[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6R34FA60KMRVMDNT5EYVAF3":3,"skill-same:01M6R34FA60KMRVMDNT5EYVAF3":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":6,"ownerHtmlUrl":54,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":55,"install":56,"resources":69},"01M6R34FA60KMRVMDNT5EYVAF3","kegg-database",null,"Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP\u002FREST work or KEGG-specific control.","Mishit18\u002Fclaude-code-templates","Mishit18","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F244127857?v=4","01M6QXBYKWM76HSKYDNWSKKE41","Mishit Sharma","01M6QXBYRJ0CAVM51S1RTEKTZZ",1,"MIT","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fkegg-database\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"database","kegg database","KEGG","use","gene","for","mapping","pathway","drug interactions","conversion","databases","only","python","work","direct","access","api","analysis","bioservices","workflows","2026-11-10 10:12:30",false,"---\nname: kegg-database\ndescription: \"Direct REST API access to KEGG (academic use only). Pathway analysis, gene-pathway mapping, metabolic pathways, drug interactions, ID conversion. For Python workflows with multiple databases, prefer bioservices. Use this for direct HTTP\u002FREST work or KEGG-specific control.\"\n---\n\n# KEGG Database\n\n## Overview\n\nKEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.\n\n**Important**: KEGG API is made available only for academic use by academic users.\n\n## When to Use This Skill\n\nThis skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.\n\n## Quick Start\n\nThe skill provides:\n1. Python helper functions (`scripts\u002Fkegg_api.py`) for all KEGG REST API operations\n2. Comprehensive reference documentation (`references\u002Fkegg_reference.md`) with detailed API specifications\n\nWhen users request KEGG data, determine which operation is needed and use the appropriate function from `scripts\u002Fkegg_api.py`.\n\n## Core Operations\n\n### 1. Database Information (`kegg_info`)\n\nRetrieve metadata and statistics about KEGG databases.\n\n**When to use**: Understanding database structure, checking available data, getting release information.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_info\n\n# Get pathway database info\ninfo = kegg_info('pathway')\n\n# Get organism-specific info\nhsa_info = kegg_info('hsa')  # Human genome\n```\n\n**Common databases**: `kegg`, `pathway`, `module`, `brite`, `genes`, `genome`, `compound`, `glycan`, `reaction`, `enzyme`, `disease`, `drug`\n\n### 2. Listing Entries (`kegg_list`)\n\nList entry identifiers and names from KEGG databases.\n\n**When to use**: Getting all pathways for an organism, listing genes, retrieving compound catalogs.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_list\n\n# List all reference pathways\npathways = kegg_list('pathway')\n\n# List human-specific pathways\nhsa_pathways = kegg_list('pathway', 'hsa')\n\n# List specific genes (max 10)\ngenes = kegg_list('hsa:10458+hsa:10459')\n```\n\n**Common organism codes**: `hsa` (human), `mmu` (mouse), `dme` (fruit fly), `sce` (yeast), `eco` (E. coli)\n\n### 3. Searching (`kegg_find`)\n\nSearch KEGG databases by keywords or molecular properties.\n\n**When to use**: Finding genes by name\u002Fdescription, searching compounds by formula or mass, discovering entries by keywords.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_find\n\n# Keyword search\nresults = kegg_find('genes', 'p53')\nshiga_toxin = kegg_find('genes', 'shiga toxin')\n\n# Chemical formula search (exact match)\ncompounds = kegg_find('compound', 'C7H10N4O2', 'formula')\n\n# Molecular weight range search\ndrugs = kegg_find('drug', '300-310', 'exact_mass')\n```\n\n**Search options**: `formula` (exact match), `exact_mass` (range), `mol_weight` (range)\n\n### 4. Retrieving Entries (`kegg_get`)\n\nGet complete database entries or specific data formats.