Visual Concept Explainer

You are orchestrating a visual concept explanation workflow that transforms text or documents into AI-generated infographic pages. The tool uses Gemini Pro 3 (via google-genai SDK) for 4K image generation and Claude Sonnet Vision for quality evaluation with iterative refinement.

Proactive Triggers

Suggest this skill when:

User has a document, report, or concept they want visualized as infographic pages
After generating a report or analysis that would benefit from a visual summary
User mentions creating infographics, visual explanations, or concept diagrams
User asks to make a document more visually appealing or presentation-ready
User wants to transform a whitepaper, guide, or technical document into visual content

Infographic Mode (Recommended)

The --infographic flag enables information-dense infographic generation optimized for 11x17 inch printing at 4K resolution. This mode:

Adaptive page count (1-6 pages) based on document complexity, word count, and content types
Zone-based layouts with explicit text placement, typography specifications, and content zones
8 page types: Hero Summary, Problem Landscape, Framework Overview, Framework Deep-Dive, Comparison Matrix, Dimensions/Variations, Reference/Action, Data/Evidence
Information density: Each page can hold 800-2000 words of readable text plus diagrams, tables, and charts

Page Type Selection

The system automatically selects appropriate page types based on content:

Content Pattern	Page Type	Purpose
Executive summary needed	Hero Summary	One-page overview with key stats
Challenges, pain points	Problem Landscape	Issues visualization with severity
Multi-step processes	Framework Overview	Visual framework with connections
Deep component analysis	Framework Deep-Dive	Detailed component exploration
Multiple options to compare	Comparison Matrix	Side-by-side analysis table
Variations, types, categories	Dimensions/Variations	Category breakdown visualization
Statistics, research data	Data/Evidence	Charts and data visualization
Action items, checklists	Reference/Action	Actionable takeaways and guides

Technical Notes

Image Generation:

Uses google-genai SDK with model from $GOOGLE_IMAGE_MODEL env var, falling back to gemini-3-pro-image-preview (default as of 2026-03-04 — verify with provider if errors occur)
Configuration: response_modalities=["IMAGE"] with ImageConfig for aspect ratio/size
4K images are approximately 6-7.5MB each (JPEG format)

Image Evaluation:

Claude Vision API has 5MB limit for base64-encoded images
Tool automatically resizes images >3.5MB before evaluation (accounts for base64 overhead)
Uses PIL/Pillow for high-quality LANCZOS resampling

Platform Compatibility:

Windows: Folder names are sanitized to remove invalid characters (:, *, ?, ", <, >, |)
All platforms: Full Unicode support in Rich terminal UI

Tested Results (4 documents, 17 images):

Formats tested: URL (Substack), Markdown, DOCX
Average scores: 0.76-0.88 (all passing with threshold 0.75)
Generation time: 5-10 minutes per document (~2 min/image including analysis)
Only 1 retry needed across 17 images (image scored 0.72 → refined to 0.82)
Recommended pass threshold: 0.75-0.85 for good quality without excessive refinement

Input Validation

Required Arguments:

Input content (one of the following):
- Raw text (pasted directly)
- Document path (.md, .txt, .docx, .pdf)
- URL (to fetch and extract content)

Optional Arguments:

Parameter	Default	Options	Description
`--infographic`	false	flag	Recommended. Generate information-dense infographic pages (11x17 format)
`--max-iterations`	5	1-10	Max refinement attempts per image
`--aspect-ratio`	16:9	16:9, 1:1, 4:3, 9:16, 3:4	Image aspect ratio
`--resolution`	high	low, medium, high	Image quality (high=4K/3200x1800)
`--style`	professional-clean	professional-clean, professional-sketch, or path	Visual style
`--output-dir`	./output	path	Output directory
`--pass-threshold`	0.85	0.0-1.0	Score required to pass evaluation
`--concurrency`	3	1-10	Max concurrent image generations
`--no-cache`	false	flag	Force fresh concept analysis
`--resume`	null	path	Resume from checkpoint file
`--dry-run`	false	flag	Show plan without generating
`--setup-keys`	false	flag	Force re-check of API key availability (use `/unlock` to load keys)
`--json`	false	flag	Output results as JSON (for programmatic use). Returns structured metadata including image paths, scores, concept mappings, and generation statistics. Useful for downstream automation or integration with other tools.

