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Search compounds by structure\u002Fproperties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33332,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fchembl-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],"database","chembl database","chembl","search","data","discovery","find","properties","query","structure","drug discovery","sar","for","Molecules","2026-11-10 10:12:30",false,"---\nname: chembl-database\ndescription: \"Query ChEMBL's bioactive molecules and drug discovery data. Search compounds by structure\u002Fproperties, retrieve bioactivity data (IC50, Ki), find inhibitors, perform SAR studies, for medicinal chemistry.\"\n---\n\n# ChEMBL Database\n\n## Overview\n\nChEMBL is a manually curated database of bioactive molecules maintained by the European Bioinformatics Institute (EBI), containing over 2 million compounds, 19 million bioactivity measurements, 13,000+ drug targets, and data on approved drugs and clinical candidates. Access and query this data programmatically using the ChEMBL Python client for drug discovery and medicinal chemistry research.\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- **Compound searches**: Finding molecules by name, structure, or properties\n- **Target information**: Retrieving data about proteins, enzymes, or biological targets\n- **Bioactivity data**: Querying IC50, Ki, EC50, or other activity measurements\n- **Drug information**: Looking up approved drugs, mechanisms, or indications\n- **Structure searches**: Performing similarity or substructure searches\n- **Cheminformatics**: Analyzing molecular properties and drug-likeness\n- **Target-ligand relationships**: Exploring compound-target interactions\n- **Drug discovery**: Identifying inhibitors, agonists, or bioactive molecules\n\n## Installation and Setup\n\n### Python Client\n\nThe ChEMBL Python client is required for programmatic access:\n\n```bash\nuv pip install chembl_webresource_client\n```\n\n### Basic Usage Pattern\n\n```python\nfrom chembl_webresource_client.new_client import new_client\n\n# Access different endpoints\nmolecule = new_client.molecule\ntarget = new_client.target\nactivity = new_client.activity\ndrug = new_client.drug\n```\n\n## Core Capabilities\n\n### 1. Molecule Queries\n\n**Retrieve by ChEMBL ID:**\n```python\nmolecule = new_client.molecule\naspirin = molecule.get('CHEMBL25')\n```\n\n**Search by name:**\n```python\nresults = molecule.filter(pref_name__icontains='aspirin')\n```\n\n**Filter by properties:**\n```python\n# Find small molecules (MW \u003C= 500) with favorable LogP\nresults = molecule.filter(\n    molecule_properties__mw_freebase__lte=500,\n    molecule_properties__alogp__lte=5\n)\n```\n\n### 2. Target Queries\n\n**Retrieve target information:**\n```python\ntarget = new_client.target\negfr = target.get('CHEMBL203')\n```\n\n**Search for specific target types:**\n```python\n# Find all kinase targets\nkinases = target.filter(\n    target_type='SINGLE PROTEIN',\n    pref_name__icontains='kinase'\n)\n```\n\n### 3. Bioactivity Data\n\n**Query activities for a target:**\n```python\nactivity = new_client.activity\n# Find potent EGFR inhibitors\nresults = activity.filter(\n    target_chembl_id='CHEMBL203',\n    standard_type='IC50',\n    standard_value__lte=100,\n    standard_units='nM'\n)\n```\n\n**Get all activities for a compound:**\n```python\ncompound_activities = activity.filter(\n    molecule_chembl_id='CHEMBL25',\n    pchembl_value__isnull=False\n)\n```\n\n### 4. Structure-Based Searches\n\n**Similarity search:**\n```python\nsimilarity = new_client.similarity\n# Find compounds similar to aspirin\nsimilar = similarity.filter(\n    smiles='CC(=O)Oc1ccccc1C(=O)O',\n    similarity=85  # 85% similarity threshold\n)\n```\n\n**Substructure search:**\n```python\nsubstructure = new_client.substructure\n# Find compounds containing benzene ring\nresults = substructure.filter(smiles='c1ccccc1')\n```\n\n### 5. Drug Information\n\n**Retrieve drug data:**\n```python\ndrug = new_client.drug\ndrug_info = drug.get('CHEMBL25')\n```\n\n**Get mechanisms of action:**\n```python\nmechanism = new_client.mechanism\nmechanisms = mechanism.filter(molecule_chembl_id='CHEMBL25')\n```\n\n**Query