AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Generate .excalidraw diagrams from natural language. Use when creating flowcharts, mind maps, system architecture diagrams, sequence diagrams, ER diagrams, network diagrams, or any visual process visualization.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
A skill for generating Excalidraw-format diagrams from natural language descriptions. This skill helps create visual representations of processes, systems, relationships, and ideas without manual drawing.
Use symptom -> action triggers: when one matches, apply this skill and verify with the protocol below.
Use this skill when users request:
Supported diagram types:
Analyze the user's description to determine:
| User Intent | Diagram Type | Example Keywords |
|---|---|---|
| Process flow, steps, procedures | Flowchart | "workflow", "process", "steps", "procedure" |
| Connections, dependencies, associations | Relationship Diagram | "relationship", "connections", "dependencies", "structure" |
| Concept hierarchy, brainstorming | Mind Map | "mind map", "concepts", "ideas", "breakdown" |
| System design, components | Architecture Diagram | "architecture", "system", "components", "modules" |
| Data flow, transformation processes | Data Flow Diagram (DFD) | "data flow", "data processing", "data transformation" |
| Cross-functional processes, actor responsibilities | Business Flow (Swimlane) | "business process", "swimlane", "actors", "responsibilities" |
| Object-oriented design, class structures | Class Diagram | "class", "inheritance", "OOP", "object model" |
| Interaction sequences, message flows | Sequence Diagram | "sequence", "interaction", "messages", "timeline" |
| Database design, entity relationships | ER Diagram | "database", "entity", "relationship", "data model" |
For Flowcharts:
For Relationship Diagrams:
For Mind Maps:
For Data Flow Diagrams (DFD):
For Business Flow (Swimlane):
For Class Diagrams:
For Sequence Diagrams:
For ER Diagrams:
Create the .excalidraw file with appropriate elements:
Available element types:
rectangle: Boxes for entities, steps, conceptsellipse: Alternative shapes for emphasisdiamond: Decision pointsarrow: Directional connectionstext: Labels and annotationsKey properties to set:
x, y coordinateswidth, heightstrokeColor, backgroundColor, fillStylefontFamily: 5 (Excalifont - required for all text elements)points array for arrowsImportant: All text elements must use fontFamily: 5 (Excalifont) for consistent visual appearance.
Structure the complete Excalidraw file:
{
"type": "excalidraw",
"version": 2,
"source": "https://excalidraw.com",
"elements": [
// Array of diagram elements
],
"appState": {
"viewBackgroundColor": "#ffffff",
"gridSize": 20
},
"files": {}
}
<descriptive-name>.excalidraw| Diagram Type | Recommended Count | Maximum |
|---|---|---|
| Flowchart steps | 3-10 | 15 |
| Relationship entities | 3-8 | 12 |
| Mind map branches | 4-6 | 8 |
| Mind map sub-topics per branch | 2-4 | 6 |
#a5d8ff)#b2f2bb)#ffd43b)#ffc9c9)fontFamily: 5 (Excalifont) for all text elementsIf user request has too many elements:
Example response:
"Your request includes 15 components. For clarity, I recommend:
1. High-level architecture diagram (6 main components)
2. Detailed diagram for each subsystem
Would you like me to start with the high-level view?"
