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Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.","davila7\u002Fclaude-code-templates","davila7","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6216945?v=4","01M6ADEWWFPS191NFM0Y1EX1JH","Daniel Avila","01M6ADJHGM78PHQRD3XZTFDVKK",33418,"MIT","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmedchem\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[5,24,25,26,27,28,29,30,31,32,33,34,35,36],"metrics","library","prioritization","Rules","filtering","complexity","filters","compound","alerts","structural","for","apply","likeness","2026-11-10 10:12:30",false,"---\nname: medchem\ndescription: \"Medicinal chemistry filters. Apply drug-likeness rules (Lipinski, Veber), PAINS filters, structural alerts, complexity metrics, for compound prioritization and library filtering.\"\n---\n\n# Medchem\n\n## Overview\n\nMedchem is a Python library for molecular filtering and prioritization in drug discovery workflows. Apply hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale. Rules and filters are context-specific—use as guidelines combined with domain expertise.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries\n- Filtering molecules by structural alerts or PAINS patterns\n- Prioritizing compounds for lead optimization\n- Assessing compound quality and medicinal chemistry properties\n- Detecting reactive or problematic functional groups\n- Calculating molecular complexity metrics\n\n## Installation\n\n```bash\nuv pip install medchem\n```\n\n## Core Capabilities\n\n### 1. Medicinal Chemistry Rules\n\nApply established drug-likeness rules to molecules using the `medchem.rules` module.\n\n**Available Rules:**\n- Rule of Five (Lipinski)\n- Rule of Oprea\n- Rule of CNS\n- Rule of leadlike (soft and strict)\n- Rule of three\n- Rule of Reos\n- Rule of drug\n- Rule of Veber\n- Golden triangle\n- PAINS filters\n\n**Single Rule Application:**\n\n```python\nimport medchem as mc\n\n# Apply Rule of Five to a SMILES string\nsmiles = \"CC(=O)OC1=CC=CC=C1C(=O)O\"  # Aspirin\npasses = mc.rules.basic_rules.rule_of_five(smiles)\n# Returns: True\n\n# Check specific rules\npasses_oprea = mc.rules.basic_rules.rule_of_oprea(smiles)\npasses_cns = mc.rules.basic_rules.rule_of_cns(smiles)\n```\n\n**Multiple Rules with RuleFilters:**\n\n```python\nimport datamol as dm\nimport medchem as mc\n\n# Load molecules\nmols = [dm.to_mol(smiles) for smiles in smiles_list]\n\n# Create filter with multiple rules\nrfilter = mc.rules.RuleFilters(\n    rule_list=[\n        \"rule_of_five\",\n        \"rule_of_oprea\",\n        \"rule_of_cns\",\n        \"rule_of_leadlike_soft\"\n    ]\n)\n\n# Apply filters with parallelization\nresults = rfilter(\n    mols=mols,\n    n_jobs=-1,  # Use all CPU cores\n    progress=True\n)\n```\n\n**Result Format:**\nResults are returned as dictionaries with pass\u002Ffail status and detailed information for each rule.\n\n### 2. Structural Alert Filters\n\nDetect potentially problematic structural patterns using the `medchem.structural` module.\n\n**Available Filters:**\n\n1. **Common Alerts** - General structural alerts derived from ChEMBL curation and literature\n2. **NIBR Filters** - Novartis Institutes for BioMedical Research filter set\n3. **Lilly Demerits** - Eli Lilly's demerit-based system (275 rules, molecules rejected at >100 demerits)\n\n**Common Alerts:**\n\n```python\nimport medchem as mc\n\n# Create filter\nalert_filter = mc.structural.CommonAlertsFilters()\n\n# Check single molecule\nmol = dm.to_mol(\"c1ccccc1\")\nhas_alerts, details = alert_filter.check_mol(mol)\n\n# Batch filtering with parallelization\nresults = alert_filter(\n    mols=mol_list,\n    n_jobs=-1,\n    progress=True\n)\n```\n\n**NIBR Filters:**\n\n```python\nimport medchem as mc\n\n# Apply NIBR filters\nnibr_filter = mc.structural.NIBRFilters()\nresults = nibr_filter(mols=mol_list, n_jobs=-1)\n```\n\n**Lilly Demerits:**\n\n```python\nimport medchem as mc\n\n# Calculate Lilly demerits\nlilly = mc.structural.LillyDemeritsFilters()\nresults = lilly(mols=mol_list, n_jobs=-1)\n\n# Each result includes demerit score and whether it passes (≤100 demerits)\n```\n\n### 3. Functional API for High-Level Operations\n\nThe `medchem.functional` module provides convenient functions for common workflows.