blockbuster-therapy-predictor
Predict which early-stage biotechnology platforms (PROTAC, mRNA, gene editing, etc.) have the highest potential to become blockbuster therapies. Analyzes clinical trial progression, patent landscape maturity, and venture capital funding trends to generate investment and R&D prioritization scores. Trigger when: User asks about technology investment potential, platform selection, or therapeutic modality comparison.
82
75%
Does it follow best practices?
Impact
95%
2.96xAverage score across 3 eval scenarios
Passed
No known issues
Optimize this skill with Tessl
npx tessl skill review --optimize ./scientific-skills/Evidence insights/blockbuster-therapy-predictor/SKILL.mdEvaluation results
90%
65%Biotech Investment Portfolio Screening
Threshold filtering and JSON report generation
Correct script invocation
50%
100%
Threshold flag used
0%
100%
JSON output flag used
0%
100%
Save flag used
0%
80%
high_potential_report.json exists
100%
100%
JSON structure correct
25%
100%
Rankings contain required fields
25%
100%
Only high-threshold techs included
100%
100%
Strongly Recommended threshold correct
0%
100%
Recommended threshold correct
0%
100%
Watch threshold correct
0%
0%
97%
49%Nucleic Acid Platform Head-to-Head Analysis
Technology filtering and component score interpretation
Correct tech names
50%
100%
Tech filter applied
30%
100%
Quick mode used
0%
100%
Save flag used
0%
87%
JSON file present
100%
100%
Three platforms in JSON
100%
100%
Formula weights correct
0%
100%
Three component scores listed
100%
100%
Correct script entry point
87%
75%
JSON rankings fields present
0%
100%
No extra platforms
100%
100%
99%
74%R&D Prioritization Study: Full Technology Landscape
Full analysis execution and scoring methodology
Correct script invocation
100%
87%
JSON output flag
0%
100%
Save flag with correct filename
0%
100%
JSON file present
100%
100%
All 10 platforms in JSON
40%
100%
Market Potential weight
0%
100%
Maturity weight
0%
100%
Momentum weight
0%
100%
Strongly Recommended tier described
0%
100%
Watch tier described
0%
100%
Cautious tier described
0%
100%
Timeline fields used
70%
100%
- Repository
- aipoch/medical-research-skills
- Commit
ca9aaa4
- Evaluated
- Agent
- Claude Code
- Model
- Claude Sonnet 4.6
Table of Contents
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