mirror of
https://github.com/K-Dense-AI/claude-scientific-skills.git
synced 2026-01-26 16:58:56 +08:00
Enhance citation management and literature review guidelines
- Updated SKILL.md in citation management to include best practices for identifying seminal and high-impact papers, emphasizing citation count thresholds, venue quality tiers, and author reputation indicators. - Expanded literature review SKILL.md to prioritize high-impact papers, detailing citation metrics, journal tiers, and author reputation assessment. - Added comprehensive evaluation strategies for paper impact and quality in literature_search_strategies.md, including citation count significance and journal impact factor guidance. - Improved research lookup scripts to prioritize results based on citation count, venue prestige, and author reputation, enhancing the quality of research outputs.
This commit is contained in:
Vinayak Agarwal
@@ -0,0 +1,207 @@
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# Scientific Schematics - Quick Reference
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**How it works:** Describe your diagram → Nano Banana Pro generates it automatically
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## Setup (One-Time)
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```bash
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# Get API key from https://openrouter.ai/keys
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export OPENROUTER_API_KEY='sk-or-v1-your_key_here'
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# Add to shell profile for persistence
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echo 'export OPENROUTER_API_KEY="sk-or-v1-your_key"' >> ~/.bashrc # or ~/.zshrc
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```
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## Basic Usage
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```bash
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# Describe your diagram, Nano Banana Pro creates it
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python scripts/generate_schematic.py "your diagram description" -o output.png
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# That's it! Automatic:
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# - Iterative refinement (3 rounds)
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# - Quality review and improvement
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# - Publication-ready output
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```
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## Common Examples
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### CONSORT Flowchart
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```bash
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python scripts/generate_schematic.py \
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"CONSORT flow: screened n=500, excluded n=150, randomized n=350" \
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-o consort.png
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```
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### Neural Network
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```bash
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python scripts/generate_schematic.py \
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"Transformer architecture with encoder and decoder stacks" \
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-o transformer.png
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```
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### Biological Pathway
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```bash
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python scripts/generate_schematic.py \
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"MAPK pathway: EGFR → RAS → RAF → MEK → ERK" \
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-o mapk.png
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```
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### Circuit Diagram
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```bash
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python scripts/generate_schematic.py \
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"Op-amp circuit with 1kΩ resistor and 10µF capacitor" \
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-o circuit.png
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```
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## Command Options
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| Option | Description | Example |
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|--------|-------------|---------|
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| `-o, --output` | Output file path | `-o figures/diagram.png` |
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| `--iterations N` | Number of refinements (1-2) | `--iterations 2` |
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| `-v, --verbose` | Show detailed output | `-v` |
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| `--api-key KEY` | Provide API key | `--api-key sk-or-v1-...` |
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## Prompt Tips
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### ✓ Good Prompts (Specific)
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- "CONSORT flowchart with screening (n=500), exclusion (n=150), randomization (n=350)"
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- "Transformer architecture: encoder on left with 6 layers, decoder on right, cross-attention connections"
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- "MAPK signaling: receptor → RAS → RAF → MEK → ERK → nucleus, label each phosphorylation"
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### ✗ Avoid (Too Vague)
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- "Make a flowchart"
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- "Neural network"
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- "Pathway diagram"
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## Output Files
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For input `diagram.png`, you get:
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- `diagram_v1.png` - First iteration
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- `diagram_v2.png` - Second iteration
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- `diagram_v3.png` - Final iteration
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- `diagram.png` - Copy of final
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- `diagram_review_log.json` - Quality scores and critiques
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## Review Log
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```json
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{
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"iterations": [
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{
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"iteration": 1,
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"score": 7.0,
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"critique": "Good start. Font too small..."
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},
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{
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"iteration": 2,
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"score": 8.5,
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"critique": "Much improved. Minor spacing issues..."
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},
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{
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"iteration": 3,
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"score": 9.5,
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"critique": "Excellent. Publication ready."