\n\n**Quick Filtering:**\n\n```python\nimport medchem as mc\n\n# Apply NIBR filters to a list\nfilter_ok = mc.functional.nibr_filter(\n    mols=mol_list,\n    n_jobs=-1\n)\n\n# Apply common alerts\nalert_results = mc.functional.common_alerts_filter(\n    mols=mol_list,\n    n_jobs=-1\n)\n```\n\n### 4. Chemical Groups Detection\n\nIdentify specific chemical groups and functional groups using `medchem.groups`.\n\n**Available Groups:**\n- Hinge binders\n- Phosphate binders\n- Michael acceptors\n- Reactive groups\n- Custom SMARTS patterns\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Create group detector\ngroup = mc.groups.ChemicalGroup(groups=[\"hinge_binders\"])\n\n# Check for matches\nhas_matches = group.has_match(mol_list)\n\n# Get detailed match information\nmatches = group.get_matches(mol)\n```\n\n### 5. Named Catalogs\n\nAccess curated collections of chemical structures through `medchem.catalogs`.\n\n**Available Catalogs:**\n- Functional groups\n- Protecting groups\n- Common reagents\n- Standard fragments\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Access named catalogs\ncatalogs = mc.catalogs.NamedCatalogs\n\n# Use catalog for matching\ncatalog = catalogs.get(\"functional_groups\")\nmatches = catalog.get_matches(mol)\n```\n\n### 6. Molecular Complexity\n\nCalculate complexity metrics that approximate synthetic accessibility using `medchem.complexity`.\n\n**Common Metrics:**\n- Bertz complexity\n- Whitlock complexity\n- Barone complexity\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Calculate complexity\ncomplexity_score = mc.complexity.calculate_complexity(mol)\n\n# Filter by complexity threshold\ncomplex_filter = mc.complexity.ComplexityFilter(max_complexity=500)\nresults = complex_filter(mols=mol_list)\n```\n\n### 7. Constraints Filtering\n\nApply custom property-based constraints using `medchem.constraints`.\n\n**Example Constraints:**\n- Molecular weight ranges\n- LogP bounds\n- TPSA limits\n- Rotatable bond counts\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Define constraints\nconstraints = mc.constraints.Constraints(\n    mw_range=(200, 500),\n    logp_range=(-2, 5),\n    tpsa_max=140,\n    rotatable_bonds_max=10\n)\n\n# Apply constraints\nresults = constraints(mols=mol_list, n_jobs=-1)\n```\n\n### 8. Medchem Query Language\n\nUse a specialized query language for complex filtering criteria.\n\n**Query Examples:**\n```\n# Molecules passing Ro5 AND not having common alerts\n\"rule_of_five AND NOT common_alerts\"\n\n# CNS-like molecules with low complexity\n\"rule_of_cns AND complexity \u003C 400\"\n\n# Leadlike molecules without Lilly demerits\n\"rule_of_leadlike AND lilly_demerits == 0\"\n```\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Parse and apply query\nquery = mc.query.parse(\"rule_of_five AND NOT common_alerts\")\nresults = query.apply(mols=mol_list, n_jobs=-1)\n```\n\n## Workflow Patterns\n\n### Pattern 1: Initial Triage of Compound Library\n\nFilter a large compound collection to identify drug-like candidates.\n\n```python\nimport datamol as dm\nimport medchem as mc\nimport pandas as pd\n\n# Load compound library\ndf = pd.read_csv(\"compounds.csv\")\nmols = [dm.to_mol(smi) for smi in df[\"smiles\"]]\n\n# Apply primary filters\nrule_filter = mc.rules.RuleFilters(rule_list=[\"rule_of_five\", \"rule_of_veber\"])\nrule_results = rule_filter(mols=mols, n_jobs=-1, progress=True)\n\n# Apply structural alerts\nalert_filter = mc.structural.CommonAlertsFilters()\nalert_results = alert_filter(mols=mols, n_jobs=-1, progress=True)\n\n# Combine results\ndf[\"passes_rules\"] = rule_results[\"pass\"]\ndf[\"has_alerts\"] = alert_results[\"has_alerts\"]\ndf[\"drug_like\"] = df[\"passes_rules\"] & ~df[\"has_alerts\"]\n\n# Save filtered compounds\nfiltered_df = df[df[\"drug_like\"]]\nfiltered_df.to_csv(\"filtered_compounds.csv\", index=False)\n```\n\n### Pattern 2: Lead Optimization Filtering\n\nApply stricter criteria during lead optimization.