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-assemblerInstalls into whichever agent you are using.
SKILL.md
Installable skill definition
Quality Score
Category
Customer SupportSupported Platforms
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.
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 foundOur 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.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| multi-panel-figure-assembler (this skill)by aipoch | 92 | 1.9k | 12d ago | SKILL.md |
| Agent-Reachby Panniantong | 100 | 86.2k | 14d ago | CLAUDE.md |
| LocalAIby mudler | 100 | 49.3k | today | MCP Server |
| algorithmic-artby anthropics | 100 | 177.9k | 7d ago | SKILL.md |
| pptxby anthropics | 100 | 177.9k | 7d ago | SKILL.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.
Skill content
View source on GitHubname: 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
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
- Validate input — confirm scope and that exactly 6 panels are provided before any processing. Do not generate any output before this check.
- Confirm the user objective, required inputs, and non-negotiable constraints.
- Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
- Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
- 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.pyfails (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 numpyand 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:
- Objective
- Inputs Received
- Assumptions
- Workflow
- Deliverable
- Risks and Limits
- 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
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.