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}
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],
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"final_score": 9.5
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}
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```
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## Python API
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```python
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from scripts.generate_schematic_ai import ScientificSchematicGenerator
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# Initialize
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gen = ScientificSchematicGenerator(api_key="your_key")
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# Generate
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results = gen.generate_iterative(
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user_prompt="diagram description",
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output_path="output.png",
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iterations=2
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)
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# Check quality
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print(f"Score: {results['final_score']}/10")
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```
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## Troubleshooting
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### API Key Not Found
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```bash
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# Check if set
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echo $OPENROUTER_API_KEY
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# Set it
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export OPENROUTER_API_KEY='your_key'
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```
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### Import Error
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```bash
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# Install requests
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pip install requests
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```
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### Low Quality Score
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- Make prompt more specific
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- Include layout details (left-to-right, top-to-bottom)
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- Specify label requirements
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- Increase iterations: `--iterations 2`
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## Testing
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```bash
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# Verify installation
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python test_ai_generation.py
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# Should show: "6/6 tests passed"
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```
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## Cost
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Typical cost per diagram (max 2 iterations):
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- Simple (1 iteration): $0.05-0.15
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- Complex (2 iterations): $0.10-0.30
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## How Nano Banana Pro Works
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**Simply describe your diagram in natural language:**
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- ✓ No coding required
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- ✓ No templates needed
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- ✓ No manual drawing
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- ✓ Automatic quality review
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- ✓ Publication-ready output
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- ✓ Works for any diagram type
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**Just describe what you want, and it's generated automatically.**
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## Getting Help
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```bash
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# Show help
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python scripts/generate_schematic.py --help
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# Verbose mode for debugging
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python scripts/generate_schematic.py "diagram" -o out.png -v
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```
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## Quick Start Checklist
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- [ ] Set `OPENROUTER_API_KEY` environment variable
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- [ ] Run `python test_ai_generation.py` (should pass 6/6)
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- [ ] Try: `python scripts/generate_schematic.py "test diagram" -o test.png`
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- [ ] Review output files (test_v1.png, v2, v3, review_log.json)
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- [ ] Read SKILL.md for detailed documentation
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- [ ] Check README.md for examples
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## Resources
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- Full documentation: `SKILL.md`
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- Detailed guide: `README.md`
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- Implementation details: `IMPLEMENTATION_SUMMARY.md`
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- Example script: `example_usage.sh`
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- Get API key: https://openrouter.ai/keys
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327
scientific-skills/scientific-schematics/references/README.md
Normal file
327
scientific-skills/scientific-schematics/references/README.md
Normal file
@@ -0,0 +1,327 @@
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# Scientific Schematics - Nano Banana Pro
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**Generate any scientific diagram by describing it in natural language.**
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Nano Banana Pro creates publication-quality diagrams automatically - no coding, no templates, no manual drawing required.
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## Quick Start
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### Generate Any Diagram
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```bash
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# Set your OpenRouter API key
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export OPENROUTER_API_KEY='your_api_key_here'
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# Generate any scientific diagram
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python scripts/generate_schematic.py "CONSORT participant flow diagram" -o figures/consort.png
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# Neural network architecture
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python scripts/generate_schematic.py "Transformer encoder-decoder architecture" -o figures/transformer.png
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# Biological pathway
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python scripts/generate_schematic.py "MAPK signaling pathway" -o figures/pathway.png
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```
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### What You Get
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- **Up to two iterations** (v1, v2) with progressive refinement
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- **Automatic quality review** after each iteration
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- **Detailed review log** with scores and critiques (JSON format)
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- **Publication-ready images** following scientific standards
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## Features
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### Iterative Refinement Process
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1. **Generation 1**: Create initial diagram from your description
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2. **Review 1**: AI evaluates clarity, labels, accuracy, accessibility
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3. **Generation 2**: Improve based on critique
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4. **Review 2**: Second evaluation with specific feedback
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5. **Generation 3**: Final polished version
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### Automatic Quality Standards
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All diagrams automatically follow:
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- Clean white/light background
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- High contrast for readability
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- Clear labels (minimum 10pt font)
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- Professional typography
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- Colorblind-friendly colors
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- Proper spacing between elements
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- Scale bars, legends, axes where appropriate
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## Installation
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### For AI Generation
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```bash
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# Get OpenRouter API key
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# Visit: https://openrouter.ai/keys
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# Set environment variable
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export OPENROUTER_API_KEY='sk-or-v1-...'
