biotech-pitch-deck-narrative

LeoYeAI/openclaw-master-skillsCommunicationAudit pendingSnapshot b4cecde1ed18

Summary

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SKILL.md

Biotech Pitch Deck Narrative

Overview

Strategic communication tool that translates complex biotechnology innovations into compelling business narratives optimized for venture capital, pharmaceutical partnerships, and public market investors.

Key Capabilities:

Science Translation: Convert technical data into business value language
Narrative Architecture: Structure Problem→Solution→Market→Traction→Vision flow
Stage Optimization: Tailor messaging for seed through IPO fundraising
Investor Calibration: Adapt for generalist vs. specialist audiences
Risk Mitigation: Frame scientific and regulatory risks as manageable challenges
Q&A Preparation: Anticipate investor questions and prepare responses

When to Use

✅ Use this skill when:

Preparing Series A/B pitch decks for VC presentations
Creating management presentations for IPO roadshows
Developing BD materials for pharma partnership discussions
Crafting executive summaries for grant applications
Rehearsing investor Q&A for earnings calls
Translating clinical data into commercial narratives
Adapting academic presentations for business audiences

❌ Do NOT use when:

Scientific conference presentations → Use technical language
Regulatory submission documents → Use formal FDA/EMA formats
Internal R&D team communications → Use full scientific detail
Patent applications → Use precise legal/scientific terminology
Patient-facing materials → Use lay-summary-gen

Integration:

Upstream: market-access-value (commercial assessment), (competitive landscape)

competitor-trial-monitor

from scripts.narrative_engine import BiotechNarrativeEngine

engine = BiotechNarrativeEngine()

# Translate technical description
translation = engine.translate_science(
    technical_description="""
    Our proprietary AAV9-based gene therapy utilizes a codon-optimized 
    transgene under control of a liver-specific promoter to restore 
    functional enzyme in patients with MPS I deficiency.
    """,
    audience="generalist_vc",
    preserve_accuracy=True
)

print(translation.business_narrative)
# "One-time gene therapy delivering a functional copy of the missing enzyme, 
#  potentially curing MPS I rather than managing symptoms"

Technical Concept	Business Translation	Why It Works
"CRISPR-Cas9 gene editing"	"Precision genetic medicine platform"	Platform implies scalability
"Phase II clinical data"	"De-risked asset with human proof-of-concept"	Reduces perceived risk
"Off-target effects"	"Industry-leading specificity profile"	Competitive framing
"MOA via JAK-STAT pathway"	"Novel mechanism addressing root cause"	Value proposition

# Generate complete narrative arc
narrative = engine.build_narrative(
    company_stage="series_b",
    science_type="gene_therapy",
    clinical_stage="phase_2",
    target_market="rare_disease",
    key_differentiation="one_time_cure"
)

# Access each component
print(narrative.hook)           # Opening grab
print(narrative.problem)        # Market pain point
print(narrative.solution)       # Your approach
print(narrative.traction)       # Validation to date
print(narrative.ask)            # Funding request

# Optimize for different stages
seed_narrative = engine.optimize_for_stage(
    base_narrative=narrative,
    stage="seed",
    focus="team_and_vision"  # Seed cares about team and big idea
)

series_a_narrative = engine.optimize_for_stage(
    base_narrative=narrative,
    stage="series_a",
    focus="proof_of_concept"  # Series A needs validation
)

ipo_narrative = engine.optimize_for_stage(
    base_narrative=narrative,
    stage="ipo",
    focus="commercial_readiness"  # IPO requires near-term revenue
)

Stage	Key Questions	Focus Areas
Seed ($500K-$2M)	Can you execute?	Team, vision, early validation
Series A ($10-30M)	Does it work?	POC data, IP position, market entry
Series B ($30-75M)	Will it scale?	Phase 2/3 data, BD traction, team expansion
Series C/IPO ($100M+)	Commercial execution	Registration trials, launch prep, revenue path

# Calibrate for specific investor
calibrated = engine.calibrate_for_audience(
    narrative=narrative,
    investor_type="healthcare_vc",  # vs "generalist_vc" or "pharma_corp"
    technical_depth="moderate",      # Depth of scientific detail
    risk_tolerance="high"            # Early vs late stage framing
)

