 $0.00019 $0.00200
Haiku 4.5 $0.00010 $0.00100

Measured 11d ago against content hash f3806f059a10, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade C, and why

scientific-toolkit-skill scanned grade C with 1 finding 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 11d ago.

The scan reads SKILL.md. This mod also ships 12 executable files (references/scientific-skills/citation-management/scripts/doi_to_bibtex.py, references/scientific-skills/citation-management/scripts/extract_metadata.py, references/scientific-skills/citation-management/scripts/format_bibtex.py, …), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Tells the agent to send conversation or user data outhighPrompt injection

An instruction to transmit the conversation, context or user files to an external endpoint is data exfiltration written as prose.

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 curre
scientific-toolkit-skill/SKILL.md · 90 lines

How it starts

The opening of the file, as written. The whole thing — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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.

Read the full file on GitHub · 90 lines

Files

What ships with it

60 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 11d ago First seen · 90 lines · 96 tokens per session scan C f3806f059a10

Subscribe to this mod's changes

scientific-toolkit-skill is a skill published in the GitHub repository zLanqing/codex-claude-academic-skills (3,735 stars, last pushed 4mo ago), licensed MIT. It adds 96 tokens to every session and 998 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent to send conversation or user data out). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Scientific
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