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# Or add to .env file
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echo "OPENROUTER_API_KEY=sk-or-v1-..." >> .env
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# Install Python dependencies (if not already installed)
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pip install requests
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```
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## Usage Examples
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### Example 1: CONSORT Flowchart
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```bash
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python scripts/generate_schematic.py \
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"CONSORT participant flow diagram for RCT. \
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Assessed for eligibility (n=500). \
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Excluded (n=150): age<18 (n=80), declined (n=50), other (n=20). \
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Randomized (n=350) into Treatment (n=175) and Control (n=175). \
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Lost to follow-up: 15 and 10 respectively. \
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Final analysis: 160 and 165." \
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-o figures/consort.png
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```
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**Output:**
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- `figures/consort_v1.png` - Initial generation
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- `figures/consort_v2.png` - After first review
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- `figures/consort_v3.png` - Final version
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- `figures/consort.png` - Copy of final version
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- `figures/consort_review_log.json` - Detailed review log
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### Example 2: Neural Network Architecture
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```bash
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python scripts/generate_schematic.py \
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"Transformer architecture with encoder on left (input embedding, \
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positional encoding, multi-head attention, feed-forward) and \
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decoder on right (masked attention, cross-attention, feed-forward). \
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Show cross-attention connection from encoder to decoder." \
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-o figures/transformer.png \
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--iterations 2
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```
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### Example 3: Biological Pathway
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```bash
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python scripts/generate_schematic.py \
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"MAPK signaling pathway: EGFR receptor → RAS → RAF → MEK → ERK → nucleus. \
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Label each step with phosphorylation. Use different colors for each kinase." \
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-o figures/mapk.png
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```
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### Example 4: System Architecture
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```bash
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python scripts/generate_schematic.py \
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"IoT system block diagram: sensors (bottom) → microcontroller → \
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WiFi module and display (middle) → cloud server → mobile app (top). \
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Label all connections with protocols." \
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-o figures/iot_system.png
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```
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## Command-Line Options
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```bash
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python scripts/generate_schematic.py [OPTIONS] "description" -o output.png
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Options:
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--iterations N Number of AI refinement iterations (default: 2, max: 2)
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--api-key KEY OpenRouter API key (or use env var)
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-v, --verbose Verbose output
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-h, --help Show help message
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```
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## Python API
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```python
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from scripts.generate_schematic_ai import ScientificSchematicGenerator
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# Initialize
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generator = ScientificSchematicGenerator(
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api_key="your_key",
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verbose=True
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)
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# Generate with iterative refinement
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results = generator.generate_iterative(
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user_prompt="CONSORT flowchart",
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output_path="figures/consort.png",
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iterations=2
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)
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# Access results
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print(f"Final score: {results['final_score']}/10")
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print(f"Final image: {results['final_image']}")
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# Review iterations
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for iteration in results['iterations']:
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print(f"Iteration {iteration['iteration']}: {iteration['score']}/10")
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print(f"Critique: {iteration['critique']}")
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```
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## Prompt Engineering Tips
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### Be Specific About Layout
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✓ "Flowchart with vertical flow, top to bottom"
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✓ "Architecture diagram with encoder on left, decoder on right"
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✗ "Make a diagram" (too vague)
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### Include Quantitative Details
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✓ "Neural network: input (784), hidden (128), output (10)"
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✓ "Flowchart: n=500 screened, n=150 excluded, n=350 randomized"
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✗ "Some numbers" (not specific)
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### Specify Visual Style
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✓ "Minimalist block diagram with clean lines"
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✓ "Detailed biological pathway with protein structures"
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✓ "Technical schematic with engineering notation"
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### Request Specific Labels
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✓ "Label all arrows with activation/inhibition"
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✓ "Include layer dimensions in each box"
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✓ "Show time progression with timestamps"
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|
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### Mention Color Requirements
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✓ "Use colorblind-friendly colors"
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✓ "Grayscale-compatible design"
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✓ "Color-code by function: blue=input, green=processing, red=output"
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## Review Log Format
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Each generation produces a JSON review log:
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```json
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{
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"user_prompt": "CONSORT participant flow diagram...",
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"iterations": [
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{
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||||
"iteration": 1,
|
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"image_path": "figures/consort_v1.png",
|
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"prompt": "Full generation prompt...",
|
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"critique": "Score: 7/10. Issues: font too small...",
|
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"score": 7.0,
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"success": true
|
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},
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{
|
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"iteration": 2,
|
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"image_path": "figures/consort_v2.png",
|
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"score": 8.5,
|
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"critique": "Much improved. Remaining issues..."