\n\n**When to use**: Retrieving pathway details, getting gene\u002Fprotein sequences, downloading pathway maps, accessing compound structures.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_get\n\n# Get pathway entry\npathway = kegg_get('hsa00010')  # Glycolysis pathway\n\n# Get multiple entries (max 10)\ngenes = kegg_get(['hsa:10458', 'hsa:10459'])\n\n# Get protein sequence (FASTA)\nsequence = kegg_get('hsa:10458', 'aaseq')\n\n# Get nucleotide sequence\nnt_seq = kegg_get('hsa:10458', 'ntseq')\n\n# Get compound structure\nmol_file = kegg_get('cpd:C00002', 'mol')  # ATP in MOL format\n\n# Get pathway as JSON (single entry only)\npathway_json = kegg_get('hsa05130', 'json')\n\n# Get pathway image (single entry only)\npathway_img = kegg_get('hsa05130', 'image')\n```\n\n**Output formats**: `aaseq` (protein FASTA), `ntseq` (nucleotide FASTA), `mol` (MOL format), `kcf` (KCF format), `image` (PNG), `kgml` (XML), `json` (pathway JSON)\n\n**Important**: Image, KGML, and JSON formats allow only one entry at a time.\n\n### 5. ID Conversion (`kegg_conv`)\n\nConvert identifiers between KEGG and external databases.\n\n**When to use**: Integrating KEGG data with other databases, mapping gene IDs, converting compound identifiers.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_conv\n\n# Convert all human genes to NCBI Gene IDs\nconversions = kegg_conv('ncbi-geneid', 'hsa')\n\n# Convert specific gene\ngene_id = kegg_conv('ncbi-geneid', 'hsa:10458')\n\n# Convert to UniProt\nuniprot_id = kegg_conv('uniprot', 'hsa:10458')\n\n# Convert compounds to PubChem\npubchem_ids = kegg_conv('pubchem', 'compound')\n\n# Reverse conversion (NCBI Gene ID to KEGG)\nkegg_id = kegg_conv('hsa', 'ncbi-geneid')\n```\n\n**Supported conversions**: `ncbi-geneid`, `ncbi-proteinid`, `uniprot`, `pubchem`, `chebi`\n\n### 6. Cross-Referencing (`kegg_link`)\n\nFind related entries within and between KEGG databases.\n\n**When to use**: Finding pathways containing genes, getting genes in a pathway, mapping genes to KO groups, finding compounds in pathways.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_link\n\n# Find pathways linked to human genes\npathways = kegg_link('pathway', 'hsa')\n\n# Get genes in a specific pathway\ngenes = kegg_link('genes', 'hsa00010')  # Glycolysis genes\n\n# Find pathways containing a specific gene\ngene_pathways = kegg_link('pathway', 'hsa:10458')\n\n# Find compounds in a pathway\ncompounds = kegg_link('compound', 'hsa00010')\n\n# Map genes to KO (orthology) groups\nko_groups = kegg_link('ko', 'hsa:10458')\n```\n\n**Common links**: genes ↔ pathway, pathway ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology)\n\n### 7. Drug-Drug Interactions (`kegg_ddi`)\n\nCheck for drug-drug interactions.\n\n**When to use**: Analyzing drug combinations, checking for contraindications, pharmacological research.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_ddi\n\n# Check single drug\ninteractions = kegg_ddi('D00001')\n\n# Check multiple drugs (max 10)\ninteractions = kegg_ddi(['D00001', 'D00002', 'D00003'])\n```\n\n## Common Analysis Workflows\n\n### Workflow 1: Gene to Pathway Mapping\n\n**Use case**: Finding pathways associated with genes of interest (e.g., for pathway enrichment analysis).