Input Format Handling:

Format	Handling
`.md`, `.txt`	Direct text extraction
`.docx`	Requires `python-docx` - extracts paragraphs preserving headings
`.pdf`	Requires `PyPDF2` - extracts text content
URL	Requires `beautifulsoup4` - fetches and extracts main content
Web content	Best practice: Save as markdown first for reproducibility and future reference

DOCX Conversion Tip: For best results with DOCX files, pre-convert to markdown:

from docx import Document
doc = Document('document.docx')
with open('document.md', 'w') as f:
    for para in doc.paragraphs:
        style = para.style.name if para.style else ''
        if style.startswith('Heading'):
            level = int(style[-1]) if style[-1].isdigit() else 1
            f.write('#' * level + ' ' + para.text + '\n\n')
        else:
            f.write(para.text + '\n\n')

Environment Requirements (Secrets Policy): API keys must be loaded into the environment before use. The primary method is the /unlock skill, which loads secrets from Bitwarden Secrets Manager via the bws CLI (see CLAUDE.md Secrets Management Policy):

GOOGLE_API_KEY - For Gemini Pro 3 image generation
ANTHROPIC_API_KEY - For Claude concept analysis and image evaluation

Optional Model Configuration (non-sensitive, safe for .env):

GOOGLE_IMAGE_MODEL - Override Gemini image model (default: gemini-3-pro-image-preview, as of 2026-03-04 — verify with provider if errors occur)

If keys are not in the environment, suggest running /unlock before proceeding. Secrets policy compliance:

Do NOT write API keys to .env files or any configuration files
Do NOT guide users through creating .env files with API key values
Do NOT hardcode API keys in commands or scripts
Always direct users to /unlock or the Bitwarden Secrets Manager workflow

Tool vs Claude Responsibilities

Understanding what the Python tool handles vs what you (Claude) must do:

Component	Responsibility	What It Does
visual-explainer (Python tool)	Core pipeline	Concept analysis, prompt generation, Gemini API calls, evaluation, refinement loop, output organization
You (Claude)	Input collection	Gather input text/path/URL from user
You (Claude)	Interactive confirmation	Style selection, image count confirmation
You (Claude)	Progress display	Show generation progress to user
You (Claude)	Results presentation	Display completion summary

Workflow

Phase 1: Setup and Dependency Check

The tool is bundled at ../tools/visual-explainer/ relative to this skill file.

Step 1: Set Up Tool Path

# Determine the plugin directory
PLUGIN_DIR="${CLAUDE_PLUGIN_ROOT:-/path/to/plugins/personal-plugin}"
TOOL_SRC="$PLUGIN_DIR/tools/visual-explainer/src"

Step 2: Check Dependencies

The tool automatically checks dependencies when run. You can also do a dry-run to verify setup:

PYTHONPATH="$TOOL_SRC" python -m visual_explainer --dry-run --input "test content"

Required packages:

Core: google-genai, anthropic, httpx, python-dotenv, pydantic, aiofiles, rich, pillow
Optional (format-specific): python-docx (DOCX), PyPDF2 (PDF), beautifulsoup4 (URLs)

If packages are missing, install them:

pip install google-genai anthropic httpx python-dotenv pydantic aiofiles rich pillow
pip install python-docx PyPDF2 beautifulsoup4  # Optional, for specific formats

Phase 2: API Key Setup (if needed)

If API keys are missing:

API Key Setup Required
======================

This tool requires two API keys:
- GOOGLE_API_KEY - for Gemini Pro 3 image generation
- ANTHROPIC_API_KEY - for Claude concept analysis and image evaluation

Missing keys detected:
  - GOOGLE_API_KEY - not found
  - ANTHROPIC_API_KEY - not found

To load API keys from Bitwarden, run: /unlock
This loads secrets from Bitwarden Secrets Manager into the current environment.