\n\n```python\nimport medchem as mc\n\n# Create comprehensive filter\nfilters = {\n    \"rules\": mc.rules.RuleFilters(rule_list=[\"rule_of_leadlike_strict\"]),\n    \"alerts\": mc.structural.NIBRFilters(),\n    \"lilly\": mc.structural.LillyDemeritsFilters(),\n    \"complexity\": mc.complexity.ComplexityFilter(max_complexity=400)\n}\n\n# Apply all filters\nresults = {}\nfor name, filt in filters.items():\n    results[name] = filt(mols=candidate_mols, n_jobs=-1)\n\n# Identify compounds passing all filters\npasses_all = all(r[\"pass\"] for r in results.values())\n```\n\n### Pattern 3: Identify Specific Chemical Groups\n\nFind molecules containing specific functional groups or scaffolds.\n\n```python\nimport medchem as mc\n\n# Create group detector for multiple groups\ngroup_detector = mc.groups.ChemicalGroup(\n    groups=[\"hinge_binders\", \"phosphate_binders\"]\n)\n\n# Screen library\nmatches = group_detector.get_all_matches(mol_list)\n\n# Filter molecules with desired groups\nmol_with_groups = [mol for mol, match in zip(mol_list, matches) if match]\n```\n\n## Best Practices\n\n1. **Context Matters**: Don't blindly apply filters. Understand the biological target and chemical space.\n\n2. **Combine Multiple Filters**: Use rules, structural alerts, and domain knowledge together for better decisions.\n\n3. **Use Parallelization**: For large datasets (>1000 molecules), always use `n_jobs=-1` for parallel processing.\n\n4. **Iterative Refinement**: Start with broad filters (Ro5), then apply more specific criteria (CNS, leadlike) as needed.\n\n5. **Document Filtering Decisions**: Track which molecules were filtered out and why for reproducibility.\n\n6. **Validate Results**: Remember that marketed drugs often fail standard filters—use these as guidelines, not absolute rules.\n\n7. **Consider Prodrugs**: Molecules designed as prodrugs may intentionally violate standard medicinal chemistry rules.\n\n## Resources\n\n### references\u002Fapi_guide.md\nComprehensive API reference covering all medchem modules with detailed function signatures, parameters, and return types.\n\n### references\u002Frules_catalog.md\nComplete catalog of available rules, filters, and alerts with descriptions, thresholds, and literature references.\n\n### scripts\u002Ffilter_molecules.py\nProduction-ready script for batch filtering workflows. Supports multiple input formats (CSV, SDF, SMILES), configurable filter combinations, and detailed reporting.\n\n**Usage:**\n```bash\npython scripts\u002Ffilter_molecules.py input.csv --rules rule_of_five,rule_of_cns --alerts nibr --output filtered.csv\n```\n\n## Documentation\n\nOfficial documentation: https:\u002F\u002Fmedchem-docs.datamol.io\u002F\nGitHub repository: https:\u002F\u002Fgithub.com\u002Fdatamol-io\u002Fmedchem\n","# Medchem\n\n## Overview\n\nMedchem is a Python library for molecular filtering and prioritization in drug discovery workflows. Apply hundreds of well-established and novel molecular filters, structural alerts, and medicinal chemistry rules to efficiently triage and prioritize compound libraries at scale. Rules and filters are context-specific—use as guidelines combined with domain expertise.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Applying drug-likeness rules (Lipinski, Veber, etc.) to compound libraries\n- Filtering molecules by structural alerts or PAINS patterns\n- Prioritizing compounds for lead optimization\n- Assessing compound quality and medicinal chemistry properties\n- Detecting reactive or problematic functional groups\n- Calculating molecular complexity metrics\n\n## Installation\n\n```bash\nuv pip install medchem\n```\n\n## Core Capabilities\n\n### 1. Medicinal Chemistry Rules\n\nApply established drug-likeness rules to molecules using the `medchem.rules` module.\n\n**Available Rules:**\n- Rule of Five (Lipinski)\n- Rule of Oprea\n- Rule of CNS\n- Rule of leadlike (soft and strict)\n- Rule of three\n- Rule of Reos\n- Rule of drug\n- Rule of Veber\n- Golden triangle\n- PAINS filters\n\n**Single Rule Application:**\n\n```python\nimport medchem as mc\n\n# Apply Rule of Five to a SMILES string\nsmiles = \"CC(=O)OC1=CC=CC=C1C(=O)O\"  # Aspirin\npasses = mc.rules.basic_rules.rule_of_five(smiles)\n# Returns: True\n\n# Check specific rules\npasses_oprea = mc.rules.basic_rules.rule_of_oprea(smiles)\npasses_cns = mc.rules.basic_rules.rule_of_cns(smiles)\n```\n\n**Multiple Rules with RuleFilters:**\n\n```python\nimport datamol as dm\nimport medchem as mc\n\n# Load molecules\nmols = [dm.to_mol(smiles) for smiles in smiles_list]\n\n# Create filter with multiple rules\nrfilter = mc.rules.RuleFilters(\n    rule_list=[\n        \"rule_of_five\",\n        \"rule_of_oprea\",\n        \"rule_of_cns\",\n        \"rule_of_leadlike_soft\"\n    ]\n)\n\n# Apply filters with parallelization\nresults = rfilter(\n    mols=mols,\n    n_jobs=-1,  # Use all CPU cores\n    progress=True\n)\n```\n\n**Result Format:**\nResults are returned as dictionaries with pass\u002Ffail status and detailed information for each rule.