\n\n```python\nfrom scripts.kegg_api import kegg_find, kegg_link, kegg_get\n\n# Step 1: Find gene ID by name\ngene_results = kegg_find('genes', 'p53')\n\n# Step 2: Link gene to pathways\npathways = kegg_link('pathway', 'hsa:7157')  # TP53 gene\n\n# Step 3: Get detailed pathway information\nfor pathway_line in pathways.split('\\n'):\n    if pathway_line:\n        pathway_id = pathway_line.split('\\t')[1].replace('path:', '')\n        pathway_info = kegg_get(pathway_id)\n        # Process pathway information\n```\n\n### Workflow 2: Pathway Enrichment Context\n\n**Use case**: Getting all genes in organism pathways for enrichment analysis.\n\n```python\nfrom scripts.kegg_api import kegg_list, kegg_link\n\n# Step 1: List all human pathways\npathways = kegg_list('pathway', 'hsa')\n\n# Step 2: For each pathway, get associated genes\nfor pathway_line in pathways.split('\\n'):\n    if pathway_line:\n        pathway_id = pathway_line.split('\\t')[0]\n        genes = kegg_link('genes', pathway_id)\n        # Process genes for enrichment analysis\n```\n\n### Workflow 3: Compound to Pathway Analysis\n\n**Use case**: Finding metabolic pathways containing compounds of interest.\n\n```python\nfrom scripts.kegg_api import kegg_find, kegg_link, kegg_get\n\n# Step 1: Search for compound\ncompound_results = kegg_find('compound', 'glucose')\n\n# Step 2: Link compound to reactions\nreactions = kegg_link('reaction', 'cpd:C00031')  # Glucose\n\n# Step 3: Link reactions to pathways\npathways = kegg_link('pathway', 'rn:R00299')  # Specific reaction\n\n# Step 4: Get pathway details\npathway_info = kegg_get('map00010')  # Glycolysis\n```\n\n### Workflow 4: Cross-Database Integration\n\n**Use case**: Integrating KEGG data with UniProt, NCBI, or PubChem databases.\n\n```python\nfrom scripts.kegg_api import kegg_conv, kegg_get\n\n# Step 1: Convert KEGG gene IDs to external database IDs\nuniprot_map = kegg_conv('uniprot', 'hsa')\nncbi_map = kegg_conv('ncbi-geneid', 'hsa')\n\n# Step 2: Parse conversion results\nfor line in uniprot_map.split('\\n'):\n    if line:\n        kegg_id, uniprot_id = line.split('\\t')\n        # Use external IDs for integration\n\n# Step 3: Get sequences using KEGG\nsequence = kegg_get('hsa:10458', 'aaseq')\n```\n\n### Workflow 5: Organism-Specific Pathway Analysis\n\n**Use case**: Comparing pathways across different organisms.\n\n```python\nfrom scripts.kegg_api import kegg_list, kegg_get\n\n# Step 1: List pathways for multiple organisms\nhuman_pathways = kegg_list('pathway', 'hsa')\nmouse_pathways = kegg_list('pathway', 'mmu')\nyeast_pathways = kegg_list('pathway', 'sce')\n\n# Step 2: Get reference pathway for comparison\nref_pathway = kegg_get('map00010')  # Reference glycolysis\n\n# Step 3: Get organism-specific versions\nhsa_glycolysis = kegg_get('hsa00010')\nmmu_glycolysis = kegg_get('mmu00010')\n```\n\n## Pathway Categories\n\nKEGG organizes pathways into seven major categories. When interpreting pathway IDs or recommending pathways to users:\n\n1. **Metabolism** (e.g., `map00010` - Glycolysis, `map00190` - Oxidative phosphorylation)\n2. **Genetic Information Processing** (e.g., `map03010` - Ribosome, `map03040` - Spliceosome)\n3. **Environmental Information Processing** (e.g., `map04010` - MAPK signaling, `map02010` - ABC transporters)\n4. **Cellular Processes** (e.g., `map04140` - Autophagy, `map04210` - Apoptosis)\n5. **Organismal Systems** (e.g., `map04610` - Complement cascade, `map04910` - Insulin signaling)\n6. **Human Diseases** (e.g., `map05200` - Pathways in cancer, `map05010` - Alzheimer disease)\n7. **Drug Development** (chronological and target-based classifications)\n\nReference `references\u002Fkegg_reference.md` for detailed pathway lists and classifications.