drug indications:**\n```python\ndrug_indication = new_client.drug_indication\nindications = drug_indication.filter(molecule_chembl_id='CHEMBL25')\n```\n\n## Query Workflow\n\n### Workflow 1: Finding Inhibitors for a Target\n\n1. **Identify the target** by searching by name:\n   ```python\n   targets = new_client.target.filter(pref_name__icontains='EGFR')\n   target_id = targets[0]['target_chembl_id']\n   ```\n\n2. **Query bioactivity data** for that target:\n   ```python\n   activities = new_client.activity.filter(\n       target_chembl_id=target_id,\n       standard_type='IC50',\n       standard_value__lte=100\n   )\n   ```\n\n3. **Extract compound IDs** and retrieve details:\n   ```python\n   compound_ids = [act['molecule_chembl_id'] for act in activities]\n   compounds = [new_client.molecule.get(cid) for cid in compound_ids]\n   ```\n\n### Workflow 2: Analyzing a Known Drug\n\n1. **Get drug information**:\n   ```python\n   drug_info = new_client.drug.get('CHEMBL1234')\n   ```\n\n2. **Retrieve mechanisms**:\n   ```python\n   mechanisms = new_client.mechanism.filter(molecule_chembl_id='CHEMBL1234')\n   ```\n\n3. **Find all bioactivities**:\n   ```python\n   activities = new_client.activity.filter(molecule_chembl_id='CHEMBL1234')\n   ```\n\n### Workflow 3: Structure-Activity Relationship (SAR) Study\n\n1. **Find similar compounds**:\n   ```python\n   similar = new_client.similarity.filter(smiles='query_smiles', similarity=80)\n   ```\n\n2. **Get activities for each compound**:\n   ```python\n   for compound in similar:\n       activities = new_client.activity.filter(\n           molecule_chembl_id=compound['molecule_chembl_id']\n       )\n   ```\n\n3. **Analyze property-activity relationships** using molecular properties from results.\n\n## Filter Operators\n\nChEMBL supports Django-style query filters:\n\n- `__exact` - Exact match\n- `__iexact` - Case-insensitive exact match\n- `__contains` \u002F `__icontains` - Substring matching\n- `__startswith` \u002F `__endswith` - Prefix\u002Fsuffix matching\n- `__gt`, `__gte`, `__lt`, `__lte` - Numeric comparisons\n- `__range` - Value in range\n- `__in` - Value in list\n- `__isnull` - Null\u002Fnot null check\n\n## Data Export and Analysis\n\nConvert results to pandas DataFrame for analysis:\n\n```python\nimport pandas as pd\n\nactivities = new_client.activity.filter(target_chembl_id='CHEMBL203')\ndf = pd.DataFrame(list(activities))\n\n# Analyze results\nprint(df['standard_value'].describe())\nprint(df.groupby('standard_type').size())\n```\n\n## Performance Optimization\n\n### Caching\n\nThe client automatically caches results for 24 hours. Configure caching:\n\n```python\nfrom chembl_webresource_client.settings import Settings\n\n# Disable caching\nSettings.Instance().CACHING = False\n\n# Adjust cache expiration (seconds)\nSettings.Instance().CACHE_EXPIRE = 86400\n```\n\n### Lazy Evaluation\n\nQueries execute only when data is accessed. Convert to list to force execution:\n\n```python\n# Query is not executed yet\nresults = molecule.filter(pref_name__icontains='aspirin')\n\n# Force execution\nresults_list = list(results)\n```\n\n### Pagination\n\nResults are paginated automatically. Iterate through all results:\n\n```python\nfor activity in new_client.activity.filter(target_chembl_id='CHEMBL203'):\n    # Process each activity\n    print(activity['molecule_chembl_id'])\n```\n\n## Common Use Cases\n\n### Find Kinase Inhibitors\n\n```python\n# Identify kinase targets\nkinases = new_client.target.filter(\n    target_type='SINGLE PROTEIN',\n    pref_name__icontains='kinase'\n)\n\n# Get potent inhibitors\nfor kinase in kinases[:5]:  # First 5 kinases\n    activities = new_client.activity.filter(\n        target_chembl_id=kinase['target_chembl_id'],\n        standard_type='IC50',\n        standard_value__lte=50\n    )\n```\n\n### Explore Drug Repurposing\n\n```python\n# Get approved drugs\ndrugs = new_client.drug.filter()\n\n# For each drug, find all targets\nfor drug in drugs[:10]:\n    mechanisms = new_client.mechanism.filter(\n        molecule_chembl_id=drug['molecule_chembl_id']\n    )\n```\n\n### Virtual Screening\n\n```python\n# Find compounds with desired properties\ncandidates = new_client.molecule.filter(\n    