\n\n### 2. Structural Alert Filters\n\nDetect potentially problematic structural patterns using the `medchem.structural` module.\n\n**Available Filters:**\n\n1. **Common Alerts** - General structural alerts derived from ChEMBL curation and literature\n2. **NIBR Filters** - Novartis Institutes for BioMedical Research filter set\n3. **Lilly Demerits** - Eli Lilly's demerit-based system (275 rules, molecules rejected at >100 demerits)\n\n**Common Alerts:**\n\n```python\nimport medchem as mc\n\n# Create filter\nalert_filter = mc.structural.CommonAlertsFilters()\n\n# Check single molecule\nmol = dm.to_mol(\"c1ccccc1\")\nhas_alerts, details = alert_filter.check_mol(mol)\n\n# Batch filtering with parallelization\nresults = alert_filter(\n    mols=mol_list,\n    n_jobs=-1,\n    progress=True\n)\n```\n\n**NIBR Filters:**\n\n```python\nimport medchem as mc\n\n# Apply NIBR filters\nnibr_filter = mc.structural.NIBRFilters()\nresults = nibr_filter(mols=mol_list, n_jobs=-1)\n```\n\n**Lilly Demerits:**\n\n```python\nimport medchem as mc\n\n# Calculate Lilly demerits\nlilly = mc.structural.LillyDemeritsFilters()\nresults = lilly(mols=mol_list, n_jobs=-1)\n\n# Each result includes demerit score and whether it passes (≤100 demerits)\n```\n\n### 3. Functional API for High-Level Operations\n\nThe `medchem.functional` module provides convenient functions for common workflows.\n\n**Quick Filtering:**\n\n```python\nimport medchem as mc\n\n# Apply NIBR filters to a list\nfilter_ok = mc.functional.nibr_filter(\n    mols=mol_list,\n    n_jobs=-1\n)\n\n# Apply common alerts\nalert_results = mc.functional.common_alerts_filter(\n    mols=mol_list,\n    n_jobs=-1\n)\n```\n\n### 4. Chemical Groups Detection\n\nIdentify specific chemical groups and functional groups using `medchem.groups`.\n\n**Available Groups:**\n- Hinge binders\n- Phosphate binders\n- Michael acceptors\n- Reactive groups\n- Custom SMARTS patterns\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Create group detector\ngroup = mc.groups.ChemicalGroup(groups=[\"hinge_binders\"])\n\n# Check for matches\nhas_matches = group.has_match(mol_list)\n\n# Get detailed match information\nmatches = group.get_matches(mol)\n```\n\n### 5. Named Catalogs\n\nAccess curated collections of chemical structures through `medchem.catalogs`.\n\n**Available Catalogs:**\n- Functional groups\n- Protecting groups\n- Common reagents\n- Standard fragments\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Access named catalogs\ncatalogs = mc.catalogs.NamedCatalogs\n\n# Use catalog for matching\ncatalog = catalogs.get(\"functional_groups\")\nmatches = catalog.get_matches(mol)\n```\n\n### 6. Molecular Complexity\n\nCalculate complexity metrics that approximate synthetic accessibility using `medchem.complexity`.\n\n**Common Metrics:**\n- Bertz complexity\n- Whitlock complexity\n- Barone complexity\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Calculate complexity\ncomplexity_score = mc.complexity.calculate_complexity(mol)\n\n# Filter by complexity threshold\ncomplex_filter = mc.complexity.ComplexityFilter(max_complexity=500)\nresults = complex_filter(mols=mol_list)\n```\n\n### 7. Constraints Filtering\n\nApply custom property-based constraints using `medchem.constraints`.\n\n**Example Constraints:**\n- Molecular weight ranges\n- LogP bounds\n- TPSA limits\n- Rotatable bond counts\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Define constraints\nconstraints = mc.constraints.Constraints(\n    mw_range=(200, 500),\n    logp_range=(-2, 5),\n    tpsa_max=140,\n    rotatable_bonds_max=10\n)\n\n# Apply constraints\nresults = constraints(mols=mol_list, n_jobs=-1)\n```\n\n### 8. Medchem Query Language\n\nUse a specialized query language for complex filtering criteria.