blockbuster-therapy-predictor

name	blockbuster-therapy-predictor
description	Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital mar.
license	MIT
author	AIPOCH

Source: https://github.com/aipoch/medical-research-skills

Blockbuster Therapy Predictor

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.

When to Use

• Use this skill when the task needs Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital mar.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

See ## Features above for related details.

• Scope-focused workflow aligned to: Comprehensive analytics tool for forecasting breakthrough therapeutic technologies by integrating multi-dimensional data sources including clinical development pipelines, intellectual property landscapes, and capital mar.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

Example Usage

See ## Usage above for related details.

cd "20260318/scientific-skills/Evidence Insight/blockbuster-therapy-predictor"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

Features

• Multi-Source Data Integration: Aggregates clinical trials, patents, and funding data
• Predictive Scoring: Calculates Blockbuster Index combining maturity, market potential, and momentum
• Technology Landscape Mapping: Tracks 10+ emerging therapeutic platforms
• Investment Intelligence: Provides data-driven R&D and investment recommendations
• Trend Analysis: Identifies acceleration patterns and inflection points

Usage

Basic Usage

# Run complete analysis with all technologies
python scripts/main.py

# Analyze specific technologies
python scripts/main.py --tech PROTAC,mRNA,CRISPR

# Output in JSON format
python scripts/main.py --output json

Parameters

Parameter	Type	Default	Required	Description
--mode	str	full	No	Analysis mode: full or quick
--tech	str	None	No	Comma-separated list of technologies to analyze
--output	str	console	No	Output format: console or json
--threshold	float	0	No	Minimum blockbuster index threshold (0-100)
--save	str	None	No	Save report to file path

Advanced Usage

# Analyze high-potential technologies only (index ≥70)
python scripts/main.py \
  --threshold 70 \
  --output json \
  --save high_potential_report.json

# Quick analysis of specific platforms
python scripts/main.py \
  --mode quick \
  --tech CAR-T,ADC,Bispecific \
  --output console

Output

Console Output

🏆 BLOCKBUSTER THERAPY PREDICTOR Report
Generated: 2026-02-15 10:30:00
Technologies analyzed: 10

📊 Technology Rankings
Rank  Technology       Blockbuster Index    Maturity    Market Potential    Momentum    Recommendation
🥇 1   mRNA             85.2                 78.5        92.1                88.0        Strongly Recommended
🥈 2   CAR-T            82.3                 85.2        78.5                75.0        Strongly Recommended
🥉 3   CRISPR           79.8                 72.3        88.2                68.0        Recommended

JSON Output Structure

{
  "generated_at": "2026-02-15T10:30:00",
  "total_routes": 10,
  "rankings": [
    {
      "rank": 1,
      "tech_name": "mRNA",
      "blockbuster_index": 85.2,
      "maturity_score": 78.5,
      "market_potential_score": 92.1,
      "momentum_score": 88.0,
      "recommendation": "Strongly Recommended",
      "key_drivers": ["Multiple Phase III trials", "Rapid patent growth"],
      "risk_factors": ["Regulatory uncertainties"],
      "timeline_prediction" 
    
  

Scoring Methodology

Blockbuster Index Formula

Blockbuster Index = (Market Potential × 0.5) + (Maturity × 0.3) + (Momentum × 0.2)

Component Scores

Component	Weight	Factors
Market Potential	50%	Market size, unmet need, competition
Maturity	30%	Clinical stage, patent depth, funding stage
Momentum	20%	Patent growth, funding activity, clinical progress

Investment Recommendation Thresholds

Blockbuster Index	Recommendation	Action
≥ 80	Strongly Recommended	Prioritize R&D investment
60-79	Recommended	Active monitoring and early partnerships
40-59	Watch	Monitor milestones; reassess in 6-12 months
< 40	Cautious	Minimal investment; consider divestment