\n\n## Important Identifiers and Formats\n\n### Pathway IDs\n- `map#####` - Reference pathway (generic, not organism-specific)\n- `hsa#####` - Human pathway\n- `mmu#####` - Mouse pathway\n\n### Gene IDs\n- Format: `organism:gene_number` (e.g., `hsa:10458`)\n\n### Compound IDs\n- Format: `cpd:C#####` (e.g., `cpd:C00002` for ATP)\n\n### Drug IDs\n- Format: `dr:D#####` (e.g., `dr:D00001`)\n\n### Enzyme IDs\n- Format: `ec:EC_number` (e.g., `ec:1.1.1.1`)\n\n### KO (KEGG Orthology) IDs\n- Format: `ko:K#####` (e.g., `ko:K00001`)\n\n## API Limitations\n\nRespect these constraints when using the KEGG API:\n\n1. **Entry limits**: Maximum 10 entries per operation (except image\u002Fkgml\u002Fjson: 1 entry only)\n2. **Academic use**: API is for academic use only; commercial use requires licensing\n3. **HTTP status codes**: Check for 200 (success), 400 (bad request), 404 (not found)\n4. **Rate limiting**: No explicit limit, but avoid rapid-fire requests\n\n## Detailed Reference\n\nFor comprehensive API documentation, database specifications, organism codes, and advanced usage, refer to `references\u002Fkegg_reference.md`. This includes:\n\n- Complete list of KEGG databases\n- Detailed API operation syntax\n- All organism codes\n- HTTP status codes and error handling\n- Integration with Biopython and R\u002FBioconductor\n- Best practices for API usage\n\n## Troubleshooting\n\n**404 Not Found**: Entry or database doesn't exist; verify IDs and organism codes\n**400 Bad Request**: Syntax error in API call; check parameter formatting\n**Empty results**: Search term may not match entries; try broader keywords\n**Image\u002FKGML errors**: These formats only work with single entries; remove batch processing\n\n## Additional Tools\n\nFor interactive pathway visualization and annotation:\n- **KEGG Mapper**: https:\u002F\u002Fwww.kegg.jp\u002Fkegg\u002Fmapper\u002F\n- **BlastKOALA**: Automated genome annotation\n- **GhostKOALA**: Metagenome\u002Fmetatranscriptome annotation\n\n","# KEGG Database\n\n## Overview\n\nKEGG (Kyoto Encyclopedia of Genes and Genomes) is a comprehensive bioinformatics resource for biological pathway analysis and molecular interaction networks.\n\n**Important**: KEGG API is made available only for academic use by academic users.\n\n## When to Use This Skill\n\nThis skill should be used when querying pathways, genes, compounds, enzymes, diseases, and drugs across multiple organisms using KEGG's REST API.\n\n## Quick Start\n\nThe skill provides:\n1. Python helper functions (`scripts\u002Fkegg_api.py`) for all KEGG REST API operations\n2. Comprehensive reference documentation (`references\u002Fkegg_reference.md`) with detailed API specifications\n\nWhen users request KEGG data, determine which operation is needed and use the appropriate function from `scripts\u002Fkegg_api.py`.\n\n## Core Operations\n\n### 1. Database Information (`kegg_info`)\n\nRetrieve metadata and statistics about KEGG databases.\n\n**When to use**: Understanding database structure, checking available data, getting release information.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_info\n\n# Get pathway database info\ninfo = kegg_info('pathway')\n\n# Get organism-specific info\nhsa_info = kegg_info('hsa')  # Human genome\n```\n\n**Common databases**: `kegg`, `pathway`, `module`, `brite`, `genes`, `genome`, `compound`, `glycan`, `reaction`, `enzyme`, `disease`, `drug`\n\n### 2. Listing Entries (`kegg_list`)\n\nList entry identifiers and names from KEGG databases.\n\n**When to use**: Getting all pathways for an organism, listing genes, retrieving compound catalogs.