|
||||
},
|
||||
{
|
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"iteration": 3,
|
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"image_path": "figures/consort_v3.png",
|
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"score": 9.5,
|
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"critique": "Excellent. Publication ready."
|
||||
}
|
||||
],
|
||||
"final_image": "figures/consort_v3.png",
|
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"final_score": 9.5,
|
||||
"success": true
|
||||
}
|
||||
```
|
||||
|
||||
## Why Use Nano Banana Pro
|
||||
|
||||
**Simply describe what you want - Nano Banana Pro creates it:**
|
||||
|
||||
- ✓ **Fast**: Results in minutes
|
||||
- ✓ **Easy**: Natural language descriptions (no coding)
|
||||
- ✓ **Quality**: Automatic review and refinement
|
||||
- ✓ **Universal**: Works for all diagram types
|
||||
- ✓ **Publication-ready**: High-quality output immediately
|
||||
|
||||
**Just describe your diagram, and it's generated automatically.**
|
||||
|
||||
## Troubleshooting
|
||||
|
||||
### API Key Issues
|
||||
|
||||
```bash
|
||||
# Check if key is set
|
||||
echo $OPENROUTER_API_KEY
|
||||
|
||||
# Set temporarily
|
||||
export OPENROUTER_API_KEY='your_key'
|
||||
|
||||
# Set permanently (add to ~/.bashrc or ~/.zshrc)
|
||||
echo 'export OPENROUTER_API_KEY="your_key"' >> ~/.bashrc
|
||||
```
|
||||
|
||||
### Import Errors
|
||||
|
||||
```bash
|
||||
# Install requests library
|
||||
pip install requests
|
||||
|
||||
# Or use the package manager
|
||||
pip install -r requirements.txt
|
||||
```
|
||||
|
||||
### Generation Fails
|
||||
|
||||
```bash
|
||||
# Use verbose mode to see detailed errors
|
||||
python scripts/generate_schematic.py "diagram" -o out.png -v
|
||||
|
||||
# Check API status
|
||||
curl https://openrouter.ai/api/v1/models
|
||||
```
|
||||
|
||||
### Low Quality Scores
|
||||
|
||||
If iterations consistently score below 7/10:
|
||||
1. Make your prompt more specific
|
||||
2. Include more details about layout and labels
|
||||
3. Specify visual requirements explicitly
|
||||
4. Increase iterations: `--iterations 2`
|
||||
|
||||
## Testing
|
||||
|
||||
Run verification tests:
|
||||
|
||||
```bash
|
||||
python test_ai_generation.py
|
||||
```
|
||||
|
||||
This tests:
|
||||
- File structure
|
||||
- Module imports
|
||||
- Class initialization
|
||||
- Error handling
|
||||
- Prompt engineering
|
||||
- Wrapper script
|
||||
|
||||
## Cost Considerations
|
||||
|
||||
OpenRouter pricing for models used:
|
||||
- **Nano Banana Pro**: ~$2/M input tokens, ~$12/M output tokens
|
||||
|
||||
Typical costs per diagram:
|
||||
- Simple diagram (1 iteration): ~$0.05-0.15
|
||||
- Complex diagram (2 iterations): ~$0.10-0.30
|
||||
|
||||
## Examples Gallery
|
||||
|
||||
See the full SKILL.md for extensive examples including:
|
||||
- CONSORT flowcharts
|
||||
- Neural network architectures (Transformers, CNNs, RNNs)
|
||||
- Biological pathways
|
||||
- Circuit diagrams
|
||||
- System architectures
|
||||
- Block diagrams
|
||||
|
||||
## Support
|
||||
|
||||
For issues or questions:
|
||||
1. Check SKILL.md for detailed documentation
|
||||
2. Run test_ai_generation.py to verify setup
|
||||
3. Use verbose mode (-v) to see detailed errors
|
||||
4. Review the review_log.json for quality feedback
|
||||
|
||||
## License
|
||||
|
||||
Part of the scientific-writer package. See main repository for license information.
