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Query metabolites, RefMet nomenclature, MS\u002FNMR data, m\u002Fz searches, study metadata, for metabolomics and biomarker discovery.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33481,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmetabolomics-workbench-database\u002FSKILL.md",[18,23],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口","SCENARIO","使用场景",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"metabolomics workbench database","metabolomics","database","ms","for","via","biomarker","biomarker discovery","rest api","query","study","access","discovery","api","REST","data","metadata","2026-11-10 10:12:30",false,"---\nname: metabolomics-workbench-database\ndescription: \"Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS\u002FNMR data, m\u002Fz searches, study metadata, for metabolomics and biomarker discovery.\"\n---\n\n# Metabolomics Workbench Database\n\n## Overview\n\nThe Metabolomics Workbench is a comprehensive NIH Common Fund-sponsored platform hosted at UCSD that serves as the primary repository for metabolomics research data. It provides programmatic access to over 4,200 processed studies (3,790+ publicly available), standardized metabolite nomenclature through RefMet, and powerful search capabilities across multiple analytical platforms (GC-MS, LC-MS, NMR).\n\n## When to Use This Skill\n\nThis skill should be used when querying metabolite structures, accessing study data, standardizing nomenclature, performing mass spectrometry searches, or retrieving gene\u002Fprotein-metabolite associations through the Metabolomics Workbench REST API.\n\n## Core Capabilities\n\n### 1. Querying Metabolite Structures and Data\n\nAccess comprehensive metabolite information including structures, identifiers, and cross-references to external databases.\n\n**Key operations:**\n- Retrieve compound data by various identifiers (PubChem CID, InChI Key, KEGG ID, HMDB ID, etc.)\n- Download molecular structures as MOL files or PNG images\n- Access standardized compound classifications\n- Cross-reference between different metabolite databases\n\n**Example queries:**\n```python\nimport requests\n\n# Get compound information by PubChem CID\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fpubchem_cid\u002F5281365\u002Fall\u002Fjson')\n\n# Download molecular structure as PNG\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F11\u002Fpng')\n\n# Get compound name by registry number\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F11\u002Fname\u002Fjson')\n```\n\n### 2. Accessing Study Metadata and Experimental Results\n\nQuery metabolomics studies by various criteria and retrieve complete experimental datasets.\n\n**Key operations:**\n- Search studies by metabolite, institute, investigator, or title\n- Access study summaries, experimental factors, and analysis details\n- Retrieve complete experimental data in various formats\n- Download mwTab format files for complete study information\n- Query untargeted metabolomics data\n\n**Example queries:**\n```python\n# List all available public studies\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST\u002Favailable\u002Fjson')\n\n# Get study summary\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST000001\u002Fsummary\u002Fjson')\n\n# Retrieve experimental data\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST000001\u002Fdata\u002Fjson')\n\n# Find studies containing a specific metabolite\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Frefmet_name\u002FTyrosine\u002Fsummary\u002Fjson')\n```\n\n### 3. Standardizing Metabolite Nomenclature with RefMet\n\nUse the RefMet database to standardize metabolite names and access systematic classification across four structural resolution levels.\n\n**Key operations:**\n- Match common metabolite names to standardized RefMet names\n- Query by chemical formula, exact mass, or InChI Key\n- Access hierarchical classification (super class, main class, sub class)\n- Retrieve all RefMet entries or filter by classification\n\n**Example queries:**\n```python\n# Standardize a metabolite name\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fmatch\u002Fcitrate\u002Fname\u002Fjson')\n\n# Query by molecular formula\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fformula\u002FC12H24O2\u002Fall\u002Fjson')\n\n# Get all metabolites in a specific class\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fmain_class\u002FFatty%20Acids\u002Fall\u002Fjson')\n\n# Retrieve complete RefMet database\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fall\u002Fjson')\n```\n\n### 4. Performing Mass Spectrometry Searches\n\nSearch for compounds by mass-to-charge ratio (m\u002Fz) with specified ion adducts and tolerance levels.\n\n**Key operations:**\n- Search precursor ion masses across multiple databases (Metabolomics Workbench, LIPIDS, RefMet)\n- Specify ion adduct types (M+H, M-H, M+Na, M+NH4, M+2H, etc.)