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_list\n\n# List all reference pathways\npathways = kegg_list('pathway')\n\n# List human-specific pathways\nhsa_pathways = kegg_list('pathway', 'hsa')\n\n# List specific genes (max 10)\ngenes = kegg_list('hsa:10458+hsa:10459')\n```\n\n**Common organism codes**: `hsa` (human), `mmu` (mouse), `dme` (fruit fly), `sce` (yeast), `eco` (E. coli)\n\n### 3. Searching (`kegg_find`)\n\nSearch KEGG databases by keywords or molecular properties.\n\n**When to use**: Finding genes by name\u002Fdescription, searching compounds by formula or mass, discovering entries by keywords.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_find\n\n# Keyword search\nresults = kegg_find('genes', 'p53')\nshiga_toxin = kegg_find('genes', 'shiga toxin')\n\n# Chemical formula search (exact match)\ncompounds = kegg_find('compound', 'C7H10N4O2', 'formula')\n\n# Molecular weight range search\ndrugs = kegg_find('drug', '300-310', 'exact_mass')\n```\n\n**Search options**: `formula` (exact match), `exact_mass` (range), `mol_weight` (range)\n\n### 4. Retrieving Entries (`kegg_get`)\n\nGet complete database entries or specific data formats.\n\n**When to use**: Retrieving pathway details, getting gene\u002Fprotein sequences, downloading pathway maps, accessing compound structures.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_get\n\n# Get pathway entry\npathway = kegg_get('hsa00010')  # Glycolysis pathway\n\n# Get multiple entries (max 10)\ngenes = kegg_get(['hsa:10458', 'hsa:10459'])\n\n# Get protein sequence (FASTA)\nsequence = kegg_get('hsa:10458', 'aaseq')\n\n# Get nucleotide sequence\nnt_seq = kegg_get('hsa:10458', 'ntseq')\n\n# Get compound structure\nmol_file = kegg_get('cpd:C00002', 'mol')  # ATP in MOL format\n\n# Get pathway as JSON (single entry only)\npathway_json = kegg_get('hsa05130', 'json')\n\n# Get pathway image (single entry only)\npathway_img = kegg_get('hsa05130', 'image')\n```\n\n**Output formats**: `aaseq` (protein FASTA), `ntseq` (nucleotide FASTA), `mol` (MOL format), `kcf` (KCF format), `image` (PNG), `kgml` (XML), `json` (pathway JSON)\n\n**Important**: Image, KGML, and JSON formats allow only one entry at a time.\n\n### 5. ID Conversion (`kegg_conv`)\n\nConvert identifiers between KEGG and external databases.\n\n**When to use**: Integrating KEGG data with other databases, mapping gene IDs, converting compound identifiers.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_conv\n\n# Convert all human genes to NCBI Gene IDs\nconversions = kegg_conv('ncbi-geneid', 'hsa')\n\n# Convert specific gene\ngene_id = kegg_conv('ncbi-geneid', 'hsa:10458')\n\n# Convert to UniProt\nuniprot_id = kegg_conv('uniprot', 'hsa:10458')\n\n# Convert compounds to PubChem\npubchem_ids = kegg_conv('pubchem', 'compound')\n\n# Reverse conversion (NCBI Gene ID to KEGG)\nkegg_id = kegg_conv('hsa', 'ncbi-geneid')\n```\n\n**Supported conversions**: `ncbi-geneid`, `ncbi-proteinid`, `uniprot`, `pubchem`, `chebi`\n\n### 6. Cross-Referencing (`kegg_link`)\n\nFind related entries within and between KEGG databases.\n\n**When to use**: Finding pathways containing genes, getting genes in a pathway, mapping genes to KO groups, finding compounds in pathways.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_link\n\n# Find pathways linked to human genes\npathways = kegg_link('pathway', 'hsa')\n\n# Get genes in a specific pathway\ngenes = kegg_link('genes', 'hsa00010')  # Glycolysis genes\n\n# Find pathways containing a specific gene\ngene_pathways = kegg_link('pathway', 'hsa:10458')\n\n# Find compounds in a pathway\ncompounds = kegg_link('compound', 'hsa00010')\n\n# Map genes to KO (orthology) groups\nko_groups = kegg_link('ko', 'hsa:10458')\n```\n\n**Common links**: genes ↔ pathway, pathway ↔ compound, pathway ↔ enzyme, genes ↔ ko (orthology)\n\n### 7. Drug-Drug Interactions (`kegg_ddi`)\n\nCheck for drug-drug interactions.