See CLAUDE.md Secrets Management Policy for details on storing and retrieving secrets.

If keys are still missing after /unlock, ask the user to verify the secrets are stored in their Bitwarden vault. Do NOT offer to write keys to .env files or guide users through creating .env files with API keys.

Phase 3: Input Collection

If no input was provided in arguments, prompt:

I'll help you create visual explanations for your content.

Please provide your input in one of these formats:
1. Paste text directly
2. Provide a file path (e.g., ./docs/concept.md)
3. Provide a URL to fetch content from

Phase 4: Content Analysis

After receiving input, run concept analysis:

PYTHONPATH="$TOOL_SRC" python -m visual_explainer analyze \
  --input "<input_text_or_path>" \
  --output-json

Display the analysis summary:

Content Analysis
================
Document: "Understanding Quantum Entanglement"
Word Count: 1,847 words
Key Concepts: 5 concepts identified
Recommended Images: 3 images

Concept Flow:
1. Classical Physics Background
   -> 2. Quantum Superposition
   -> 3. Entanglement Phenomenon
   -> 4. Applications
   -> 5. Future Implications

Phase 5: Style Selection (Interactive)

Prompt for style selection:

Visual Style Selection
======================
What style would you prefer?

1. Professional Clean (Recommended)
   - Clean, corporate-ready with warm accents
   - Best for: Business, presentations, reports

2. Professional Sketch
   - Hand-drawn sketch aesthetic
   - Best for: Creative, educational, informal

3. Custom
   - Provide path to your own style JSON

4. Skip (use Professional Clean default)

Select style [1-4]:

Phase 6: Image Count Confirmation

Image Generation Plan
=====================
Based on analysis, I recommend 3 images:

Image 1: "The Classical Foundation"
  - Covers: Classical Physics Background
  - Intent: Establish baseline understanding

Image 2: "Quantum Superposition"
  - Covers: Superposition, probability states
  - Intent: Introduce quantum concepts

Image 3: "Entanglement Synthesis"
  - Covers: Entanglement, applications, future
  - Intent: Tie concepts together

Would you like to:
1. Proceed with 3 images (Recommended)
2. Use fewer images (condense concepts)
3. Use more images (expand detail)
4. Adjust settings (aspect ratio, iterations)

Phase 7: Generation Execution

Execute the full generation pipeline:

PYTHONPATH="$TOOL_SRC" python -m visual_explainer generate \
  --input "<input_text_or_path>" \
  --style "<selected_style>" \
  --max-iterations <n> \
  --aspect-ratio "<ratio>" \
  --resolution "<level>" \
  --output-dir "<output_path>" \
  --pass-threshold <threshold> \
  --concurrency <n>

Progress Display Format:

Starting Image Generation
=========================

Image 1 of 3: "The Classical Foundation"
----------------------------------------

Attempt 1/5:
  [=========>         ] Generating... (4.2s)
  Generated
  Evaluating...
    - Concept clarity: 72%
    - Visual appeal: 85%
    - Flow continuity: 60%
  Overall: 72% - NEEDS_REFINEMENT

Attempt 2/5:
  Refining: Adding visual flow indicators
  [=========>         ] Generating... (3.8s)
  Generated
  Evaluating...
    - Concept clarity: 91%
    - Visual appeal: 88%
    - Flow continuity: 85%
  Overall: 88% - PASS

Image 1 complete. Best version: Attempt 2

Image 2 of 3: "Quantum Superposition"
-------------------------------------
...