Before claiming "skill applied successfully":
User: "Create a flowchart for user registration"
Agent generates:
user-registration-flow.excalidrawUser: "Diagram the relationship between User, Post, and Comment entities"
Agent generates:
user-content-relationships.excalidrawUser: "Mind map about machine learning concepts"
Agent generates:
machine-learning-mindmap.excalidraw| Issue | Solution |
|---|---|
| Elements overlap | Increase spacing between coordinates |
| Text doesn't fit in boxes | Increase box width or reduce font size |
| Too many elements | Break into multiple diagrams |
| Unclear layout | Use grid layout (rows/columns) or radial layout (mind maps) |
| Colors inconsistent | Define color palette upfront based on element types |
const columns = Math.ceil(Math.sqrt(entityCount));
const x = startX + (index % columns) * horizontalGap;
const y = startY + Math.floor(index / columns) * verticalGap;
const angle = (2 * Math.PI * index) / branchCount;
const x = centerX + radius * Math.cos(angle);
const y = centerY + radius * Math.sin(angle);
Use timestamp + random string for unique IDs:
const id = Date.now().toString(36) + Math.random().toString(36).substr(2);
Always provide:
.excalidraw JSON fileExample summary:
Created: user-workflow.excalidraw
Type: Flowchart
Elements: 7 rectangles, 6 arrows, 1 title text
Total: 14 elements
To view:
1. Visit https://excalidraw.com
2. Drag and drop user-workflow.excalidraw
3. Or use File → Open in Excalidraw VS Code extension
Before delivering the diagram:
fontFamily: 5 (Excalifont)For specialized diagrams (e.g., AWS/GCP/Azure architecture diagrams), you can use pre-made icon libraries from Excalidraw. This provides professional, standardized icons instead of basic shapes.
If user asks for AWS/cloud architecture diagrams or mentions wanting to use specific icons:
Check if library exists: Look for libraries/<library-name>/reference.md
If library exists: Proceed to use icons (see AI Assistant Workflow below)
If library does NOT exist: Respond with setup instructions:
To use [AWS/GCP/Azure/etc.] architecture icons, please follow these steps:
1. Visit https://libraries.excalidraw.com/
2. Search for "[AWS Architecture Icons/etc.]" and download the .excalidrawlib file
3. Create directory: skills/excalidraw-diagram-generator/libraries/[icon-set-name]/
4. Place the downloaded file in that directory
5. Run the splitter script:
python skills/excalidraw-diagram-generator/scripts/split-excalidraw-library.py skills/excalidraw-diagram-generator/libraries/[icon-set-name]/
This will split the library into individual icon files for efficient use.
After setup is complete, I can create your diagram using the actual AWS/cloud icons.
Alternatively, I can create the diagram now using simple shapes (rectangles, ellipses)
which you can later replace with icons manually in Excalidraw.
Step 1: Create Library Directory
mkdir -p skills/excalidraw-diagram-generator/libraries/aws-architecture-icons
Step 2: Download Library
.excalidrawlib fileStep 3: Place Library File
aws-architecture-icons.excalidrawlib)Step 4: Run Splitter Script
python skills/excalidraw-diagram-generator/scripts/split-excalidraw-library.py skills/excalidraw-diagram-generator/libraries/aws-architecture-icons/
Step 5: Verify Setup After running the script, verify the following structure exists:
skills/excalidraw-diagram-generator/libraries/aws-architecture-icons/
aws-architecture-icons.excalidrawlib (original)
reference.md (generated - icon lookup table)
icons/ (generated - individual icon files)
API-Gateway.json
CloudFront.json
EC2.json
Lambda.json
RDS.json
S3.json
...
When icon libraries are available in libraries/:
RECOMMENDED APPROACH: Use Python Scripts (Efficient & Reliable)
The repository includes Python scripts that handle icon integration automatically:
Create base diagram structure:
.excalidraw file with basic layout (title, boxes, regions)Add icons using Python script:
python skills/excalidraw-diagram-generator/scripts/add-icon-to-diagram.py \