|
||||
|
||||
89
scientific-skills/scientific-schematics/scripts/example_usage.sh
Executable file
89
scientific-skills/scientific-schematics/scripts/example_usage.sh
Executable file
@@ -0,0 +1,89 @@
|
||||
#!/bin/bash
|
||||
# Example usage of AI-powered scientific schematic generation
|
||||
#
|
||||
# Prerequisites:
|
||||
# 1. Set OPENROUTER_API_KEY environment variable
|
||||
# 2. Ensure Python 3.10+ is installed
|
||||
# 3. Install requests: pip install requests
|
||||
|
||||
set -e
|
||||
|
||||
echo "=========================================="
|
||||
echo "Scientific Schematics - AI Generation"
|
||||
echo "Example Usage Demonstrations"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
|
||||
# Check for API key
|
||||
if [ -z "$OPENROUTER_API_KEY" ]; then
|
||||
echo "❌ Error: OPENROUTER_API_KEY environment variable not set"
|
||||
echo ""
|
||||
echo "Get an API key at: https://openrouter.ai/keys"
|
||||
echo "Then set it with: export OPENROUTER_API_KEY='your_key'"
|
||||
exit 1
|
||||
fi
|
||||
|
||||
echo "✓ OPENROUTER_API_KEY is set"
|
||||
echo ""
|
||||
|
||||
# Create output directory
|
||||
mkdir -p figures
|
||||
echo "✓ Created figures/ directory"
|
||||
echo ""
|
||||
|
||||
# Example 1: Simple flowchart
|
||||
echo "Example 1: CONSORT Flowchart"
|
||||
echo "----------------------------"
|
||||
python scripts/generate_schematic.py \
|
||||
"CONSORT participant flow diagram. Assessed for eligibility (n=500). Excluded (n=150) with reasons: age<18 (n=80), declined (n=50), other (n=20). Randomized (n=350) into Treatment (n=175) and Control (n=175). Lost to follow-up: 15 and 10. Final analysis: 160 and 165." \
|
||||
-o figures/consort_example.png \
|
||||
--iterations 2
|
||||
|
||||
echo ""
|
||||
echo "✓ Generated: figures/consort_example.png"
|
||||
echo " - Also created: consort_example_v1.png, v2.png, v3.png"
|
||||
echo " - Review log: consort_example_review_log.json"
|
||||
echo ""
|
||||
|
||||
# Example 2: Neural network (shorter for demo)
|
||||
echo "Example 2: Simple Neural Network"
|
||||
echo "--------------------------------"
|
||||
python scripts/generate_schematic.py \
|
||||
"Simple feedforward neural network diagram. Input layer with 4 nodes, hidden layer with 6 nodes, output layer with 2 nodes. Show all connections. Label layers clearly." \
|
||||
-o figures/neural_net_example.png \
|
||||
--iterations 2
|
||||
|
||||
echo ""
|
||||
echo "✓ Generated: figures/neural_net_example.png"
|
||||
echo ""
|
||||
|
||||
# Example 3: Biological pathway (minimal)
|
||||
echo "Example 3: Signaling Pathway"
|
||||
echo "---------------------------"
|
||||
python scripts/generate_schematic.py \
|
||||
"Simple signaling pathway: Receptor → Kinase A → Kinase B → Transcription Factor → Gene. Show arrows with 'activation' labels. Use different colors for each component." \
|
||||
-o figures/pathway_example.png \
|
||||
--iterations 2
|
||||
|
||||
echo ""
|
||||
echo "✓ Generated: figures/pathway_example.png"
|
||||
echo ""
|
||||
|
||||
echo "=========================================="
|
||||
echo "All examples completed successfully!"
|
||||
echo "=========================================="
|
||||
echo ""
|
||||
echo "Generated files in figures/:"
|
||||
ls -lh figures/*example*.png 2>/dev/null || echo " (Files will appear after running with valid API key)"
|
||||
echo ""
|
||||
echo "Review the review_log.json files to see:"
|
||||
echo " - Quality scores for each iteration"
|
||||
echo " - Detailed critiques and suggestions"
|
||||
echo " - Improvement progression"
|
||||
echo ""
|
||||
echo "Next steps:"
|
||||
echo " 1. View the generated images"
|
||||
echo " 2. Review the quality scores in *_review_log.json"
|
||||
echo " 3. Try your own prompts!"