\n\n**When to use**: Analyzing drug combinations, checking for contraindications, pharmacological research.\n\n**Usage**:\n```python\nfrom scripts.kegg_api import kegg_ddi\n\n# Check single drug\ninteractions = kegg_ddi('D00001')\n\n# Check multiple drugs (max 10)\ninteractions = kegg_ddi(['D00001', 'D00002', 'D00003'])\n```\n\n## Common Analysis Workflows\n\n### Workflow 1: Gene to Pathway Mapping\n\n**Use case**: Finding pathways associated with genes of interest (e.g., for pathway enrichment analysis).\n\n```python\nfrom scripts.kegg_api import kegg_find, kegg_link, kegg_get\n\n# Step 1: Find gene ID by name\ngene_results = kegg_find('genes', 'p53')\n\n# Step 2: Link gene to pathways\npathways = kegg_link('pathway', 'hsa:7157')  # TP53 gene\n\n# Step 3: Get detailed pathway information\nfor pathway_line in pathways.split('\\n'):\n    if pathway_line:\n        pathway_id = pathway_line.split('\\t')[1].replace('path:', '')\n        pathway_info = kegg_get(pathway_id)\n        # Process pathway information\n```\n\n### Workflow 2: Pathway Enrichment Context\n\n**Use case**: Getting all genes in organism pathways for enrichment analysis.\n\n```python\nfrom scripts.kegg_api import kegg_list, kegg_link\n\n# Step 1: List all human pathways\npathways = kegg_list('pathway', 'hsa')\n\n# Step 2: For each pathway, get associated genes\nfor pathway_line in pathways.split('\\n'):\n    if pathway_line:\n        pathway_id = pathway_line.split('\\t')[0]\n        genes = kegg_link('genes', pathway_id)\n        # Process genes for enrichment analysis\n```\n\n### Workflow 3: Compound to Pathway Analysis\n\n**Use case**: Finding metabolic pathways containing compounds of interest.\n\n```python\nfrom scripts.kegg_api import kegg_find, kegg_link, kegg_get\n\n# Step 1: Search for compound\ncompound_results = kegg_find('compound', 'glucose')\n\n# Step 2: Link compound to reactions\nreactions = kegg_link('reaction', 'cpd:C00031')  # Glucose\n\n# Step 3: Link reactions to pathways\npathways = kegg_link('pathway', 'rn:R00299')  # Specific reaction\n\n# Step 4: Get pathway details\npathway_info = kegg_get('map00010')  # Glycolysis\n```\n\n### Workflow 4: Cross-Database Integration\n\n**Use case**: Integrating KEGG data with UniProt, NCBI, or PubChem databases.\n\n```python\nfrom scripts.kegg_api import kegg_conv, kegg_get\n\n# Step 1: Convert KEGG gene IDs to external database IDs\nuniprot_map = kegg_conv('uniprot', 'hsa')\nncbi_map = kegg_conv('ncbi-geneid', 'hsa')\n\n# Step 2: Parse conversion results\nfor line in uniprot_map.split('\\n'):\n    if line:\n        kegg_id, uniprot_id = line.split('\\t')\n        # Use external IDs for integration\n\n# Step 3: Get sequences using KEGG\nsequence = kegg_get('hsa:10458', 'aaseq')\n```\n\n### Workflow 5: Organism-Specific Pathway Analysis\n\n**Use case**: Comparing pathways across different organisms.\n\n```python\nfrom scripts.kegg_api import kegg_list, kegg_get\n\n# Step 1: List pathways for multiple organisms\nhuman_pathways = kegg_list('pathway', 'hsa')\nmouse_pathways = kegg_list('pathway', 'mmu')\nyeast_pathways = kegg_list('pathway', 'sce')\n\n# Step 2: Get reference pathway for comparison\nref_pathway = kegg_get('map00010')  # Reference glycolysis\n\n# Step 3: Get organism-specific versions\nhsa_glycolysis = kegg_get('hsa00010')\nmmu_glycolysis = kegg_get('mmu00010')\n```\n\n## Pathway Categories\n\nKEGG organizes pathways into seven major