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Agent skill

Scientific Schematics

by aipoch in aipoch/medical-research-skills

Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.

MITAuto-check passedData & Analytics

Install Scientific Schematics

skills CLI
$ npx skills add aipoch/medical-research-skills --skill scientific-schematics -a claude-code

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

GitHub CLI
$ gh skill install aipoch/medical-research-skills scientific-schematics --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/aipoch/medical-research-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/scientific-skills/Other/scientific-schematics .claude/skills/scientific-schematics && 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-schematics
GitHub stars
2k
Token cost
~987 tokens
SKILL.md length
335 words
Files
5 (incl. scripts, references)
Skills in repo
578
Repo updated
First seen
Licence
MIT

At a glance

Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.

  • Works in 4 steps: Set the OpenRouter API key → Run the generator (journal/poster) → Override the generation model → …
  • Tasks that involve Data visualization
  • SKILL.md covers When to Use, Key Features, Dependencies and Example Usage, plus 1 more section
  • Runs Python scripts from its folder; calls python; needs OPENROUTER_API_KEY

What it does

Scientific Schematics is an agent skill from aipoch/medical-research-skills. Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.

Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `references/best_practices.md`, `references/diagram_types.md` and `scientific-schematics_audit_result_v1.json`).

It sits in Data & Analytics, covering Data visualization and Diagrams. The repository describes itself as: Hundreds of agent skills for medical research, including protocol design, data analysis, evidence insights, and academic writing. The licence is MIT.

When your agent uses it

  • Tasks that involve Data visualization
  • Tasks that involve Diagrams

Example prompts

  • “Use the scientific-schematics skill to automate publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need…”
  • “/scientific-schematics”

Requirements

  • Python 3
  • A credential in OPENROUTER_API_KEY

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Set the OpenRouter API key
  2. Run the generator (journal/poster)
  3. Override the generation model
  4. (Optional) Override both generator and reviewer

What it can do on your machine

Read from SKILL.md and the folder at commit 686e09d. 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

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

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

  • Network

    No URLs in SKILL.md.

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

  • Credentials

    Names these keys or tokens, usually read from environment variables:

    • OPENROUTER_API_KEY

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

Context cost

Scientific Schematics loads about 987 tokens when it runs, and up to ~1.2k if it reads all its reference files. Until then it costs about 49 tokens; SKILL.md has 335 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~49
When it runs · the whole SKILL.md, loaded when a task matches
~987
With references · SKILL.md plus every file in references/, read only if the agent opens them
~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); the scripts in this folder are not scanned.

SKILL.md

The full file from aipoch/medical-research-skills at commit 686e09d, republished under its MIT licence (© aipoch). 335 words, ~987 tokens.

Download SKILL.mdSave it as .claude/skills/scientific-schematics/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
name
scientific-schematics
description
Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.
license
MIT
author
AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Scientific Schematics Skill

When to Use

  • Creating journal-ready figures (clean typography, consistent styling, high resolution) from a short textual description.
  • Producing poster-friendly diagrams that prioritize readability at distance (larger labels, stronger contrast).
  • Drafting neural network architecture schematics (e.g., Transformer blocks, attention modules) for papers or slides.
  • Generating biological pathway visuals (e.g., Krebs cycle) with iterative quality review.
  • Rapidly iterating on a diagram concept when you need AI-assisted refinement loops instead of manual redraws.

Key Features

  • Text-to-diagram automation: Converts a natural-language prompt into a publication-quality schematic.
  • Iterative generate → review → refine loop: Automatically improves the figure until a quality threshold is met.
  • Document-type aware critique: Reviewer feedback adapts to journal vs poster requirements.
  • Model-configurable pipeline: Choose separate LLMs for generation and vision-based review.
  • Output validation: Performs final checks (e.g., resolution/accessibility considerations) before saving to figures/.
  • Reference guidance:
    • Best practices: references/best_practices.md
    • Supported diagram categories: references/diagram_types.md

Dependencies

  • Python 3.10+ (recommended)
  • Python packages:
    • pillow (PIL)
    • matplotlib
    • requests
  • Environment:
    • OPENROUTER_API_KEY (required)

Example Usage

1) Set the OpenRouter API key

Windows (PowerShell)

powershell
$env:OPENROUTER_API_KEY="your_key_here"

Linux/macOS

bash
export OPENROUTER_API_KEY="your_key_here"
2) Run the generator (journal/poster)
bash
python scripts/generate_schematic.py "Transformer architecture with attention mechanism" --doc-type journal
3) Override the generation model
bash
python scripts/generate_schematic.py "Krebs cycle" --doc-type journal --generator anthropic/claude-3.5-sonnet
4) (Optional) Override both generator and reviewer
bash
python scripts/generate_schematic.py "Flowchart of a clinical trial enrollment pipeline" \
  --doc-type poster \
  --generator google/gemini-2.0-flash-001 \
  --reviewer google/gemini-2.0-flash-001

Implementation Details

Pipeline Stages

  1. Generation

    • A code-capable LLM converts the prompt into a diagram image.
    • Default generator model: google/gemini-2.0-flash-001.