# Generate complete pitch narrative
python scripts/main.py \
  --science "Small molecule inhibitor targeting mutant KRAS G12C" \
  --stage "phase_2" \
  --indication "lung_cancer" \
  --data "ORR 45%, median PFS 6.5 months" \
  --competition "Mirati, J&J" \
  --output series_b_narrative.json

platform_narrative = engine.generate_platform_narrative(
    platform_technology="Lipid nanoparticle for CNS delivery",
    differentiator="Crosses BBB with 50x improvement over existing LNPs",
    applications=["Alzheimer's", "Parkinson's", "brain_cancer"],
    stage="seed",
    target="platform_value_creation"
)

device_narrative = engine.generate_device_narrative(
    device_type="surgical_robot",
    clinical_benefit="50% reduction in complications, 30% faster recovery",
    regulatory_path="510k_de_novo",
    reimbursement="CPT_code_established",
    stage="series_a"
)

# Generate BD materials
python scripts/main.py \
  --mode partnership \
  --asset "Phase 2 ready asset" \
  --indication "NASH" \
  --data_package "Phase 1b complete, biomarker validated" \
  --partner_profile "novo_nordisk" \
  --output bd_presentation.json

from scripts.narrative_engine import BiotechNarrativeEngine
from scripts.slide_generator import SlideGenerator
from scripts.qa_prep import QAPreparation

# Initialize
engine = BiotechNarrativeEngine()
slides = SlideGenerator()
qa = QAPreparation()

# Step 1: Generate core narrative
narrative = engine.build_narrative(
    company_stage="series_a",
    therapeutic_area="oncology",
    modality="cell_therapy",
    clinical_stage="phase_1",
    key_differentiation="allogeneic_off_the_shelf"
)

# Step 2: Create slide-by-slide guidance
slide_guide = slides.generate_guide(
    narrative=narrative,
    n_slides=12,
    include_visual_suggestions=True
)

# Step 3: Prepare Q&A
qa_prep = qa.generate_qa(
    narrative=narrative,
    investor_type="healthcare_vc",
    depth="comprehensive"
)

# Step 4: Export complete package
engine.export_package(
    narrative=narrative,
    slides=slide_guide,
    qa=qa_prep,
    output_dir="series_a_pitch_package/"
)

Parameter	Type	Default	Required	Description
`--science`	string	-	Yes*	Scientific description of technology
`--stage`	string	-	Yes*	Funding stage (pre-seed, seed, series-a, etc.)
`--audience`	string	-	Yes*	Target audience type (generalist-vc, healthcare-vc, etc.)
`--section`	string	-	No	Section to rewrite (hook, problem, solution, etc.)
`--content`	string	-	No	Content to rewrite
`--input`	string	-	No	Input file path
`--output`, `-o`	string	-	No	Output file path

# Generate narrative from science description
python scripts/main.py generate --science "CRISPR gene therapy for sickle cell" --stage series-a --audience healthcare-vc

# Rewrite specific section
python scripts/main.py rewrite --section technology --content "We use AAV vectors..." --audience generalist-vc

# Analyze existing pitch deck
python scripts/main.py analyze --input pitch.pptx --stage series-a

Risk Indicator	Assessment	Level
Code Execution	Python script executed locally	Low
Network Access	No external API calls	Low
File System Access	Read/write files	Low
Data Exposure	May process confidential business info	Medium
Regulatory	Does not ensure SEC compliance	Medium

# Python 3.7+
# No additional packages required (uses standard library)

biotech-pitch-deck-narrative

Summary

SKILL.md

Biotech Pitch Deck Narrative

Overview

When to Use

Core Capabilities

1. Science-to-Business Translation

2. Narrative Architecture

3. Stage-Specific Optimization

4. Investor Audience Calibration

Common Patterns

Pattern 1: Clinical-Stage Therapeutics

Pattern 2: Platform Company

Pattern 3: MedTech Device

Pattern 4: Pharma Partnership Pitch

Complete Workflow Example

Quality Checklist

Common Pitfalls

References

Scripts

Limitations

Parameters

Usage

Basic Usage

Risk Assessment

Security Checklist

Prerequisites

Evaluation Criteria

Success Metrics

Test Cases

Lifecycle Status

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