molecule_properties__mw_freebase__range=[300, 500],\n    molecule_properties__alogp__lte=5,\n    molecule_properties__hba__lte=10,\n    molecule_properties__hbd__lte=5\n)\n```\n\n## Resources\n\n### scripts\u002Fexample_queries.py\n\nReady-to-use Python functions demonstrating common ChEMBL query patterns:\n\n- `get_molecule_info()` - Retrieve molecule details by ID\n- `search_molecules_by_name()` - Name-based molecule search\n- `find_molecules_by_properties()` - Property-based filtering\n- `get_bioactivity_data()` - Query bioactivities for targets\n- `find_similar_compounds()` - Similarity searching\n- `substructure_search()` - Substructure matching\n- `get_drug_info()` - Retrieve drug information\n- `find_kinase_inhibitors()` - Specialized kinase inhibitor search\n- `export_to_dataframe()` - Convert results to pandas DataFrame\n\nConsult this script for implementation details and usage examples.\n\n### references\u002Fapi_reference.md\n\nComprehensive API documentation including:\n\n- Complete endpoint listing (molecule, target, activity, assay, drug, etc.)\n- All filter operators and query patterns\n- Molecular properties and bioactivity fields\n- Advanced query examples\n- Configuration and performance tuning\n- Error handling and rate limiting\n\nRefer to this document when detailed API information is needed or when troubleshooting queries.\n\n## Important Notes\n\n### Data Reliability\n\n- ChEMBL data is manually curated but may contain inconsistencies\n- Always check `data_validity_comment` field in activity records\n- Be aware of `potential_duplicate` flags\n\n### Units and Standards\n\n- Bioactivity values use standard units (nM, uM, etc.)\n- `pchembl_value` provides normalized activity (-log scale)\n- Check `standard_type` to understand measurement type (IC50, Ki, EC50, etc.)\n\n### Rate Limiting\n\n- Respect ChEMBL's fair usage policies\n- Use caching to minimize repeated requests\n- Consider bulk downloads for large datasets\n- Avoid hammering the API with rapid consecutive requests\n\n### Chemical Structure Formats\n\n- SMILES strings are the primary structure format\n- InChI keys available for compounds\n- SVG images can be generated via the image endpoint\n\n## Additional Resources\n\n- ChEMBL website: https:\u002F\u002Fwww.ebi.ac.uk\u002Fchembl\u002F\n- API documentation: https:\u002F\u002Fwww.ebi.ac.uk\u002Fchembl\u002Fapi\u002Fdata\u002Fdocs\n- Python client GitHub: https:\u002F\u002Fgithub.com\u002Fchembl\u002Fchembl_webresource_client\n- Interface documentation: https:\u002F\u002Fchembl.gitbook.io\u002Fchembl-interface-documentation\u002F\n- Example notebooks: https:\u002F\u002Fgithub.com\u002Fchembl\u002Fnotebooks\n","# ChEMBL Database\n\n## Overview\n\nChEMBL is a manually curated database of bioactive molecules maintained by the European Bioinformatics Institute (EBI), containing over 2 million compounds, 19 million bioactivity measurements, 13,000+ drug targets, and data on approved drugs and clinical candidates. Access and query this data programmatically using the ChEMBL Python client for drug discovery and medicinal chemistry research.\n\n## When to Use This Skill\n\nThis skill should be used when:\n\n- **Compound searches**: Finding molecules by name, structure, or properties\n- **Target information**: Retrieving data about proteins, enzymes, or biological targets\n- **Bioactivity data**: Querying IC50, Ki, EC50, or other activity measurements\n- **Drug information**: Looking up approved drugs, mechanisms, or indications\n- **Structure searches**: Performing similarity or substructure searches\n- **Cheminformatics**: Analyzing molecular properties and drug-likeness\n- **Target-ligand relationships**: Exploring compound-target interactions\n- **Drug discovery**: Identifying inhibitors, agonists, or bioactive molecules\n\n## Installation and Setup\n\n### Python Client\n\nThe ChEMBL Python client is required for programmatic access:\n\n```bash\nuv pip install chembl_webresource_client\n```\n\n### Basic Usage Pattern\n\n```python\nfrom chembl_webresource_client.new_client import new_client\n\n# Access different endpoints\nmolecule = new_client.molecule\ntarget = new_client.target\nactivity = new_client.activity\ndrug = new_client.drug\n```\n\n## Core