<diagram-path> <icon-name> <x> <y> [--label "Text"] [--library-path PATH]
.excalidraw.edit is enabled by default to avoid overwrite issues; pass --no-use-edit-suffix to disable.Examples:
# Add EC2 icon at position (400, 300) with label
python scripts/add-icon-to-diagram.py diagram.excalidraw EC2 400 300 --label "Web Server"
# Add VPC icon at position (200, 150)
python scripts/add-icon-to-diagram.py diagram.excalidraw VPC 200 150
# Add icon from different library
python scripts/add-icon-to-diagram.py diagram.excalidraw Compute-Engine 500 200 \
--library-path libraries/gcp-icons --label "API Server"
Add connecting arrows:
python skills/excalidraw-diagram-generator/scripts/add-arrow.py \
<diagram-path> <from-x> <from-y> <to-x> <to-y> [--label "Text"] [--style solid|dashed|dotted] [--color HEX]
.excalidraw.edit is enabled by default to avoid overwrite issues; pass --no-use-edit-suffix to disable.Examples:
# Simple arrow from (300, 250) to (500, 300)
python scripts/add-arrow.py diagram.excalidraw 300 250 500 300
# Arrow with label
python scripts/add-arrow.py diagram.excalidraw 300 250 500 300 --label "HTTPS"
# Dashed arrow with custom color
python scripts/add-arrow.py diagram.excalidraw 400 350 600 400 --style dashed --color "#7950f2"
Workflow summary:
# Step 1: Create base diagram with title and structure
# (Create .excalidraw file with initial elements)
# Step 2: Add icons with labels
python scripts/add-icon-to-diagram.py my-diagram.excalidraw "Internet-gateway" 200 150 --label "Internet Gateway"
python scripts/add-icon-to-diagram.py my-diagram.excalidraw VPC 250 250
python scripts/add-icon-to-diagram.py my-diagram.excalidraw ELB 350 300 --label "Load Balancer"
python scripts/add-icon-to-diagram.py my-diagram.excalidraw EC2 450 350 --label "EC2 Instance"
python scripts/add-icon-to-diagram.py my-diagram.excalidraw RDS 550 400 --label "Database"
# Step 3: Add connecting arrows
python scripts/add-arrow.py my-diagram.excalidraw 250 200 300 250 # Internet → VPC
python scripts/add-arrow.py my-diagram.excalidraw 300 300 400 300 # VPC → ELB
python scripts/add-arrow.py my-diagram.excalidraw 400 330 500 350 # ELB → EC2
python scripts/add-arrow.py my-diagram.excalidraw 500 380 600 400 # EC2 → RDS
Benefits of Python Script Approach:
ALTERNATIVE: Manual Icon Integration (Not Recommended)
Only use this if Python scripts are unavailable:
Check for libraries:
List directory: skills/excalidraw-diagram-generator/libraries/
Look for subdirectories containing reference.md files
Read reference.md:
Open: libraries/<library-name>/reference.md
This is lightweight (typically <300 lines) and lists all available icons
Find relevant icons:
Search the reference.md table for icon names matching diagram needs
Example: For AWS diagram with EC2, S3, Lambda → Find "EC2", "S3", "Lambda" in table
Load specific icon data (WARNING: Large files):
Read ONLY the needed icon files:
- libraries/aws-architecture-icons/icons/EC2.json (200-300 lines)
- libraries/aws-architecture-icons/icons/S3.json (200-300 lines)
- libraries/aws-architecture-icons/icons/Lambda.json (200-300 lines)
Note: Each icon file is 200-1000 lines - this consumes significant tokens
Extract and transform elements:
Each icon JSON contains an "elements" array
Calculate bounding box (min_x, min_y, max_x, max_y)
Apply offset to all x/y coordinates
Generate new unique IDs for all elements
Update groupIds references
Copy transformed elements into your diagram
Position icons and add connections:
Adjust x/y coordinates to position icons correctly in the diagram
Update IDs to ensure uniqueness across diagram
Add connecting arrows and labels as needed
Manual Integration Challenges:
Request: "Create an AWS architecture diagram with Internet Gateway, VPC, ELB, EC2, and RDS"
Recommended Workflow (using Python scripts): Request: "Create an AWS architecture diagram with Internet Gateway, VPC, ELB, EC2, and RDS"
Recommended Workflow (using Python scripts):