Phase 8: Completion Summary

Generation Complete
===================

Results:
  - Images generated: 3 of 3
  - Total attempts: 7
  - Average quality score: 89%
  - Estimated cost: $0.70

Output saved to:
  ./output/visual-explainer-quantum-entanglement-20260118-143052/

Final Images:
  1. 01-classical-foundation.jpg (Score: 88%)
  2. 02-quantum-superposition.jpg (Score: 91%)
  3. 03-entanglement-synthesis.jpg (Score: 88%)

Output Structure:
  metadata.json          # Full generation metadata
  concepts.json          # Extracted concepts
  summary.md             # Human-readable summary
  all-images/            # Final images only
    01-classical-foundation.jpg
    02-quantum-superposition.jpg
    03-entanglement-synthesis.jpg
  image-01/              # All attempts for image 1
    final.jpg
    prompt-v1.txt
    attempt-01.jpg
    evaluation-01.json
    ...

Would you like to:
1. View the summary report
2. Regenerate a specific image
3. Open output folder

Resume from Checkpoint

If generation was interrupted, resume with:

PYTHONPATH="$TOOL_SRC" python -m visual_explainer generate \
  --resume "./output/visual-explainer-[topic]-[timestamp]/checkpoint.json"

The checkpoint contains:

Generation state
Completed images
Current progress
Configuration used

Cost Estimation

Estimated costs per session:

Scenario	Images	Avg Attempts	Est. Total
Simple doc, 1 image	1	2	~$0.28
Medium doc, 3 images	3	2.3	~$0.95
Complex doc, 5 images	5	3	~$2.10

Component costs:

Gemini image generation: ~$0.10 per image
Claude concept analysis: ~$0.02 per document
Claude image evaluation: ~$0.03 per evaluation

Performance

Scenario	Images	Expected Duration	Estimated API Cost
Simple document, 1 image	1	2-4 minutes	~$0.28
Medium document, 3 images	3	5-10 minutes	~$0.95
Complex document, 5 images	5	12-20 minutes	~$2.10
Infographic mode, 3 pages	3	8-15 minutes	~$1.20
Infographic mode, 6 pages	6	15-30 minutes	~$2.80

Duration scales linearly with image count. Each image takes approximately 2 minutes including Gemini generation, Claude evaluation, and potential refinement. Refinement retries (when score < pass threshold) add ~1.5 minutes per retry.

API cost breakdown per image: Gemini Pro 3 generation ~$0.10, Claude concept analysis ~$0.02/document (one-time), Claude image evaluation ~$0.03/evaluation. Costs increase with refinement retries. At default settings (max 5 iterations, 0.85 threshold), typical cost is $0.15-0.25 per final image. Use --dry-run to preview the generation plan without incurring any API costs.

Error Handling

Error	Response
Missing API key	Suggest running `/unlock` to load keys from Bitwarden
Rate limit (429)	Exponential backoff, respect Retry-After header
Safety filter	Log, skip to next attempt with modified prompt
Timeout	Retry with increased timeout (up to 5 min)
All attempts exhausted	Select best attempt, report scores
Network error	Retry up to 3 times with backoff

Examples

Infographic mode (recommended for complex documents):

/visual-explainer --input docs/architecture-overview.md --infographic

Infographic with dry-run preview:

/visual-explainer --input whitepaper.md --infographic --dry-run

Basic usage (interactive):

/visual-explainer

With document path:

/visual-explainer --input docs/architecture-overview.md

Custom settings:

/visual-explainer --input concept.txt --style professional-sketch --max-iterations 3 --aspect-ratio 1:1

High quality infographic:

/visual-explainer --input whitepaper.md --infographic --resolution high --max-iterations 7 --pass-threshold 0.90

Dry run (plan only):

/visual-explainer --input document.md --dry-run

Resume interrupted generation:

/visual-explainer --resume ./output/visual-explainer-topic-20260118/checkpoint.json

Execution Summary

Follow these steps in order:

Setup - Parse arguments, set up tool path
Dependency Check - Verify packages and API keys
API Key Setup - If missing, guide user through setup wizard
Input Collection - Get text/path/URL from user (if not provided)
Content Analysis - Extract concepts, determine image count
Style Selection - Let user choose or use default
Image Count Confirmation - Confirm plan with user
Generation Execution - Run full pipeline with progress display
Completion Summary - Display results and output locations