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multi-panel-figure-assembler

Assemble 6 sub-figures (A–F) into a high-resolution composite figure with consistent labels, padding, and publication-ready DPI.

Install / Use

npx skills add aipoch/medical-research-skills --skill multi-panel-figure-assembler

Installs into whichever agent you are using.

About this skill
📄

SKILL.md

Installable skill definition

Quality Score

92/100

Supported Platforms

Universal

Our assessment of multi-panel-figure-assembler

multi-panel-figure-assembler scores 92/100 on our quality scale, 88th of 255 Customer Support skills we index (top 35%).

Its SKILL.md is 5.9 KB long, well organised into 16 sections with 4 code examples: a thorough specification that gives an agent plenty to work with.

With 1,916 GitHub stars, it is one of the more widely adopted skills in the catalogue.

Substance
29/30
Structure
20/20
Description
15/15
Adoption
14/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 12 days ago, so multi-panel-figure-assembler is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 100/100, with no cautions. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

Safety scan

No issues found

Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands.

Automated pattern scan on 2026-09-30. It catches known dangerous patterns, not every risk — read a skill before letting an agent act on it.

multi-panel-figure-assembler compared with similar skills

All 4 of these similar skills score higher than multi-panel-figure-assembler; compare them before choosing.

SkillScoreStarsUpdatedFormat
multi-panel-figure-assembler (this skill)by aipoch921.9k12d agoSKILL.md
Agent-Reachby Panniantong10086.2k14d agoCLAUDE.md
LocalAIby mudler10049.3ktodayMCP Server
algorithmic-artby anthropics100177.9k7d agoSKILL.md
pptxby anthropics100177.9k7d agoSKILL.md

Frequently asked questions

How do I install multi-panel-figure-assembler?
Run npx skills add aipoch/medical-research-skills --skill multi-panel-figure-assembler. The install tabs above show the steps for each supported agent.
Which AI agents does multi-panel-figure-assembler work with?
It is written for Universal, as a SKILL.md file. Other agents that read the same format can often use it too.
Is multi-panel-figure-assembler safe to use?
Our scan of the whole file found no instruction hijacking, hidden characters, credential access, data exfiltration or destructive commands. It is MIT-licensed and scores 100/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is multi-panel-figure-assembler still maintained?
The repository was last updated 12 days ago, so multi-panel-figure-assembler is actively maintained.

name: multi-panel-figure-assembler description: Assemble 6 sub-figures (A–F) into a high-resolution composite figure with consistent labels, padding, and publication-ready DPI. license: MIT author: AIPOCH

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

Multi-Panel Figure Assembler

Assemble 6 sub-figures (A–F) into a high-resolution composite figure with consistent styling, labels, and publication-ready output.

Input Validation

This skill accepts: exactly 6 image files (panels A–F) in supported formats, plus an output path, for assembly into a composite figure.

If the request does not involve assembling exactly 6 image panels into a composite figure — for example, asking to generate plots from data, edit image content, or assemble a different number of panels — do not proceed. Instead respond:

"multi-panel-figure-assembler is designed to assemble exactly 6 sub-figures (A–F) into a composite image. Your request appears to be outside this scope. Please provide 6 image files and an output path, or use a more appropriate tool for your task. For plot generation from data, consider matplotlib, seaborn, or R ggplot2."

Do not attempt any data processing or partial analysis before emitting this refusal. Validate scope first — this is the absolute first action before any other processing.

When to Use

  • Combining individual plot panels into a single composite figure for publication
  • Standardizing label fonts, padding, and DPI across a figure set
  • Producing 2×3 or 3×2 grid layouts from existing image files
  • Automating figure assembly to ensure reproducibility

Note: This skill is fixed to exactly 6 panels (A–F labeling convention). For 4-panel (2×2) or 9-panel (3×3) layouts, a future --panels parameter may be added.

Workflow

  1. Validate input — confirm scope and that exactly 6 panels are provided before any processing. Do not generate any output before this check.
  2. Confirm the user objective, required inputs, and non-negotiable constraints.
  3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
  4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
  5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Usage

# Basic 2×3 layout
python scripts/main.py --input A.png B.png C.png D.png E.png F.png --output figure.png

# 3×2 layout at 600 DPI
python scripts/main.py --input A.png B.png C.png D.png E.png F.png --output figure.png --layout 3x2 --dpi 600

# Custom label styling
python scripts/main.py --input A.png B.png C.png D.png E.png F.png --output figure.png \
  --label-size 32 --label-position topright --padding 20 --border 4

Parameters

| Parameter | Type | Default | Description | |-----------|------|---------|-------------| | --input / -i | 6 paths | Required | Input image paths for panels A–F | | --output / -o | path | Required | Output composite file path | | --layout / -l | enum | 2x3 | Grid layout: 2x3 or 3x2 | | --dpi / -d | int | 300 | Output DPI | | --label-font | str | Arial | Font family for panel labels | | --label-size | int | 24 | Font size for panel labels | | --label-position | str | topleft | Label position: topleft, topright, bottomleft, bottomright | | --padding / -p | int | 10 | Padding between panels (pixels) | | --border / -b | int | 2 | Border width around each panel (pixels) | | --bg-color | str | white | Background color (white/black/hex) | | --label-color | str | black | Label text color |

Supported Formats

  • Input: PNG, JPG, JPEG, BMP, TIFF, GIF
  • Output: PNG (recommended), JPG, TIFF

Quick Check

python -m py_compile scripts/main.py
python scripts/main.py --help
python -c "import PIL; print('Pillow OK')"

Error Handling

  • If fewer or more than 6 input images are provided, state the count mismatch and stop.
  • If any input file path contains ../ or points outside the workspace, reject with a path traversal warning.
  • If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
  • If scripts/main.py fails (e.g., returncode=2 from missing required args), report the exact error and provide the correct command syntax.
  • If PIL/Pillow is not installed, print: pip install Pillow numpy and exit with a non-zero code.
  • Do not fabricate files, citations, or execution outcomes.

Fallback Template

When execution fails or inputs are incomplete, respond with this structure:

FALLBACK REPORT
───────────────────────────────────────
Objective      : [restate the goal]
Blocked by     : [exact missing input or error — e.g., only 4 of 6 panels provided]
Partial result : [what can be completed — e.g., layout plan, parameter defaults]
Assumptions    : [layout, DPI, label style assumed]
Constraints    : [format requirements, DPI minimum]
Risks          : [aspect ratio mismatch, font availability]
Unresolved     : [what still needs user input]
Next step      : [minimum action needed to unblock]
───────────────────────────────────────

Response Template

Use the following fixed structure for non-trivial requests:

  1. Objective
  2. Inputs Received
  3. Assumptions
  4. Workflow
  5. Deliverable
  6. Risks and Limits
  7. Next Checks

If the request is simple, compress the structure but keep assumptions and limits explicit when they affect correctness.

Notes

  • Input images are automatically resized to match the largest dimension while maintaining aspect ratio
  • For best results, use input images with similar aspect ratios
  • Label fonts require the font to be available on the system; Arial falls back to DejaVu Sans if unavailable
  • PNG output preserves transparency if any input images have alpha channels

Prerequisites

pip install Pillow numpy

Related Skills

View on GitHub
GitHub Stars1.9k
CategoryCustomer
Updated12d ago
Forks175

Languages

Python

Trust signals

100/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

No cautions