Capabilities\n\n### 1. Molecule Queries\n\n**Retrieve by ChEMBL ID:**\n```python\nmolecule = new_client.molecule\naspirin = molecule.get('CHEMBL25')\n```\n\n**Search by name:**\n```python\nresults = molecule.filter(pref_name__icontains='aspirin')\n```\n\n**Filter by properties:**\n```python\n# Find small molecules (MW \u003C= 500) with favorable LogP\nresults = molecule.filter(\n    molecule_properties__mw_freebase__lte=500,\n    molecule_properties__alogp__lte=5\n)\n```\n\n### 2. Target Queries\n\n**Retrieve target information:**\n```python\ntarget = new_client.target\negfr = target.get('CHEMBL203')\n```\n\n**Search for specific target types:**\n```python\n# Find all kinase targets\nkinases = target.filter(\n    target_type='SINGLE PROTEIN',\n    pref_name__icontains='kinase'\n)\n```\n\n### 3. Bioactivity Data\n\n**Query activities for a target:**\n```python\nactivity = new_client.activity\n# Find potent EGFR inhibitors\nresults = activity.filter(\n    target_chembl_id='CHEMBL203',\n    standard_type='IC50',\n    standard_value__lte=100,\n    standard_units='nM'\n)\n```\n\n**Get all activities for a compound:**\n```python\ncompound_activities = activity.filter(\n    molecule_chembl_id='CHEMBL25',\n    pchembl_value__isnull=False\n)\n```\n\n### 4. Structure-Based Searches\n\n**Similarity search:**\n```python\nsimilarity = new_client.similarity\n# Find compounds similar to aspirin\nsimilar = similarity.filter(\n    smiles='CC(=O)Oc1ccccc1C(=O)O',\n    similarity=85  # 85% similarity threshold\n)\n```\n\n**Substructure search:**\n```python\nsubstructure = new_client.substructure\n# Find compounds containing benzene ring\nresults = substructure.filter(smiles='c1ccccc1')\n```\n\n### 5. Drug Information\n\n**Retrieve drug data:**\n```python\ndrug = new_client.drug\ndrug_info = drug.get('CHEMBL25')\n```\n\n**Get mechanisms of action:**\n```python\nmechanism = new_client.mechanism\nmechanisms = mechanism.filter(molecule_chembl_id='CHEMBL25')\n```\n\n**Query drug indications:**\n```python\ndrug_indication = new_client.drug_indication\nindications = drug_indication.filter(molecule_chembl_id='CHEMBL25')\n```\n\n## Query Workflow\n\n### Workflow 1: Finding Inhibitors for a Target\n\n1. **Identify the target** by searching by name:\n   ```python\n   targets = new_client.target.filter(pref_name__icontains='EGFR')\n   target_id = targets[0]['target_chembl_id']\n   ```\n\n2. **Query bioactivity data** for that target:\n   ```python\n   activities = new_client.activity.filter(\n       target_chembl_id=target_id,\n       standard_type='IC50',\n       standard_value__lte=100\n   )\n   ```\n\n3. **Extract compound IDs** and retrieve details:\n   ```python\n   compound_ids = [act['molecule_chembl_id'] for act in activities]\n   compounds = [new_client.molecule.get(cid) for cid in compound_ids]\n   ```\n\n### Workflow 2: Analyzing a Known Drug\n\n1. **Get drug information**:\n   ```python\n   drug_info = new_client.drug.get('CHEMBL1234')\n   ```\n\n2. **Retrieve mechanisms**:\n   ```python\n   mechanisms = new_client.mechanism.filter(molecule_chembl_id='CHEMBL1234')\n   ```\n\n3. **Find all bioactivities**:\n   ```python\n   activities = new_client.activity.filter(molecule_chembl_id='CHEMBL1234')\n   ```\n\n### Workflow 3: Structure-Activity Relationship (SAR) Study\n\n1. **Find similar compounds**:\n   ```python\n   similar = new_client.similarity.filter(smiles='query_smiles', similarity=80)\n   ```\n\n2. **Get activities for each compound**:\n   ```python\n   for compound in similar:\n       activities = new_client.activity.filter(\n           molecule_chembl_id=compound['molecule_chembl_id']\n       )\n   ```\n\n3. **Analyze property-activity relationships** using molecular properties from results.