categories. When interpreting pathway IDs or recommending pathways to users:\n\n1. **Metabolism** (e.g., `map00010` - Glycolysis, `map00190` - Oxidative phosphorylation)\n2. **Genetic Information Processing** (e.g., `map03010` - Ribosome, `map03040` - Spliceosome)\n3. **Environmental Information Processing** (e.g., `map04010` - MAPK signaling, `map02010` - ABC transporters)\n4. **Cellular Processes** (e.g., `map04140` - Autophagy, `map04210` - Apoptosis)\n5. **Organismal Systems** (e.g., `map04610` - Complement cascade, `map04910` - Insulin signaling)\n6. **Human Diseases** (e.g., `map05200` - Pathways in cancer, `map05010` - Alzheimer disease)\n7. **Drug Development** (chronological and target-based classifications)\n\nReference `references\u002Fkegg_reference.md` for detailed pathway lists and classifications.\n\n## Important Identifiers and Formats\n\n### Pathway IDs\n- `map#####` - Reference pathway (generic, not organism-specific)\n- `hsa#####` - Human pathway\n- `mmu#####` - Mouse pathway\n\n### Gene IDs\n- Format: `organism:gene_number` (e.g., `hsa:10458`)\n\n### Compound IDs\n- Format: `cpd:C#####` (e.g., `cpd:C00002` for ATP)\n\n### Drug IDs\n- Format: `dr:D#####` (e.g., `dr:D00001`)\n\n### Enzyme IDs\n- Format: `ec:EC_number` (e.g., `ec:1.1.1.1`)\n\n### KO (KEGG Orthology) IDs\n- Format: `ko:K#####` (e.g., `ko:K00001`)\n\n## API Limitations\n\nRespect these constraints when using the KEGG API:\n\n1. **Entry limits**: Maximum 10 entries per operation (except image\u002Fkgml\u002Fjson: 1 entry only)\n2. **Academic use**: API is for academic use only; commercial use requires licensing\n3. **HTTP status codes**: Check for 200 (success), 400 (bad request), 404 (not found)\n4. **Rate limiting**: No explicit limit, but avoid rapid-fire requests\n\n## Detailed Reference\n\nFor comprehensive API documentation, database specifications, organism codes, and advanced usage, refer to `references\u002Fkegg_reference.md`. This includes:\n\n- Complete list of KEGG databases\n- Detailed API operation syntax\n- All organism codes\n- HTTP status codes and error handling\n- Integration with Biopython and R\u002FBioconductor\n- Best practices for API usage\n\n## Troubleshooting\n\n**404 Not Found**: Entry or database doesn't exist; verify IDs and organism codes\n**400 Bad Request**: Syntax error in API call; check parameter formatting\n**Empty results**: Search term may not match entries; try broader keywords\n**Image\u002FKGML errors**: These formats only work with single entries; remove batch processing\n\n## Additional Tools\n\nFor interactive pathway visualization and annotation:\n- **KEGG Mapper**: https:\u002F\u002Fwww.kegg.jp\u002Fkegg\u002Fmapper\u002F\n- **BlastKOALA**: Automated genome annotation\n- **GhostKOALA**: Metagenome\u002Fmetatranscriptome annotation\n\n",{"name":5,"description":7},"main","015422534a7457348a313b6381f0255d3364eb8c","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fkegg-database",0,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates","https:\u002F\u002Fgithub.com\u002FMishit18","2026-11-10 18:35:05",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add Mishit18\u002Fclaude-code-templates --skill kegg-database",{"tool":62,"command":63},"bunx","bunx skills add Mishit18\u002Fclaude-code-templates --skill kegg-database",{"tool":65,"command":66},"pnpm","pnpm dlx skills add Mishit18\u002Fclaude-code-templates --skill kegg-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fkegg-database\u002FSKILL.md",true,[70,76],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fkegg_reference.md","REFERENCE","参考文档",10750,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fkegg-database\u002Freferences\u002Fkegg_reference.