|
||||
echo ""
|
||||
|
||||
@@ -0,0 +1,243 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Test script to verify AI generation implementation.
|
||||
|
||||
This script performs dry-run tests without making actual API calls.
|
||||
It verifies:
|
||||
1. Script structure and imports
|
||||
2. Class initialization
|
||||
3. Method signatures
|
||||
4. Error handling
|
||||
5. Command-line interface
|
||||
|
||||
Usage:
|
||||
python test_ai_generation.py
|
||||
"""
|
||||
|
||||
import sys
|
||||
import os
|
||||
from pathlib import Path
|
||||
|
||||
# Add scripts directory to path
|
||||
scripts_dir = Path(__file__).parent / "scripts"
|
||||
sys.path.insert(0, str(scripts_dir))
|
||||
|
||||
def test_imports():
|
||||
"""Test that all required modules can be imported."""
|
||||
print("Testing imports...")
|
||||
try:
|
||||
from generate_schematic_ai import ScientificSchematicGenerator
|
||||
print("✓ generate_schematic_ai imports successfully")
|
||||
return True
|
||||
except ImportError as e:
|
||||
print(f"✗ Import failed: {e}")
|
||||
return False
|
||||
|
||||
def test_class_structure():
|
||||
"""Test class initialization and structure."""
|
||||
print("\nTesting class structure...")
|
||||
try:
|
||||
from generate_schematic_ai import ScientificSchematicGenerator
|
||||
|
||||
# Test initialization with dummy key
|
||||
generator = ScientificSchematicGenerator(api_key="test_key", verbose=False)
|
||||
print("✓ Class initializes successfully")
|
||||
|
||||
# Check required methods exist
|
||||
required_methods = [
|
||||
'generate_image',
|
||||
'review_image',
|
||||
'improve_prompt',
|
||||
'generate_iterative'
|
||||
]
|
||||
|
||||
for method in required_methods:
|
||||
if not hasattr(generator, method):
|
||||
print(f"✗ Missing method: {method}")
|
||||
return False
|
||||
print(f"✓ Method exists: {method}")
|
||||
|
||||
# Check attributes
|
||||
if not hasattr(generator, 'api_key'):
|
||||
print("✗ Missing attribute: api_key")
|
||||
return False
|
||||
print("✓ Attribute exists: api_key")
|
||||
|
||||
if not hasattr(generator, 'image_model'):
|
||||
print("✗ Missing attribute: image_model")
|
||||
return False
|
||||
print(f"✓ Image model: {generator.image_model}")
|
||||
|
||||
if not hasattr(generator, 'review_model'):
|
||||
print("✗ Missing attribute: review_model")
|
||||
return False
|
||||
print(f"✓ Review model: {generator.review_model}")
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"✗ Class structure test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_error_handling():
|
||||
"""Test error handling for missing API key."""
|
||||
print("\nTesting error handling...")
|
||||
try:
|
||||
from generate_schematic_ai import ScientificSchematicGenerator
|
||||
|
||||
# Clear environment variable
|
||||
old_key = os.environ.get("OPENROUTER_API_KEY")
|
||||
if old_key:
|
||||
del os.environ["OPENROUTER_API_KEY"]
|
||||
|
||||
# Try to initialize without key
|
||||
try:
|
||||
generator = ScientificSchematicGenerator()
|
||||
print("✗ Should have raised ValueError for missing API key")
|
||||
return False
|
||||
except ValueError as e:
|
||||
if "OPENROUTER_API_KEY" in str(e):
|
||||
print("✓ Correctly raises ValueError for missing API key")
|
||||
else:
|
||||
print(f"✗ Wrong error message: {e}")
|
||||
return False
|
||||
|
||||
# Restore environment variable
|
||||
if old_key:
|
||||
os.environ["OPENROUTER_API_KEY"] = old_key
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"✗ Error handling test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_wrapper_script():
|
||||
"""Test wrapper script structure."""
|
||||
print("\nTesting wrapper script...")
|
||||
try:
|
||||
import generate_schematic
|
||||
print("✓ generate_schematic imports successfully")
|
||||
|
||||
# Check main functions exist
|
||||
if not hasattr(generate_schematic, 'main'):
|
||||
print("✗ Missing function: main")
|
||||
return False
|
||||
print("✓ Function exists: main")
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"✗ Wrapper script test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_prompt_engineering():
|
||||
"""Test prompt construction."""