# Step 1: Create base diagram file with title
# Create my-aws-diagram.excalidraw with basic structure (title, etc.)
# Step 2: Check icon availability
# Read: libraries/aws-architecture-icons/reference.md
# Confirm icons exist: Internet-gateway, VPC, ELB, EC2, RDS
# Step 3: Add icons with Python script
python scripts/add-icon-to-diagram.py my-aws-diagram.excalidraw "Internet-gateway" 150 100 --label "Internet Gateway"
python scripts/add-icon-to-diagram.py my-aws-diagram.excalidraw VPC 200 200
python scripts/add-icon-to-diagram.py my-aws-diagram.excalidraw ELB 350 250 --label "Load Balancer"
python scripts/add-icon-to-diagram.py my-aws-diagram.excalidraw EC2 500 300 --label "Web Server"
python scripts/add-icon-to-diagram.py my-aws-diagram.excalidraw RDS 650 350 --label "Database"
# Step 4: Add connecting arrows
python scripts/add-arrow.py my-aws-diagram.excalidraw 200 150 250 200 # Internet → VPC
python scripts/add-arrow.py my-aws-diagram.excalidraw 265 230 350 250 # VPC → ELB
python scripts/add-arrow.py my-aws-diagram.excalidraw 415 280 500 300 # ELB → EC2
python scripts/add-arrow.py my-aws-diagram.excalidraw 565 330 650 350 --label "SQL" --style dashed
# Result: Complete diagram with professional AWS icons, labels, and connections
Benefits:
Alternative Workflow (manual, if scripts unavailable):
libraries/aws-architecture-icons/reference.md exists → Yesicons/Internet-gateway.json (298 lines)icons/VPC.json (550 lines)icons/ELB.json (363 lines)icons/EC2.json (231 lines)icons/RDS.json (similar size)
Total: ~2000+ lines of JSON to process.excalidraw fileChallenges with manual approach:
.excalidrawlib file you provide.If no icon libraries are set up:
See bundled references for:
references/excalidraw-schema.md - Complete Excalidraw JSON schemareferences/element-types.md - Detailed element type specificationstemplates/flowchart-template.json - Basic flowchart startertemplates/relationship-template.json - Relationship diagram startertemplates/mindmap-template.json - Mind map starterscripts/split-excalidraw-library.py - Tool to split .excalidrawlib filesscripts/README.md - Documentation for library toolsscripts/.gitignore - Prevents local Python artifacts from being committedPotential improvements:
This skill is written to stay usable across GitHub Copilot, Claude Code, and Codex.
$CODEX_HOME/skills/excalidraw-diagram-generator and restart Codex after major changes.Preferred MCP Server: None required
SKILL.md, bundled references or scripts, and manual verification. Show the exact commands, evidence, and final checks you used before concluding."まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
AI-powered adeno-associated virus (AAV) vector design for gene therapy including capsid engineering, promoter selection, and tropism optimization.
日本語の概要は準備中です。原文の説明を表示しています。
Improve the clarity and voice of AI-assisted academic writing (papers, theses, rebuttals) and
日本語の概要は準備中です。原文の説明を表示しています。
12-agent academic paper writing pipeline. 11 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure/rebuttal-audit). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, audit my rebuttal, check my response draft, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見, 評估回覆, 논문 작성, 초록 작성, 논문 수정, 논문 계획을 도와줘, 심사 의견 반영, 답변서 점검, AI 사용 고지.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic writing framework for philosophy and interdisciplinary academic papers from optimized outline to submission-ready manuscript. Use when users want to: (1) write a paper from a detailed outline, (2) ensure quality control during writing, (3) maintain consistency across chapters, (4) prepare a submission-ready manuscript, or (5) systematically execute a planned paper. Triggered by phrases like 'write the paper from this outline,' 'compose the full manuscript,' 'execute the outline,' or when users have completed strategic planning (academic-paper-strategist skill) and are ready to write. Takes optimized outline as input; outputs complete manuscript with iterative quality checks.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-perspective academic paper review with dynamic reviewer personas. Runs a 5-seat, role-separated review panel (Journal-Fit Reviewer + 3 peer-review roles + Devil's Advocate) with field-specific expertise; role separation is not a claim of independent error processes. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy, 審查論文, 論文審查, 模擬審查, 同儕審查, 幫我審這篇, 以審查人角度評估, 審查者校準, 논문 심사, 동료 심사, 모의 심사, 심사자 관점에서 평가, 심사자 보정.
日本語の概要は準備中です。原文の説明を表示しています。
Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints.
日本語の概要は準備中です。原文の説明を表示しています。