\n- Calculate exact masses for known metabolites with specific adducts\n- Set mass tolerance for flexible matching\n\n**Example queries:**\n```python\n# Search by m\u002Fz value with M+H adduct\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002FMB\u002F635.52\u002FM+H\u002F0.5\u002Fjson')\n\n# Calculate exact mass for a metabolite with specific adduct\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002Fexactmass\u002FPC(34:1)\u002FM+H\u002Fjson')\n\n# Search across RefMet database\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002FREFMET\u002F200.15\u002FM-H\u002F0.3\u002Fjson')\n```\n\n### 5. Filtering Studies by Analytical and Biological Parameters\n\nUse the MetStat context to find studies matching specific experimental conditions.\n\n**Key operations:**\n- Filter by analytical method (LCMS, GCMS, NMR)\n- Specify ionization polarity (POSITIVE, NEGATIVE)\n- Filter by chromatography type (HILIC, RP, GC)\n- Target specific species, sample sources, or diseases\n- Combine multiple filters using semicolon-delimited format\n\n**Example queries:**\n```python\n# Find human blood studies on diabetes using LC-MS\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002FLCMS;POSITIVE;HILIC;Human;Blood;Diabetes\u002Fjson')\n\n# Find all human blood studies containing tyrosine\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002F;;;Human;Blood;;;Tyrosine\u002Fjson')\n\n# Filter by analytical method only\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002FGCMS;;;;;;\u002Fjson')\n```\n\n### 6. Accessing Gene and Protein Information\n\nRetrieve gene and protein data associated with metabolic pathways and metabolite metabolism.\n\n**Key operations:**\n- Query genes by symbol, name, or ID\n- Access protein sequences and annotations\n- Cross-reference between gene IDs, RefSeq IDs, and UniProt IDs\n- Retrieve gene-metabolite associations\n\n**Example queries:**\n```python\n# Get gene information by symbol\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fgene\u002Fgene_symbol\u002FACACA\u002Fall\u002Fjson')\n\n# Retrieve protein data by UniProt ID\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fprotein\u002Funiprot_id\u002FQ13085\u002Fall\u002Fjson')\n```\n\n## Common Workflows\n\n### Workflow 1: Finding Studies for a Specific Metabolite\n\nTo find all studies containing measurements of a specific metabolite:\n\n1. First standardize the metabolite name using RefMet:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fmatch\u002Fglucose\u002Fname\u002Fjson')\n   ```\n\n2. Use the standardized name to search for studies:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Frefmet_name\u002FGlucose\u002Fsummary\u002Fjson')\n   ```\n\n3. Retrieve experimental data from specific studies:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST000001\u002Fdata\u002Fjson')\n   ```\n\n### Workflow 2: Identifying Compounds from MS Data\n\nTo identify potential compounds from mass spectrometry m\u002Fz values:\n\n1. Perform m\u002Fz search with appropriate adduct and tolerance:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002FMB\u002F180.06\u002FM+H\u002F0.5\u002Fjson')\n   ```\n\n2. Review candidate compounds from results\n\n3. Retrieve detailed information for candidate compounds:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F{regno}\u002Fall\u002Fjson')\n   ```\n\n4. Download structures for confirmation:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F{regno}\u002Fpng')\n   ```\n\n### Workflow 3: Exploring Disease-Specific Metabolomics\n\nTo find metabolomics studies for a specific disease and analytical platform:\n\n1. Use MetStat to filter studies:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002FLCMS;POSITIVE;;Human;;Cancer\u002Fjson')\n   ```\n\n2. Review study IDs from results\n\n3. Access detailed study information:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST{ID}\u002Fsummary\u002Fjson')\n   ```\n\n4. Retrieve complete experimental data:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST{ID}\u002Fdata\u002Fjson')\n   ```\n\n## Output Formats\n\nThe API supports two primary output formats:\n- **JSON** (default): Machine-readable format, ideal for programmatic access\n- **TXT**: Human-readable tab-delimited text format\n\nSpecify format by appending `\u002Fjson` or `\u002Ftxt` to API URLs. When format is omitted, JSON is returned by default.