|
||||
print("\nTesting prompt engineering...")
|
||||
try:
|
||||
from generate_schematic_ai import ScientificSchematicGenerator
|
||||
|
||||
generator = ScientificSchematicGenerator(api_key="test_key", verbose=False)
|
||||
|
||||
# Test improve_prompt method
|
||||
original = "Create a flowchart"
|
||||
critique = "Add more spacing between boxes"
|
||||
improved = generator.improve_prompt(original, critique, 2)
|
||||
|
||||
if not improved:
|
||||
print("✗ improve_prompt returned empty string")
|
||||
return False
|
||||
|
||||
if original not in improved:
|
||||
print("✗ Improved prompt doesn't include original")
|
||||
return False
|
||||
|
||||
if critique not in improved:
|
||||
print("✗ Improved prompt doesn't include critique")
|
||||
return False
|
||||
|
||||
if "ITERATION 2" not in improved:
|
||||
print("✗ Improved prompt doesn't include iteration number")
|
||||
return False
|
||||
|
||||
print("✓ Prompt engineering works correctly")
|
||||
print(f" Original length: {len(original)} chars")
|
||||
print(f" Improved length: {len(improved)} chars")
|
||||
|
||||
return True
|
||||
except Exception as e:
|
||||
print(f"✗ Prompt engineering test failed: {e}")
|
||||
return False
|
||||
|
||||
def test_file_paths():
|
||||
"""Test that all required files exist."""
|
||||
print("\nTesting file structure...")
|
||||
|
||||
base_dir = Path(__file__).parent
|
||||
required_files = [
|
||||
"scripts/generate_schematic_ai.py",
|
||||
"scripts/generate_schematic.py",
|
||||
"SKILL.md",
|
||||
"README.md"
|
||||
]
|
||||
|
||||
all_exist = True
|
||||
for file_path in required_files:
|
||||
full_path = base_dir / file_path
|
||||
if full_path.exists():
|
||||
print(f"✓ {file_path}")
|
||||
else:
|
||||
print(f"✗ Missing: {file_path}")
|
||||
all_exist = False
|
||||
|
||||
return all_exist
|
||||
|
||||
def main():
|
||||
"""Run all tests."""
|
||||
print("="*60)
|
||||
print("Scientific Schematics AI Generation - Verification Tests")
|
||||
print("="*60)
|
||||
|
||||
tests = [
|
||||
("File Structure", test_file_paths),
|
||||
("Imports", test_imports),
|
||||
("Class Structure", test_class_structure),
|
||||
("Error Handling", test_error_handling),
|
||||
("Wrapper Script", test_wrapper_script),
|
||||
("Prompt Engineering", test_prompt_engineering),
|
||||
]
|
||||
|
||||
results = []
|
||||
for test_name, test_func in tests:
|
||||
try:
|
||||
result = test_func()
|
||||
results.append((test_name, result))
|
||||
except Exception as e:
|
||||
print(f"\n✗ Test '{test_name}' crashed: {e}")
|
||||
results.append((test_name, False))
|
||||
|
||||
# Summary
|
||||
print("\n" + "="*60)
|
||||
print("Test Summary")
|
||||
print("="*60)
|
||||
|
||||
passed = sum(1 for _, result in results if result)
|
||||
total = len(results)
|
||||
|
||||
for test_name, result in results:
|
||||
status = "✓ PASS" if result else "✗ FAIL"
|
||||
print(f"{status}: {test_name}")
|
||||
|
||||
print(f"\nTotal: {passed}/{total} tests passed")
|
||||
|
||||
if passed == total:
|
||||
print("\n✓ All tests passed! Implementation verified.")
|
||||
print("\nNext steps:")
|
||||
print("1. Set OPENROUTER_API_KEY environment variable")
|
||||
print("2. Test with actual API call:")
|
||||
print(" python scripts/generate_schematic.py 'test diagram' -o test.png")
|
||||
return 0
|
||||
else:
|
||||
print(f"\n✗ {total - passed} test(s) failed. Please review errors above.")
|
||||
return 1
|
||||
|
||||
if __name__ == "__main__":
|
||||
sys.exit(main())
|
||||
|
||||
Reference in New Issue
Block a user