  2. Review

    • A vision-capable LLM evaluates the generated image against the target --doc-type.
    • Default reviewer model: google/gemini-2.0-flash-001.
    • The reviewer returns actionable critique and a numeric quality score.

  3. Refinement Loop

    • If the score is below the acceptance threshold (e.g., 8.5/10), the system re-enters generation using the reviewer’s feedback as constraints.
    • This repeats until the threshold is met or the run terminates by internal stopping conditions.

  4. Finalization

    • Performs final checks such as resolution suitability and accessibility-oriented considerations (e.g., legibility).
    • Saves the final artifact to the figures/ directory.
Key Parameters
  • --doc-type <journal|poster>: Controls review criteria (e.g., density/precision for journals vs readability/scale for posters).
  • --generator <model_id>: Model used to produce the diagram.
  • --reviewer <model_id>: Model used to critique the diagram.
  • Quality threshold: A numeric cutoff (example: 8.5/10) that determines whether refinement continues.

© aipoch, 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 4 other files (scripts, references) in scientific-skills/Other/scientific-schematics of aipoch/medical-research-skills.

  • SKILL.md
  • references/best_practices.md
  • references/diagram_types.md
  • scientific-schematics_audit_result_v1.json
  • scripts/generate_schematic.py

Open the folder on GitHubat commit 686e09d

Compare with similar skills

Scientific Schematics 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 Schematics compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Scientific Schematics this skillaipoch/medical-research-skills2k—~987Automated safety check: PassMIT
Academic Figurejoshua-zyy/academic-paper-writer115—~816Automated safety check: PassMIT
Scientific Schematicsjimmc414/Kosmos595—~16kAutomated safety check: NotesNone
Openai Dotcom Vizyzlnew/infra-skills149—~1.3kAutomated safety check: PassNone
Scientific SchematicsK-Dense-AI/claude-scientific-writer2.4k1 repos~4.3kAutomated safety check: NotesMIT
Claude D3js Skillaiskillstore/marketplace4304 repos~5.4kAutomated safety check: PassNone

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Categories

Questions about Scientific Schematics

What does Scientific Schematics do?

Automates publication-quality scientific diagrams (e.g., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description. Scientific Schematics is an agent skill from aipoch/medical-research-skills., flowcharts, architectures, pathways) when you need journal/poster-ready visuals from a natural-language description.

When should I use Scientific Schematics?

Scientific Schematics fits situations like: tasks that involve Data visualization; tasks that involve Diagrams.

How do I install Scientific Schematics in Claude Code?

Run `npx skills add aipoch/medical-research-skills --skill scientific-schematics -a claude-code`. Or copy the skill folder (scientific-skills/Other/scientific-schematics in aipoch/medical-research-skills) into .claude/skills/scientific-schematics in your project. Claude Code loads it when a task matches its description.

How do I install Scientific Schematics in Codex?

Run `npx skills add aipoch/medical-research-skills --skill scientific-schematics -a codex`. Or copy the skill folder (scientific-skills/Other/scientific-schematics in aipoch/medical-research-skills) into .agents/skills/scientific-schematics in your project. Codex loads it when a task matches its description.

Can I use Scientific Schematics 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 aipoch/medical-research-skills --skill scientific-schematics -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-schematics, .gemini/skills/scientific-schematics, .github/skills/scientific-schematics and .opencode/skills/scientific-schematics in your project.

What does Scientific Schematics need to run?

Going by SKILL.md and its folder, Scientific Schematics needs Python for the scripts in its folder, the command-line tools its instructions call (python) and credentials named OPENROUTER_API_KEY. Our summary lists: Python 3; A credential in OPENROUTER_API_KEY.

Does Scientific Schematics access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Scientific Schematics 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. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Scientific Schematics use?

Scientific Schematics is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Scientific Schematics use?

About 987 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 243 tokens, read only when the agent opens those files.

What are the alternatives to Scientific Schematics?

Skills that share tags, products or a category with Scientific Schematics: Academic Figure (joshua-zyy/academic-paper-writer, 115 stars), Scientific Schematics (jimmc414/Kosmos, 595 stars), Openai Dotcom Viz (yzlnew/infra-skills, 149 stars) and Scientific Schematics (K-Dense-AI/claude-scientific-writer, 2.4k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Scientific Schematics?

aipoch (a GitHub organization) maintains it in aipoch/medical-research-skills, which has 1,978 GitHub stars. The repository holds 578 skills in this directory. The repository was last updated on September 17, 2026.

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