\n\n## Best Practices\n\n1. **Use RefMet for standardization**: Always standardize metabolite names through RefMet before searching studies to ensure consistent nomenclature\n\n2. **Specify appropriate adducts**: When performing m\u002Fz searches, use the correct ion adduct type for your analytical method (e.g., M+H for positive mode ESI)\n\n3. **Set reasonable tolerances**: Use appropriate mass tolerance values (typically 0.5 Da for low-resolution, 0.01 Da for high-resolution MS)\n\n4. **Cache reference data**: Consider caching frequently used reference data (RefMet database, compound information) to minimize API calls\n\n5. **Handle pagination**: For large result sets, be prepared to handle multiple data structures in responses\n\n6. **Validate identifiers**: Cross-reference metabolite identifiers across multiple databases when possible to ensure correct compound identification\n\n## Resources\n\n### references\u002F\n\nDetailed API reference documentation is available in `references\u002Fapi_reference.md`, including:\n- Complete REST API endpoint specifications\n- All available contexts (compound, study, refmet, metstat, gene, protein, moverz)\n- Input\u002Foutput parameter details\n- Ion adduct types for mass spectrometry\n- Additional query examples\n\nLoad this reference file when detailed API specifications are needed or when working with less common endpoints.\n","# Metabolomics Workbench Database\n\n## Overview\n\nThe Metabolomics Workbench is a comprehensive NIH Common Fund-sponsored platform hosted at UCSD that serves as the primary repository for metabolomics research data. It provides programmatic access to over 4,200 processed studies (3,790+ publicly available), standardized metabolite nomenclature through RefMet, and powerful search capabilities across multiple analytical platforms (GC-MS, LC-MS, NMR).\n\n## When to Use This Skill\n\nThis skill should be used when querying metabolite structures, accessing study data, standardizing nomenclature, performing mass spectrometry searches, or retrieving gene\u002Fprotein-metabolite associations through the Metabolomics Workbench REST API.\n\n## Core Capabilities\n\n### 1. Querying Metabolite Structures and Data\n\nAccess comprehensive metabolite information including structures, identifiers, and cross-references to external databases.\n\n**Key operations:**\n- Retrieve compound data by various identifiers (PubChem CID, InChI Key, KEGG ID, HMDB ID, etc.)\n- Download molecular structures as MOL files or PNG images\n- Access standardized compound classifications\n- Cross-reference between different metabolite databases\n\n**Example queries:**\n```python\nimport requests\n\n# Get compound information by PubChem CID\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fpubchem_cid\u002F5281365\u002Fall\u002Fjson')\n\n# Download molecular structure as PNG\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F11\u002Fpng')\n\n# Get compound name by registry number\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F11\u002Fname\u002Fjson')\n```\n\n### 2. Accessing Study Metadata and Experimental Results\n\nQuery metabolomics studies by various criteria and retrieve complete experimental datasets.\n\n**Key operations:**\n- Search studies by metabolite, institute, investigator, or title\n- Access study summaries, experimental factors, and analysis details\n- Retrieve complete experimental data in various formats\n- Download mwTab format files for complete study information\n- Query untargeted metabolomics data\n\n**Example queries:**\n```python\n# List all available public studies\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST\u002Favailable\u002Fjson')\n\n# Get study summary\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST000001\u002Fsummary\u002Fjson')\n\n# Retrieve experimental data\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST000001\u002Fdata\u002Fjson')\n\n# Find studies containing a specific metabolite\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Frefmet_name\u002FTyrosine\u002Fsummary\u002Fjson')\n```\n\n### 3. Standardizing Metabolite Nomenclature with RefMet\n\nUse the RefMet database to standardize metabolite names and access systematic classification across four structural resolution levels.\n\n**Key operations:**\n- Match common metabolite names to standardized RefMet names\n- Query by chemical formula, exact mass, or InChI Key\n- Access hierarchical classification (super class, main class, sub class)\n- Retrieve all RefMet entries or filter by classification\n\n**Example queries:**\n```python\n# Standardize a metabolite name\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fmatch\u002Fcitrate\u002Fname\u002Fjson')\n\n# Query by molecular formula\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fformula\u002FC12H24O2\u002Fall\u002Fjson')\n\n# Get all metabolites in a specific class\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fmain_class\u002FFatty%20Acids\u002Fall\u002Fjson')\n\n# Retrieve complete RefMet database\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fall\u002Fjson')\n```\n\n### 4. Performing Mass Spectrometry Searches\n\nSearch for compounds by mass-to-charge ratio (m\u002Fz) with specified ion adducts and tolerance levels.