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"scripts\u002Fkegg_api.py","SCRIPT","脚本",7698,"https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fkegg-database\u002Fscripts\u002Fkegg_api.py",[83,113,139,166,192,217],{"skillNo":84,"name":85,"nameZh":6,"description":86,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":88,"sourceHtmlUrl":89,"categories":90,"keywords":94,"publishedAt":44},"01M6R4ARN4W2VZRDERDTMN1KP0","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). 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Use this skill whenever the user is about to plan a system, make a technology choice, evaluate trade-offs, decide on an approach, or faces any decision where multiple perspectives would sharpen the outcome. Also trigger when the user says \"think tank\", \"debate this\", \"perspectives on\", \"trade-offs\", \"should I use X or Y\", \"help me decide\", \"before we plan\", or asks for pros\u002Fcons of competing approaches. This skill should run BEFORE any implementation planning begins — it produces a structured analysis that feeds into better plans.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fthink-tank\u002FSKILL.md",[],[147,148,149,150,151,41,152,153,154,155,156,157,158,159,160,161,162,163,164,165],"think tank","think","design","run","feeds","persona","plan","sharpen","virtual","decision","choice","decide","perspectives","outcome","me","debate","make","trade-offs","when",{"skillNo":167,"name":168,"nameZh":6,"description":169,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":170,"sourceHtmlUrl":171,"categories":172,"keywords":176,"publishedAt":44},"01M6R2FSR55AVRFA9YZ8SC2GZG","skill-creator","Guide for creating effective skills. This skill should be used when users want to create a new skill (or update an existing skill) that extends Claude's capabilities with specialized knowledge, workflows, or tool integrations.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fskill-creator\u002FSKILL.md",[173],{"categoryNo":174,"categoryName":175,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[177,178,43,179,180,181,182,183,184,185,186,187,188,189,138,29,190,165,191],"skill creator","creator","create","update","capabilities","knowledge","tool","integrations","specialized","guide","new skill","existing skill","users","Existing","new",{"skillNo":193,"name":194,"nameZh":6,"description":195,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":196,"categories":197,"keywords":198,"publishedAt":44},"01M6R2FK42ZZ4HJTJVVK9WJBBP","ship-learn-next","Transform learning content (like YouTube transcripts, articles, tutorials) into actionable implementation plans using the Ship-Learn-Next framework. Use when user wants to turn advice, lessons, or educational content into concrete action steps, reps, or a learning quest.","https:\u002F\u002Fgithub.com\u002FMishit18\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fship-learn-next\u002FSKILL.md",[],[199,200,201,202,203,204,205,206,27,207,208,209,210,211,212,213,214,215,216,165],"learn","ship learn next","ship","next","framework","learning","read","write","advice","implementation","youtube","plans","quest","transform","actionable","action","lessons","youtube transcripts",{"skillNo":218,"name":219,"nameZh":6,"description":220,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":221,"sourceHtmlUrl":222,"categories":223,"keywords":230,"publishedAt":44},"01M6R2088G1RHDA5MDX16HASRB","slack-gif-creator","Knowledge and utilities for creating animated GIFs optimized for Slack. 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