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Add news skill for Skill-Drug-Discovery. Patent landscape (verified) & therapy-predictor
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‎skills/Skill-Drug-Discovery/blockbuster-therapy-predictor-0.1.0/SKILL.md‎

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---
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name: blockbuster-therapy-predictor
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description: "Predict which early-stage biotechnology platforms (PROTAC, mRNA, gene
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editing, etc.) have the highest potential to become blockbuster therapies.
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Analyzes clinical trial progression, patent landscape maturity, and venture
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capital funding trends to generate investment and R&D prioritization scores.
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Trigger when: User asks about technology investment potential, platform
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selection, or therapeutic modality comparison."
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version: 1.0.0
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category: Pharma
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tags: ["investment", "prediction", "biotech", "clinical-trials", "patents"]
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author: AIPOCH
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license: MIT
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status: Draft
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risk_level: Medium
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skill_type: Tool/Script
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owner: AIPOCH
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reviewer: ''
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last_updated: '2026-02-15'
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---
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# Blockbuster Therapy Predictor
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Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital market indicators.
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## Features
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- **Multi-Source Data Integration**: Aggregates clinical trials, patents, and funding data
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- **Predictive Scoring**: Calculates Blockbuster Index combining maturity, market potential, and momentum
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- **Technology Landscape Mapping**: Tracks 10+ emerging therapeutic platforms
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- **Investment Intelligence**: Provides data-driven R&D and investment recommendations
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- **Trend Analysis**: Identifies acceleration patterns and inflection points
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## Usage
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### Basic Usage
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```bash
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# Run complete analysis with all technologies
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python scripts/main.py
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# Analyze specific technologies
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python scripts/main.py --tech PROTAC,mRNA,CRISPR
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# Output in JSON format
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python scripts/main.py --output json
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```
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### Parameters
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| Parameter | Type | Default | Required | Description |
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|-----------|------|---------|----------|-------------|
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| `--mode` | str | full | No | Analysis mode: full or quick |
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| `--tech` | str | None | No | Comma-separated list of technologies to analyze |
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| `--output` | str | console | No | Output format: console or json |
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| `--threshold` | float | 0 | No | Minimum blockbuster index threshold (0-100) |
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| `--save` | str | None | No | Save report to file path |
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### Advanced Usage
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```bash
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# Analyze high-potential technologies only (index ≥70)
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python scripts/main.py \
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--threshold 70 \
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--output json \
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--save high_potential_report.json
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# Quick analysis of specific platforms
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python scripts/main.py \
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--mode quick \
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--tech CAR-T,ADC,Bispecific \
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--output console
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```
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## Output
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### Console Output
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```
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🏆 BLOCKBUSTER THERAPY PREDICTOR Report
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Generated: 2026-02-15 10:30:00
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Technologies analyzed: 10
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📊 Technology Rankings
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Rank Technology Blockbuster Index Maturity Market Potential Momentum Recommendation
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🥇 1 mRNA 85.2 78.5 92.1 88.0 Strongly Recommended
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🥈 2 CAR-T 82.3 85.2 78.5 75.0 Strongly Recommended
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🥉 3 CRISPR 79.8 72.3 88.2 68.0 Recommended
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```
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### JSON Output Structure
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```json
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{
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"generated_at": "2026-02-15T10:30:00",
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"total_routes": 10,
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"rankings": [
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{
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"rank": 1,
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"tech_name": "mRNA",
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"blockbuster_index": 85.2,
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"maturity_score": 78.5,
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"market_potential_score": 92.1,
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"momentum_score": 88.0,
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"recommendation": "Strongly Recommended",
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"key_drivers": ["Multiple Phase III trials", "Rapid patent growth"],
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"risk_factors": ["Regulatory uncertainties"],
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"timeline_prediction": "First product expected in 2-4 years"
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}
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]
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}
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```
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## Scoring Methodology
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### Blockbuster Index Formula
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```
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Blockbuster Index = (Market Potential × 0.5) + (Maturity × 0.3) + (Momentum × 0.2)
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```
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### Component Scores
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| Component | Weight | Factors |
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|-----------|--------|---------|
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| **Market Potential** | 50% | Market size, unmet need, competition |
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| **Maturity** | 30% | Clinical stage, patent depth, funding stage |
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| **Momentum** | 20% | Patent growth, funding activity, clinical progress |
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### Investment Recommendation Thresholds
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| Blockbuster Index | Recommendation | Action |