\n\n**Query Examples:**\n```\n# Molecules passing Ro5 AND not having common alerts\n\"rule_of_five AND NOT common_alerts\"\n\n# CNS-like molecules with low complexity\n\"rule_of_cns AND complexity \u003C 400\"\n\n# Leadlike molecules without Lilly demerits\n\"rule_of_leadlike AND lilly_demerits == 0\"\n```\n\n**Usage:**\n\n```python\nimport medchem as mc\n\n# Parse and apply query\nquery = mc.query.parse(\"rule_of_five AND NOT common_alerts\")\nresults = query.apply(mols=mol_list, n_jobs=-1)\n```\n\n## Workflow Patterns\n\n### Pattern 1: Initial Triage of Compound Library\n\nFilter a large compound collection to identify drug-like candidates.\n\n```python\nimport datamol as dm\nimport medchem as mc\nimport pandas as pd\n\n# Load compound library\ndf = pd.read_csv(\"compounds.csv\")\nmols = [dm.to_mol(smi) for smi in df[\"smiles\"]]\n\n# Apply primary filters\nrule_filter = mc.rules.RuleFilters(rule_list=[\"rule_of_five\", \"rule_of_veber\"])\nrule_results = rule_filter(mols=mols, n_jobs=-1, progress=True)\n\n# Apply structural alerts\nalert_filter = mc.structural.CommonAlertsFilters()\nalert_results = alert_filter(mols=mols, n_jobs=-1, progress=True)\n\n# Combine results\ndf[\"passes_rules\"] = rule_results[\"pass\"]\ndf[\"has_alerts\"] = alert_results[\"has_alerts\"]\ndf[\"drug_like\"] = df[\"passes_rules\"] & ~df[\"has_alerts\"]\n\n# Save filtered compounds\nfiltered_df = df[df[\"drug_like\"]]\nfiltered_df.to_csv(\"filtered_compounds.csv\", index=False)\n```\n\n### Pattern 2: Lead Optimization Filtering\n\nApply stricter criteria during lead optimization.\n\n```python\nimport medchem as mc\n\n# Create comprehensive filter\nfilters = {\n    \"rules\": mc.rules.RuleFilters(rule_list=[\"rule_of_leadlike_strict\"]),\n    \"alerts\": mc.structural.NIBRFilters(),\n    \"lilly\": mc.structural.LillyDemeritsFilters(),\n    \"complexity\": mc.complexity.ComplexityFilter(max_complexity=400)\n}\n\n# Apply all filters\nresults = {}\nfor name, filt in filters.items():\n    results[name] = filt(mols=candidate_mols, n_jobs=-1)\n\n# Identify compounds passing all filters\npasses_all = all(r[\"pass\"] for r in results.values())\n```\n\n### Pattern 3: Identify Specific Chemical Groups\n\nFind molecules containing specific functional groups or scaffolds.\n\n```python\nimport medchem as mc\n\n# Create group detector for multiple groups\ngroup_detector = mc.groups.ChemicalGroup(\n    groups=[\"hinge_binders\", \"phosphate_binders\"]\n)\n\n# Screen library\nmatches = group_detector.get_all_matches(mol_list)\n\n# Filter molecules with desired groups\nmol_with_groups = [mol for mol, match in zip(mol_list, matches) if match]\n```\n\n## Best Practices\n\n1. **Context Matters**: Don't blindly apply filters. Understand the biological target and chemical space.\n\n2. **Combine Multiple Filters**: Use rules, structural alerts, and domain knowledge together for better decisions.\n\n3. **Use Parallelization**: For large datasets (>1000 molecules), always use `n_jobs=-1` for parallel processing.\n\n4. **Iterative Refinement**: Start with broad filters (Ro5), then apply more specific criteria (CNS, leadlike) as needed.\n\n5. **Document Filtering Decisions**: Track which molecules were filtered out and why for reproducibility.\n\n6. **Validate Results**: Remember that marketed drugs often fail standard filters—use these as guidelines, not absolute rules.\n\n7. **Consider Prodrugs**: Molecules designed as prodrugs may intentionally violate standard medicinal chemistry rules.\n\n## Resources\n\n### references\u002Fapi_guide.md\nComprehensive API reference covering all medchem modules with detailed function signatures, parameters, and return types.\n\n### references\u002Frules_catalog.md\nComplete catalog of available rules, filters, and alerts with descriptions, thresholds, and literature references.\n\n### scripts\u002Ffilter_molecules.py\nProduction-ready script for batch filtering workflows. Supports multiple input formats (CSV, SDF, SMILES), configurable filter combinations, and detailed reporting.