How do I install the Patent Landscape skill?

Run npx skillmds add majiayu000/patent-landscape in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Patent Landscape safe to use?

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Which AI agents work with Patent Landscape?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Patent Landscape free to use?

Yes. Installing skills from SkillMD is free. This skill is licensed under MIT.

Who published Patent Landscape?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Patent Landscape by majiayu000 — Agent Skill

Name: Patent Landscape
Rating: 4.2 (5 reviews)
Author: majiayu000

Patent Landscape

Use when analyzing biotech patent landscapes, identifying white spaces in pharmaceutical IP, tracking competitor patents, or assessing freedom to operate for drug development. Provides comprehensive patent analysis and strategic insights for life sciences innovation.

majiayu000 1fb2365 2 files · 4.1 KB Updated Sep 9, 2026 567 repo stars

File contents

Biotech Patent Landscape Analyzer

Analyze biotech and pharmaceutical patent landscapes to identify opportunities, assess competition, and guide R&D strategy.

Quick Start

from scripts.patent_landscape import PatentLandscapeAnalyzer

analyzer = PatentLandscapeAnalyzer()

# Analyze therapeutic area
landscape = analyzer.analyze(
    therapeutic_area="CAR-T cell therapy",
    date_range="2020-2024",
    assignees=["Novartis", "Kite Pharma", "Juno Therapeutics"]
)

Core Capabilities

1. Patent Search & Analysis

results = analyzer.search_patents(
    keywords=["CRISPR", "gene editing", "therapeutic"],
    classification="C12N15/113",  # IPC class
    jurisdictions=["US", "EP", "WO"]
)

Search Strategies:

Keyword-based: Technical terms + synonyms
Classification-based: IPC/CPC codes
Citation-based: Forward/backward citations
Assignee-based: Company portfolios

2. White Space Analysis

opportunities = analyzer.identify_white_spaces(
    technology="Antibody-drug conjugates",
    target_diseases=["breast cancer", "lung cancer"],
    existing_claims=landscape
)

White Space Opportunities:

Underserved disease indications
Novel combination therapies
Alternative delivery mechanisms
Geographical gaps (emerging markets)

3. Competitor Intelligence

competitors = analyzer.analyze_competitors(
    companies=["Pfizer", "Moderna", "BioNTech"],
    focus_area="mRNA vaccines"
)

Competitor Metrics:

Metric	Description
Portfolio size	Total active patents
Filing velocity	Recent filing trends
Geographic coverage	Jurisdiction strategy
Technology focus	Core vs. peripheral areas
Partnership patterns	Collaboration trends

4. Freedom to Operate (FTO) Assessment

fto = analyzer.assess_fto(
    product_concept="Bispecific antibody targeting PD-1 and CTLA-4",
    jurisdictions=["US", "EU", "Japan"]
)

FTO Analysis Steps:

Identify relevant patent claims
Map claims to product features
Assess validity of blocking patents
Design around options
Licensing recommendations

CLI Usage

# Generate patent landscape report
python scripts/patent_landscape.py \
  --query "immuno-oncology checkpoint inhibitors" \
  --output landscape_report.pdf \
  --format comprehensive

# Quick FTO check
python scripts/patent_landscape.py \
  --fto "product_description.txt" \
  --jurisdictions US EP JP

Data Sources

USPTO (United States)
EPO (Europe)
WIPO (Global)
JPO (Japan)
CNIPA (China)

References

references/ipc-classifications.md - IPC/CPC codes for biotech
references/patent-search-strategies.md - Advanced search techniques
examples/landscape-reports/ - Sample reports

Skill ID: 204 | Version: 1.0 | License: MIT

---name: patent-landscapedescription: Use when analyzing biotech patent landscapes, identifying white spaces in pharmaceutical IP, tracking competitor patents, or assessing freedom to operate for drug development. Provides comprehensive patent analysis and strategic insights for life sciences innovation.license: MIT--- # Biotech Patent Landscape Analyzer Analyze biotech and pharmaceutical patent landscapes to identify opportunities, assess competition, and guide R&D strategy. ## Quick Start ```python from scripts.patent_landscape import PatentLandscapeAnalyzer analyzer = PatentLandscapeAnalyzer() # Analyze therapeutic area landscape = analyzer.analyze( therapeutic_area="CAR-T cell therapy", date_range="2020-2024", assignees=["Novartis", "Kite Pharma", "Juno Therapeutics"] ) ``` ## Core Capabilities ### 1. Patent Search & Analysis ```python results = analyzer.search_patents( keywords=["CRISPR", "gene editing", "therapeutic"], classification="C12N15/113", # IPC class jurisdictions=["US", "EP", "WO"] ) ``` **Search Strategies:** - **Keyword-based**: Technical terms + synonyms - **Classification-based**: IPC/CPC codes - **Citation-based**: Forward/backward citations - **Assignee-based**: Company portfolios ### 2. White Space Analysis ```python opportunities = analyzer.identify_white_spaces( technology="Antibody-drug conjugates", target_diseases=["breast cancer", "lung cancer"], existing_claims=landscape ) ``` **White Space Opportunities:** - Underserved disease indications - Novel combination therapies - Alternative delivery mechanisms - Geographical gaps (emerging markets) ### 3. Competitor Intelligence ```python competitors = analyzer.analyze_competitors( companies=["Pfizer", "Moderna", "BioNTech"], focus_area="mRNA vaccines" ) ``` **Competitor Metrics:** | Metric | Description | |--------|-------------| | Portfolio size | Total active patents | | Filing velocity | Recent filing trends | | Geographic coverage | Jurisdiction strategy | | Technology focus | Core vs. peripheral areas | | Partnership patterns | Collaboration trends | ### 4. Freedom to Operate (FTO) Assessment ```python fto = analyzer.assess_fto( product_concept="Bispecific antibody targeting PD-1 and CTLA-4", jurisdictions=["US", "EU", "Japan"] ) ``` **FTO Analysis Steps:** 1. Identify relevant patent claims 2. Map claims to product features 3. Assess validity of blocking patents 4. Design around options 5. Licensing recommendations ## CLI Usage ```bash # Generate patent landscape report python scripts/patent_landscape.py \ --query "immuno-oncology checkpoint inhibitors" \ --output landscape_report.pdf \ --format comprehensive # Quick FTO check python scripts/patent_landscape.py \ --fto "product_description.txt" \ --jurisdictions US EP JP ``` ## Data Sources - USPTO (United States) - EPO (Europe) - WIPO (Global) - JPO (Japan) - CNIPA (China) ## References - `references/ipc-classifications.md` - IPC/CPC codes for biotech - `references/patent-search-strategies.md` - Advanced search techniques - `examples/landscape-reports/` - Sample reports --- **Skill ID**: 204 | **Version**: 1.0 | **License**: MIT

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