\n\n## Filter Operators\n\nChEMBL supports Django-style query filters:\n\n- `__exact` - Exact match\n- `__iexact` - Case-insensitive exact match\n- `__contains` \u002F `__icontains` - Substring matching\n- `__startswith` \u002F `__endswith` - Prefix\u002Fsuffix matching\n- `__gt`, `__gte`, `__lt`, `__lte` - Numeric comparisons\n- `__range` - Value in range\n- `__in` - Value in list\n- `__isnull` - Null\u002Fnot null check\n\n## Data Export and Analysis\n\nConvert results to pandas DataFrame for analysis:\n\n```python\nimport pandas as pd\n\nactivities = new_client.activity.filter(target_chembl_id='CHEMBL203')\ndf = pd.DataFrame(list(activities))\n\n# Analyze results\nprint(df['standard_value'].describe())\nprint(df.groupby('standard_type').size())\n```\n\n## Performance Optimization\n\n### Caching\n\nThe client automatically caches results for 24 hours. Configure caching:\n\n```python\nfrom chembl_webresource_client.settings import Settings\n\n# Disable caching\nSettings.Instance().CACHING = False\n\n# Adjust cache expiration (seconds)\nSettings.Instance().CACHE_EXPIRE = 86400\n```\n\n### Lazy Evaluation\n\nQueries execute only when data is accessed. Convert to list to force execution:\n\n```python\n# Query is not executed yet\nresults = molecule.filter(pref_name__icontains='aspirin')\n\n# Force execution\nresults_list = list(results)\n```\n\n### Pagination\n\nResults are paginated automatically. Iterate through all results:\n\n```python\nfor activity in new_client.activity.filter(target_chembl_id='CHEMBL203'):\n    # Process each activity\n    print(activity['molecule_chembl_id'])\n```\n\n## Common Use Cases\n\n### Find Kinase Inhibitors\n\n```python\n# Identify kinase targets\nkinases = new_client.target.filter(\n    target_type='SINGLE PROTEIN',\n    pref_name__icontains='kinase'\n)\n\n# Get potent inhibitors\nfor kinase in kinases[:5]:  # First 5 kinases\n    activities = new_client.activity.filter(\n        target_chembl_id=kinase['target_chembl_id'],\n        standard_type='IC50',\n        standard_value__lte=50\n    )\n```\n\n### Explore Drug Repurposing\n\n```python\n# Get approved drugs\ndrugs = new_client.drug.filter()\n\n# For each drug, find all targets\nfor drug in drugs[:10]:\n    mechanisms = new_client.mechanism.filter(\n        molecule_chembl_id=drug['molecule_chembl_id']\n    )\n```\n\n### Virtual Screening\n\n```python\n# Find compounds with desired properties\ncandidates = new_client.molecule.filter(\n    molecule_properties__mw_freebase__range=[300, 500],\n    molecule_properties__alogp__lte=5,\n    molecule_properties__hba__lte=10,\n    molecule_properties__hbd__lte=5\n)\n```\n\n## Resources\n\n### scripts\u002Fexample_queries.py\n\nReady-to-use Python functions demonstrating common ChEMBL query patterns:\n\n- `get_molecule_info()` - Retrieve molecule details by ID\n- `search_molecules_by_name()` - Name-based molecule search\n- `find_molecules_by_properties()` - Property-based filtering\n- `get_bioactivity_data()` - Query bioactivities for targets\n- `find_similar_compounds()` - Similarity searching\n- `substructure_search()` - Substructure matching\n- `get_drug_info()` - Retrieve drug information\n- `find_kinase_inhibitors()` - Specialized kinase inhibitor search\n- `export_to_dataframe()` - Convert results to pandas DataFrame\n\nConsult this script for implementation details and usage examples.\n\n### references\u002Fapi_reference.md\n\nComprehensive API documentation including:\n\n- Complete endpoint listing (molecule, target, activity, assay, drug, etc.)\n- All filter operators and query patterns\n- Molecular properties and bioactivity fields\n- Advanced query examples\n- Configuration and performance tuning\n- Error handling and rate limiting\n\nRefer to this document when detailed API information is needed or when troubleshooting queries.\n\n## Important Notes\n\n### Data Reliability\n\n- ChEMBL data is manually curated but may contain inconsistencies\n- Always check `data_validity_comment` field in activity records\n- Be aware of `potential_duplicate` flags\n\n### Units and Standards\n\n- Bioactivity values use standard units (nM, uM, etc.)\n- `pchembl_value` provides normalized activity (-log scale)\n- Check `standard_type` to understand measurement type (IC50, Ki, EC50, etc.)