\n\n**Usage:**\n```bash\npython scripts\u002Ffilter_molecules.py input.csv --rules rule_of_five,rule_of_cns --alerts nibr --output filtered.csv\n```\n\n## Documentation\n\nOfficial documentation: https:\u002F\u002Fmedchem-docs.datamol.io\u002F\nGitHub repository: https:\u002F\u002Fgithub.com\u002Fdatamol-io\u002Fmedchem\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmedchem",3926,"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","2026-11-12 14:45:56",{"source":8,"skillName":5,"commands":53,"prompt":63,"zipAvailable":64},[54,57,60],{"tool":55,"command":56},"npx","npx skills add davila7\u002Fclaude-code-templates --skill medchem",{"tool":58,"command":59},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill medchem",{"tool":61,"command":62},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill medchem","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmedchem\u002FSKILL.md",true,[66,72,76],{"path":67,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":70,"sourceHtmlUrl":71},"references\u002Fapi_guide.md","REFERENCE","参考文档",12344,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmedchem\u002Freferences\u002Fapi_guide.md",{"path":73,"resourceType":68,"resourceTypeDesc":69,"sizeBytes":74,"sourceHtmlUrl":75},"references\u002Frules_catalog.md",12512,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmedchem\u002Freferences\u002Frules_catalog.md",{"path":77,"resourceType":78,"resourceTypeDesc":79,"sizeBytes":80,"sourceHtmlUrl":81},"scripts\u002Ffilter_molecules.py","SCRIPT","脚本",15696,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fmedchem\u002Fscripts\u002Ffilter_molecules.py",[83,110,136,169,204,228],{"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":37},"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":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[91,92,93,94,95,34,96,97,98,99,100,101,102,103,104,105,106,107,108,109],"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":37},"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":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[118,119,120,121,122,123,29,124,125,126,127,99,128,129,130,131,132,133,134,135],"technical debt","debt","technical","technical debt auditor","auditor","coverage","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":137,"name":138,"nameZh":6,"description":139,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":140,"categories":141,"keywords":151,"publishedAt":37},"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",[142,145,146],{"categoryNo":143,"categoryName":144,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":147,"categoryName":148,"taxonomyType":149,"taxonomyTypeDesc":150},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[152,153,154,155,156,157,158,34,159,160,161,162,163,164,165,98,166,167,99,168],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access","database","query",{"skillNo":170,"name":171,"nameZh":6,"description":172,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":173,"licenseFileUrl":174,"sourceHtmlUrl":175,"categories":176,"keywords":180,"publishedAt":37},"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",[177],{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,34,203],"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":205,"name":206,"nameZh":6,"description":207,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":173,"licenseFileUrl":208,"sourceHtmlUrl":209,"categories":210,"keywords":215,"publishedAt":37},"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",[211,214],{"categoryNo":212,"categoryName":213,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":178,"categoryName":179,"taxonomyType":21,"taxonomyTypeDesc":22},[216,217,218,219,220,182,221,191,192,222,223,224,225,34,226,101,227],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":229,"name":230,"nameZh":6,"description":231,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":173,"licenseFileUrl":232,"sourceHtmlUrl":233,"categories":234,"keywords":242,"publishedAt":37},"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",[235,238,239],{"categoryNo":236,"categoryName":237,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":240,"categoryName":241,"taxonomyType":149,"taxonomyTypeDesc":150},"01JBSEEDCAT000000000000036","内容创作者",[243,244,245,246,247,248,249,250,184,251,194,128,252,253,254,255,256,164,257,99,258,259,260,261,262,107,263],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]