KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.
Fetches kegg-database from davila7/claude-code-templates and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
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Restart Cursor to activate kegg-database. Access via /kegg-database in your agent's command palette.
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Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
KEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.
Important: KEGG API is made available only for academic use by academic users.
When to Use This Skill
This skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.
Quick Start
The skill provides:
Python helper functions (scripts/kegg_api.py) for all KEGG REST API operations
Comprehensive reference documentation (references/kegg_reference.md) with detailed API specifications
When users request KEGG data, determine which operation is needed and use the appropriate function from scripts/kegg_api.py.
Core Operations
1. Database Information (kegg_info)
Retrieve metadata and statistics about KEGG databases.
When to use: Understanding database structure, checking available data, getting release information.
Usage:
from scripts.kegg_api import kegg_info
# Get pathway database infoinfo = kegg_info('pathway')# Get organism-specific infohsa_info = kegg_info('hsa')# Human genome
Common databases: kegg, pathway, module, brite, genes, genome, compound, glycan, reaction, enzyme, disease, drug
2. Listing Entries (kegg_list)
List entry identifiers and names from KEGG databases.
When to use: Getting all pathways for an organism, listing genes, retrieving compound catalogs.
Usage:
from scripts.kegg_api import kegg_list
# List all reference pathwayspathways = kegg_list('pathway')# List human-specific pathwayshsa_pathways = kegg_list('pathway','hsa')# List specific genes (max 10)genes = kegg_list('hsa:10458+hsa:10459')
Common organism codes: hsa (human), mmu (mouse), dme (fruit fly), sce (yeast), eco (E. coli)
3. Searching (kegg_find)
Search KEGG databases by keywords or molecular properties.
When to use: Finding genes by name/description, searching compounds by formula or mass, discovering entries by keywords.
Usage:
from scripts.kegg_api import kegg_find
# Keyword searchresults = kegg_find('genes','p53')shiga_toxin = kegg_find('genes','shiga toxin')# Chemical formula search (exact match)compounds = kegg_find('compound','C7H10N4O2','formula')# Molecular weight range searchdrugs = kegg_find('drug','300-310','exact_mass')
Search options: formula (exact match), exact_mass (range), mol_weight (range)
4. Retrieving Entries (kegg_get)
Get complete database entries or specific data formats.
When to use: Retrieving pathway details, getting gene/protein sequences, downloading pathway maps, accessing compound structures.
Usage:
from scripts.kegg_api import kegg_get
# Get pathway entrypathway = kegg_get('hsa00010')# Glycolysis pathway# Get multiple entries (max 10)genes = kegg_get(['hsa:10458','hsa:10459'])# Get protein sequence (FASTA)sequence = kegg_get('hsa:10458','aaseq')# Get nucleotide sequencent_seq = kegg_get('hsa:10458','ntseq')# Get compound structuremol_file = kegg_get('cpd:C00002','mol')# ATP in MOL format# Get pathway as JSON (single entry only)pathway_json = kegg_get('hsa05130','json')# Get pathway image (single entry only)pathway_img = kegg_get('hsa05130','image')
Important: Image, KGML, and JSON formats allow only one entry at a time.
5. ID Conversion (kegg_conv)
Convert identifiers between KEGG and external databases.
When to use: Integrating KEGG data with other databases, mapping gene IDs, converting compound identifiers.
Usage:
from scripts.kegg_api import kegg_conv
# Convert all human genes to NCBI Gene IDsconversions = kegg_conv('ncbi-geneid','hsa')# Convert specific genegene_id = kegg_conv('ncbi-geneid','hsa:10458')# Convert to UniProtuniprot_id = kegg_conv('uniprot','hsa:10458')# Convert compounds to PubChempubchem_ids = kegg_conv('pubchem','compound')# Reverse conversion (NCBI Gene ID to KEGG)kegg_id = kegg_conv('hsa','ncbi-geneid')
Find related entries within and between KEGG databases.
When to use: Finding pathways containing genes, getting genes in a pathway, mapping genes to KO groups, finding compounds in pathways.
Usage:
from scripts.kegg_api import kegg_link
# Find pathways linked to human genespathways = kegg_link('pathway','hsa')# Get genes in a specific pathwaygenes = kegg_link('genes','hsa00010')# Glycolysis genes# Find pathways containing a specific genegene_pathways = kegg_link('pathway','hsa:10458')# Find compounds in a pathwaycompounds = kegg_link('compound','hsa00010')# Map genes to KO (orthology) groupsko_groups = kegg_link('ko','hsa:10458')
Common links: genes ↔ pathway, pathway ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology)
7. Drug-Drug Interactions (kegg_ddi)
Check for drug-drug interactions.
When to use: Analyzing drug combinations, checking for contraindications, pharmacological research.
Usage:
from scripts.kegg_api import kegg_ddi
# Check single druginteractions = kegg_ddi('D00001')# Check multiple drugs (max 10)interactions = kegg_ddi(['D00001','D00002','D00003'])
Common Analysis Workflows
Workflow 1: Gene to Pathway Mapping
Use case: Finding pathways associated with genes of interest (e.g., for pathway enrichment analysis).
from scripts.kegg_api import kegg_find, kegg_link, kegg_get
# Step 1: Find gene ID by namegene_results = kegg_find('genes','p53')# Step 2: Link gene to pathwayspathways = kegg_link('pathway','hsa:7157')# TP53 gene# Step 3: Get detailed pathway informationfor pathway_line in pathways.split('\n'):if pathway_line: pathway_id = pathway_line.split('\t')[1].replace('path:','') pathway_info = kegg_get(pathway_id)# Process pathway information
Workflow 2: Pathway Enrichment Context
Use case: Getting all genes in organism pathways for enrichment analysis.
from scripts.kegg_api import kegg_list, kegg_link
# Step 1: List all human pathwayspathways = kegg_list('pathway','hsa')# Step 2: For each pathway, get associated genesfor pathway_line in pathways.split('\n'):if pathway_line: pathway_id = pathway_line.split('\t')[0] genes = kegg_link('genes', pathway_id)# Process genes for enrichment analysis
Workflow 3: Compound to Pathway Analysis
Use case: Finding metabolic pathways containing compounds of interest.
from scripts.kegg_api import kegg_find, kegg_link, kegg_get
# Step 1: Search for compoundcompound_results = kegg_find('compound'
✓
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
›Access to product documentation and roadmap tools (Jira, Notion, etc.)
›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
1Install product management skill
2Start with user story generation for known feature
3Progress to competitive analysis: research 2-3 competitors
4Use for roadmap prioritization: apply RICE/ICE scoring
5Draft stakeholder communications and refine based on feedback
6Build template library for recurring PM tasks
7Share effective prompts with product team
Common Pitfalls
⚠Not validating competitive research—verify facts before sharing
⚠Accepting user stories without involving engineering team
⚠Over-relying on frameworks without qualitative judgment
⚠Not customizing outputs to company culture and communication style
⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
+Validate research and competitive analysis with real data
+Collaborate with engineering when generating technical requirements
+Customize frameworks and templates to your company context
+Use skill for first drafts, refine with stakeholder input
+Document successful prompt patterns for PM tasks
+Combine AI efficiency with human judgment and intuition
✗ Don't
−Don't publish competitive analysis without fact-checking
−Don't finalize user stories without engineering review
−Don't make prioritization decisions solely on AI scoring
−Don't skip customer validation of generated requirements
−Don't ignore company-specific context and culture
💡 Pro Tips
★Provide context: company goals, constraints, customer feedback
★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
1Basic: user stories, feature specs, status updates