\n\n**Key operations:**\n- Search precursor ion masses across multiple databases (Metabolomics Workbench, LIPIDS, RefMet)\n- Specify ion adduct types (M+H, M-H, M+Na, M+NH4, M+2H, etc.)\n- Calculate exact masses for known metabolites with specific adducts\n- Set mass tolerance for flexible matching\n\n**Example queries:**\n```python\n# Search by m\u002Fz value with M+H adduct\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002FMB\u002F635.52\u002FM+H\u002F0.5\u002Fjson')\n\n# Calculate exact mass for a metabolite with specific adduct\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002Fexactmass\u002FPC(34:1)\u002FM+H\u002Fjson')\n\n# Search across RefMet database\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002FREFMET\u002F200.15\u002FM-H\u002F0.3\u002Fjson')\n```\n\n### 5. Filtering Studies by Analytical and Biological Parameters\n\nUse the MetStat context to find studies matching specific experimental conditions.\n\n**Key operations:**\n- Filter by analytical method (LCMS, GCMS, NMR)\n- Specify ionization polarity (POSITIVE, NEGATIVE)\n- Filter by chromatography type (HILIC, RP, GC)\n- Target specific species, sample sources, or diseases\n- Combine multiple filters using semicolon-delimited format\n\n**Example queries:**\n```python\n# Find human blood studies on diabetes using LC-MS\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002FLCMS;POSITIVE;HILIC;Human;Blood;Diabetes\u002Fjson')\n\n# Find all human blood studies containing tyrosine\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002F;;;Human;Blood;;;Tyrosine\u002Fjson')\n\n# Filter by analytical method only\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002FGCMS;;;;;;\u002Fjson')\n```\n\n### 6. Accessing Gene and Protein Information\n\nRetrieve gene and protein data associated with metabolic pathways and metabolite metabolism.\n\n**Key operations:**\n- Query genes by symbol, name, or ID\n- Access protein sequences and annotations\n- Cross-reference between gene IDs, RefSeq IDs, and UniProt IDs\n- Retrieve gene-metabolite associations\n\n**Example queries:**\n```python\n# Get gene information by symbol\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fgene\u002Fgene_symbol\u002FACACA\u002Fall\u002Fjson')\n\n# Retrieve protein data by UniProt ID\nresponse = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fprotein\u002Funiprot_id\u002FQ13085\u002Fall\u002Fjson')\n```\n\n## Common Workflows\n\n### Workflow 1: Finding Studies for a Specific Metabolite\n\nTo find all studies containing measurements of a specific metabolite:\n\n1. First standardize the metabolite name using RefMet:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Frefmet\u002Fmatch\u002Fglucose\u002Fname\u002Fjson')\n   ```\n\n2. Use the standardized name to search for studies:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Frefmet_name\u002FGlucose\u002Fsummary\u002Fjson')\n   ```\n\n3. Retrieve experimental data from specific studies:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST000001\u002Fdata\u002Fjson')\n   ```\n\n### Workflow 2: Identifying Compounds from MS Data\n\nTo identify potential compounds from mass spectrometry m\u002Fz values:\n\n1. Perform m\u002Fz search with appropriate adduct and tolerance:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmoverz\u002FMB\u002F180.06\u002FM+H\u002F0.5\u002Fjson')\n   ```\n\n2. Review candidate compounds from results\n\n3. Retrieve detailed information for candidate compounds:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F{regno}\u002Fall\u002Fjson')\n   ```\n\n4. Download structures for confirmation:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fcompound\u002Fregno\u002F{regno}\u002Fpng')\n   ```\n\n### Workflow 3: Exploring Disease-Specific Metabolomics\n\nTo find metabolomics studies for a specific disease and analytical platform:\n\n1. Use MetStat to filter studies:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fmetstat\u002FLCMS;POSITIVE;;Human;;Cancer\u002Fjson')\n   ```\n\n2. Review study IDs from results\n\n3. Access detailed study information:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST{ID}\u002Fsummary\u002Fjson')\n   ```\n\n4. Retrieve complete experimental data:\n   ```python\n   response = requests.get('https:\u002F\u002Fwww.metabolomicsworkbench.org\u002Frest\u002Fstudy\u002Fstudy_id\u002FST{ID}\u002Fdata\u002Fjson')\n   ```\n\n## Output Formats\n\nThe API supports two primary output formats:\n- **JSON** (default): Machine-readable format, ideal for programmatic access\n- **TXT**: Human-readable tab-delimited text format\n\nSpecify format by appending `\u002Fjson` or `\u002Ftxt` to API URLs. When format is omitted, JSON is returned by default.