\n\n### Rate Limiting\n\n- Respect ChEMBL's fair usage policies\n- Use caching to minimize repeated requests\n- Consider bulk downloads for large datasets\n- Avoid hammering the API with rapid consecutive requests\n\n### Chemical Structure Formats\n\n- SMILES strings are the primary structure format\n- InChI keys available for compounds\n- SVG images can be generated via the image endpoint\n\n## Additional Resources\n\n- ChEMBL website: https:\u002F\u002Fwww.ebi.ac.uk\u002Fchembl\u002F\n- API documentation: https:\u002F\u002Fwww.ebi.ac.uk\u002Fchembl\u002Fapi\u002Fdata\u002Fdocs\n- Python client GitHub: https:\u002F\u002Fgithub.com\u002Fchembl\u002Fchembl_webresource_client\n- Interface documentation: https:\u002F\u002Fchembl.gitbook.io\u002Fchembl-interface-documentation\u002F\n- Example notebooks: https:\u002F\u002Fgithub.com\u002Fchembl\u002Fnotebooks\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fchembl-database",3911,"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","2026-11-11 21:38:31",{"source":8,"skillName":5,"commands":57,"prompt":67,"zipAvailable":68},[58,61,64],{"tool":59,"command":60},"npx","npx skills add davila7\u002Fclaude-code-templates --skill chembl-database",{"tool":62,"command":63},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill chembl-database",{"tool":65,"command":66},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill chembl-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fchembl-database\u002FSKILL.md",true,[70,76],{"path":71,"resourceType":72,"resourceTypeDesc":73,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Fapi_reference.md","REFERENCE","参考文档",6879,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fchembl-database\u002Freferences\u002Fapi_reference.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"scripts\u002Fexample_queries.py","SCRIPT","脚本",7160,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fchembl-database\u002Fscripts\u002Fexample_queries.py",[83,110,137,166,201,225],{"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":15,"licenseFileUrl":6,"sourceHtmlUrl":87,"categories":88,"keywords":90,"publishedAt":41},"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",[89],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[91,92,93,94,95,39,96,97,98,99,100,101,102,103,104,105,106,107,108,109],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":111,"name":112,"nameZh":6,"description":113,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":114,"categories":115,"keywords":117,"publishedAt":41},"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",[116],{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},[118,119,120,121,122,123,124,125,126,127,128,99,129,130,131,132,133,134,135,136],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":150,"publishedAt":41},"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",[143,144,145],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":146,"categoryName":147,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[151,152,153,154,155,156,157,39,158,159,160,161,162,163,164,98,165,27,99,35],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access",{"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":170,"licenseFileUrl":171,"sourceHtmlUrl":172,"categories":173,"keywords":177,"publishedAt":41},"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",[174],{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,39,200],"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":202,"name":203,"nameZh":6,"description":204,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":170,"licenseFileUrl":205,"sourceHtmlUrl":206,"categories":207,"keywords":212,"publishedAt":41},"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",[208,211],{"categoryNo":209,"categoryName":210,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":175,"categoryName":176,"taxonomyType":21,"taxonomyTypeDesc":22},[213,214,215,216,217,179,218,188,189,219,220,221,222,39,223,101,224],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":226,"name":227,"nameZh":6,"description":228,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":170,"licenseFileUrl":229,"sourceHtmlUrl":230,"categories":231,"keywords":239,"publishedAt":41},"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",[232,235,236],{"categoryNo":233,"categoryName":234,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":24,"categoryName":25,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":237,"categoryName":238,"taxonomyType":148,"taxonomyTypeDesc":149},"01JBSEEDCAT000000000000036","内容创作者",[240,241,242,243,244,245,31,246,181,247,191,129,248,249,250,251,252,163,253,99,254,255,256,257,258,107,259],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]