Supported Technologies

Technology	Category	Description
PROTAC	Protein Degradation	Proteolysis Targeting Chimera
mRNA	Nucleic Acid Drugs	Messenger RNA therapy platform
CRISPR	Gene Editing	CRISPR-Cas gene editing technology
CAR-T	Cell Therapy	Chimeric Antigen Receptor T-cell therapy
Bispecific	Antibody Drugs	Bispecific antibody technology
ADC	Antibody Drugs	Antibody-Drug Conjugate
RNAi	Nucleic Acid Drugs	RNA interference therapy
Gene Therapy	Gene Therapy	AAV vector gene therapy
Allogeneic	Cell Therapy	Universal/Allogeneic cell therapy
Cell Therapy	Cell Therapy	General cell therapy platform

Technical Difficulty: MEDIUM

⚠️ AI independent acceptance status: manual inspection required This skill requires:

• Python 3.8+ environment
• Basic understanding of biotech investment analysis
• Access to clinical trial, patent, and funding databases (optional)

Required Python Packages

pip install -r requirements.txt

Requirements File

dataclasses
enum

Risk Assessment

Risk Indicator	Assessment	Level
Code Execution	Python scripts executed locally	Medium
Network Access	No external API calls in mock mode	Low
File System Access	Read/write report files only	Low
Instruction Tampering	Standard prompt guidelines	Low
Data Exposure	Output files saved to workspace	Low

Security Checklist

• No hardcoded credentials or API keys
• No unauthorized file system access (../)
• Output does not expose sensitive information
• Prompt injection protections in place
• Input file paths validated (no ../ traversal)
• Output directory restricted to workspace
• Script execution in sandboxed environment
• Error messages sanitized (no stack traces exposed)
• Dependencies audited

Prerequisites

# Python dependencies
pip install -r requirements.txt

Evaluation Criteria

Success Metrics

• Successfully executes main functionality
• Output meets quality standards
• Handles edge cases gracefully
• Performance is acceptable

Test Cases

1. Basic Functionality: Run without arguments → Expected output with all technologies
2. Technology Filter: Use --tech flag → Only specified technologies analyzed
3. JSON Output: Use --output json → Valid JSON format output
4. Threshold Filter: Use --threshold 70 → Only technologies with index ≥70 shown

Lifecycle Status

• Current Stage: Draft
• Next Review Date: 2026-03-15
• Known Issues: None
• Planned Improvements:
  • Integration with real-time data APIs
  • Additional technology platforms
  • Enhanced visualization capabilities

References

See references/ for:

• Historical blockbuster case studies
• Clinical trial data sources
• Patent analysis methodologies
• Investment scoring frameworks

Limitations

• Data Source: Uses mock data for demonstration; real-time data integration required for production use
• Prediction Accuracy: Model provides indicative scores; not investment advice
• Technology Coverage: Limited to pre-configured technology platforms
• Market Dynamics: Cannot predict black swan events or regulatory changes
• Regional Bias: Data primarily focused on US/EU markets

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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.

Output Requirements

Every final response should make these items explicit when they are relevant:

• Objective or requested deliverable
• Inputs used and assumptions introduced
• Workflow or decision path
• Core result, recommendation, or artifact
• Constraints, risks, caveats, or validation needs
• Unresolved items and next-step checks

Error Handling

• If required inputs are missing, state exactly which fields are missing and request only the minimum additional information.
• If the task goes outside the documented scope, stop instead of guessing or silently widening the assignment.
• If scripts/main.py fails, report the failure point, summarize what still can be completed safely, and provide a manual fallback.
• Do not fabricate files, citations, data, search results, or execution outcomes.

Input Validation

This skill accepts requests that match the documented purpose of blockbuster-therapy-predictor and include enough context to complete the workflow safely.

Do not continue the workflow when the request is out of scope, missing a critical input, or would require unsupported assumptions. Instead respond:

blockbuster-therapy-predictor only handles its documented workflow. Please provide the missing required inputs or switch to a more suitable skill.

References

• references/audit-reference.md - Supported scope, audit commands, and fallback boundaries

Response Template

Use the following fixed structure for non-trivial requests:

1. Objective
2. Inputs Received
3. Assumptions
4. Workflow
5. Deliverable
6. Risks and Limits
7. Next Checks

If the request is simple, you may compress the structure, but still keep assumptions and limits explicit when they affect correctness.