\n\n## Best Practices\n\n1. **Use RefMet for standardization**: Always standardize metabolite names through RefMet before searching studies to ensure consistent nomenclature\n\n2. **Specify appropriate adducts**: When performing m\u002Fz searches, use the correct ion adduct type for your analytical method (e.g., M+H for positive mode ESI)\n\n3. **Set reasonable tolerances**: Use appropriate mass tolerance values (typically 0.5 Da for low-resolution, 0.01 Da for high-resolution MS)\n\n4. **Cache reference data**: Consider caching frequently used reference data (RefMet database, compound information) to minimize API calls\n\n5. **Handle pagination**: For large result sets, be prepared to handle multiple data structures in responses\n\n6. **Validate identifiers**: Cross-reference metabolite identifiers across multiple databases when possible to ensure correct compound identification\n\n## Resources\n\n### references\u002F\n\nDetailed API reference documentation is available in `references\u002Fapi_reference.md`, including:\n- Complete REST API endpoint specifications\n- All available contexts (compound, study, refmet, metstat, gene, protein, moverz)\n- Input\u002Foutput parameter details\n- Ion adduct types for mass spectrometry\n- Additional query examples\n\nLoad this reference file when detailed API specifications are needed or when working with less common endpoints.\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmetabolomics-workbench-database",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill metabolomics-workbench-database",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill metabolomics-workbench-database",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill metabolomics-workbench-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmetabolomics-workbench-database\u002FSKILL.md",true,[73],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fapi_reference.md","REFERENCE","参考文档",16384,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmetabolomics-workbench-database\u002Freferences\u002Fapi_reference.md",[80,107,134,162,197,221],{"skillNo":81,"name":82,"nameZh":6,"description":83,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":84,"categories":85,"keywords":87,"publishedAt":44},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[86],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[88,89,90,91,92,31,93,94,95,96,97,98,99,100,101,102,103,104,105,106],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":108,"name":109,"nameZh":6,"description":110,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":111,"categories":112,"keywords":114,"publishedAt":44},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[113],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[115,116,117,118,119,120,121,122,123,124,125,96,126,127,128,129,130,131,132,133],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":135,"name":136,"nameZh":6,"description":137,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":138,"categories":139,"keywords":147,"publishedAt":44},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[140,141,142],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":143,"categoryName":144,"taxonomyType":145,"taxonomyTypeDesc":146},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[148,149,150,151,152,153,154,31,155,156,157,158,159,160,161,95,38,29,96,36],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP",{"skillNo":163,"name":164,"nameZh":6,"description":165,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":167,"sourceHtmlUrl":168,"categories":169,"keywords":173,"publishedAt":44},"01M6AFY6F7CC2BBZP1QD6VTNVC","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). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[170],{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,31,196],"artifacts builder","web","web artifacts builder","artifacts","builder","state","routing","state management","component","css","frontend","ui","Tools","file","components","shadcn","react","Tailwind","suite","jsx","html","single-file","ui components",{"skillNo":198,"name":199,"nameZh":6,"description":200,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":201,"sourceHtmlUrl":202,"categories":203,"keywords":208,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[204,207],{"categoryNo":205,"categoryName":206,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":171,"categoryName":172,"taxonomyType":21,"taxonomyTypeDesc":22},[209,210,211,212,213,175,214,184,185,215,216,217,218,31,219,98,220],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":222,"name":223,"nameZh":6,"description":224,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":166,"licenseFileUrl":225,"sourceHtmlUrl":226,"categories":227,"keywords":235,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[228,231,232],{"categoryNo":229,"categoryName":230,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":233,"categoryName":234,"taxonomyType":145,"taxonomyTypeDesc":146},"01JBSEEDCAT000000000000036","内容创作者",[236,237,238,239,240,241,42,242,177,243,187,126,244,245,246,247,248,160,249,96,250,251,252,253,254,104,255],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]