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|-------------------|----------------|--------|
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| ≥ 80 | **Strongly Recommended** | Prioritize R&D investment |
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| 60-79 | **Recommended** | Active monitoring and early partnerships |
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| 40-59 | **Watch** | Monitor milestones; reassess in 6-12 months |
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| < 40 | **Cautious** | Minimal investment; consider divestment |
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## Supported Technologies
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| Technology | Category | Description |
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|------------|----------|-------------|
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| PROTAC | Protein Degradation | Proteolysis Targeting Chimera |
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| mRNA | Nucleic Acid Drugs | Messenger RNA therapy platform |
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| CRISPR | Gene Editing | CRISPR-Cas gene editing technology |
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| CAR-T | Cell Therapy | Chimeric Antigen Receptor T-cell therapy |
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| Bispecific | Antibody Drugs | Bispecific antibody technology |
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| ADC | Antibody Drugs | Antibody-Drug Conjugate |
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| RNAi | Nucleic Acid Drugs | RNA interference therapy |
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| Gene Therapy | Gene Therapy | AAV vector gene therapy |
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| Allogeneic | Cell Therapy | Universal/Allogeneic cell therapy |
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| Cell Therapy | Cell Therapy | General cell therapy platform |
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## Technical Difficulty: **MEDIUM**
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⚠️ **AI自主验收状态**: 需人工检查
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This skill requires:
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- Python 3.8+ environment
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- Basic understanding of biotech investment analysis
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- Access to clinical trial, patent, and funding databases (optional)
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## Dependencies
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### Required Python Packages
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```bash
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pip install -r requirements.txt
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```
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### Requirements File
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```
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dataclasses
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enum
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```
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## Risk Assessment
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| Risk Indicator | Assessment | Level |
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|----------------|------------|-------|
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| Code Execution | Python scripts executed locally | Medium |
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| Network Access | No external API calls in mock mode | Low |
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| File System Access | Read/write report files only | Low |
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| Instruction Tampering | Standard prompt guidelines | Low |
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| Data Exposure | Output files saved to workspace | Low |
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## Security Checklist
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- [x] No hardcoded credentials or API keys
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- [x] No unauthorized file system access (../)
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- [x] Output does not expose sensitive information
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- [x] Prompt injection protections in place
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- [x] Input file paths validated (no ../ traversal)
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- [x] Output directory restricted to workspace
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- [x] Script execution in sandboxed environment
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- [x] Error messages sanitized (no stack traces exposed)
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- [x] Dependencies audited
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## Prerequisites
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```bash
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# Python dependencies
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pip install -r requirements.txt
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```
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## Evaluation Criteria
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### Success Metrics
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- [ ] Successfully executes main functionality
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- [ ] Output meets quality standards
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- [ ] Handles edge cases gracefully
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- [ ] Performance is acceptable
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### Test Cases
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1. **Basic Functionality**: Run without arguments → Expected output with all technologies
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2. **Technology Filter**: Use --tech flag → Only specified technologies analyzed
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3. **JSON Output**: Use --output json → Valid JSON format output
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4. **Threshold Filter**: Use --threshold 70 → Only technologies with index ≥70 shown
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## Lifecycle Status
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- **Current Stage**: Draft
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- **Next Review Date**: 2026-03-15
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- **Known Issues**: None
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- **Planned Improvements**:
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- Integration with real-time data APIs
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- Additional technology platforms
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- Enhanced visualization capabilities
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## References
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See `references/` for:
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- Historical blockbuster case studies
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- Clinical trial data sources
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- Patent analysis methodologies
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- Investment scoring frameworks
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## Limitations
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- **Data Source**: Uses mock data for demonstration; real-time data integration required for production use
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- **Prediction Accuracy**: Model provides indicative scores; not investment advice
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- **Technology Coverage**: Limited to pre-configured technology platforms
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- **Market Dynamics**: Cannot predict black swan events or regulatory changes
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- **Regional Bias**: Data primarily focused on US/EU markets
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---
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**⚠️ DISCLAIMER: This tool provides quantitative analysis for decision support only. All investment and R&D decisions should incorporate qualitative domain expertise, regulatory consultation, and comprehensive due diligence. Past performance of historical blockbusters does not guarantee future success of emerging technologies.**

‎skills/Skill-Drug-Discovery/blockbuster-therapy-predictor-0.1.0/_meta.json‎

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{
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"ownerId": "kn76fcc8jhxekae91m2ekbrsgn821avs",
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"slug": "blockbuster-therapy-predictor",
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"version": "0.1.0",
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"publishedAt": 1773718619330
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}

‎skills/Skill-Drug-Discovery/blockbuster-therapy-predictor-0.1.0/requirements.txt‎

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dataclasses
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enum

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