[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFMFHZ8RM5BC34RHQ8GZ1Z":3,"skill-same:01M6AFMFHZ8RM5BC34RHQ8GZ1Z":89},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":28,"publishedAt":48,"linkOnly":49,"markdownContent":50,"markdownBody":51,"frontMatter":52,"refName":53,"commitSha":54,"skillDirectory":55,"forksCount":56,"repositoryHtmlUrl":57,"repositoryHomepageUrl":58,"ownerHtmlUrl":59,"ownerBlogUrl":60,"ownerDescription":61,"updatedAt":62,"install":63,"resources":76},"01M6AFMFHZ8RM5BC34RHQ8GZ1Z","reactome-database",null,"Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.","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\u002Freactome-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":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000030","开发工程师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47],"reactome database","Reactome","database","systems biology","gene","for","biology","pathways","mapping","pathway","expression","systems","rest api","molecular","query","api","analysis","REST","enrichment","2026-11-10 10:12:30",false,"---\nname: reactome-database\ndescription: \"Query Reactome REST API for pathway analysis, enrichment, gene-pathway mapping, disease pathways, molecular interactions, expression analysis, for systems biology studies.\"\n---\n\n# Reactome Database\n\n## Overview\n\nReactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Performing pathway enrichment analysis on gene or protein lists\n- Analyzing gene expression data to identify relevant biological pathways\n- Querying specific pathway information, reactions, or molecular interactions\n- Mapping genes or proteins to biological pathways and processes\n- Exploring disease-related pathways and mechanisms\n- Visualizing analysis results in the Reactome Pathway Browser\n- Conducting comparative pathway analysis across species\n\n## Core Capabilities\n\nReactome provides two main API services and a Python client library:\n\n### 1. Content Service - Data Retrieval\n\nQuery and retrieve biological pathway data, molecular interactions, and entity information.\n\n**Common operations:**\n- Retrieve pathway information and hierarchies\n- Query specific entities (proteins, reactions, complexes)\n- Get participating molecules in pathways\n- Access database version and metadata\n- Explore pathway compartments and locations\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FContentService`\n\n### 2. Analysis Service - Pathway Analysis\n\nPerform computational analysis on gene lists and expression data.\n\n**Analysis types:**\n- **Overrepresentation Analysis**: Identify statistically significant pathways from gene\u002Fprotein lists\n- **Expression Data Analysis**: Analyze gene expression datasets to find relevant pathways\n- **Species Comparison**: Compare pathway data across different organisms\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FAnalysisService`\n\n### 3. reactome2py Python Package\n\nPython client library that wraps Reactome API calls for easier programmatic access.\n\n**Installation:**\n```bash\nuv pip install reactome2py\n```\n\n**Note:** The reactome2py package (version 3.0.0, released January 2021) is functional but not actively maintained. For the most up-to-date functionality, consider using direct REST API calls.\n\n## Querying Pathway Data\n\n### Using Content Service REST API\n\nThe Content Service uses REST protocol and returns data in JSON or plain text formats.\n\n**Get database version:**\n```python\nimport requests\n\nresponse = requests.get(\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fdatabase\u002Fversion\")\nversion = response.text\nprint(f\"Reactome version: {version}\")\n```\n\n**Query a specific entity:**\n```python\nimport requests\n\nentity_id = \"R-HSA-69278\"  # Example pathway ID\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fquery\u002F{entity_id}\")\ndata = response.json()\n```\n\n**Get participating molecules in a pathway:**\n```python\nimport requests\n\nevent_id = \"R-HSA-69278\"\nresponse = requests.get(\n    f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fevent\u002F{event_id}\u002FparticipatingPhysicalEntities\"\n)\nmolecules = response.json()\n```\n\n### Using reactome2py Package\n\n```python\nimport reactome2py\nfrom reactome2py import content\n\n# Query pathway information\npathway_info = content.query_by_id(\"R-HSA-69278\")\n\n# Get database version\nversion = content.get_database_version()\n```\n\n**For detailed API endpoints and parameters**, refer to `references\u002Fapi_reference.md` in this skill.\n\n## Performing Pathway Analysis\n\n### Overrepresentation Analysis\n\nSubmit a list of gene\u002Fprotein identifiers to find enriched pathways.\n\n**Using REST API:**\n```python\nimport requests\n\n# Prepare identifier list\nidentifiers = [\"TP53\", \"BRCA1\", \"EGFR\", \"MYC\"]\ndata = \"\\n\".join(identifiers)\n\n# Submit analysis\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\ntoken = result[\"summary\"][\"token\"]  # Save token to retrieve results later\n\n# Access pathways\nfor pathway in result[\"pathways\"]:\n    print(f\"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})\")\n```\n\n**Retrieve analysis by token:**\n```python\n# Token is valid for 7 days\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Ftoken\u002F{token}\")\nresults = response.json()\n```\n\n### Expression Data Analysis\n\nAnalyze gene expression datasets with quantitative values.\n\n**Input format (TSV with header starting with #):**\n```\n#Gene\tSample1\tSample2\tSample3\nTP53\t2.5\t3.1\t2.8\nBRCA1\t1.2\t1.5\t1.3\nEGFR\t4.5\t4.2\t4.8\n```\n\n**Submit expression data:**\n```python\nimport requests\n\n# Read TSV file\nwith open(\"expression_data.tsv\", \"r\") as f:\n    data = f.read()\n\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\n```\n\n### Species Projection\n\nMap identifiers to human pathways exclusively using the `\u002Fprojection\u002F` endpoint:\n\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002Fprojection\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n```\n\n## Visualizing Results\n\nAnalysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:\n\n```python\ntoken = result[\"summary\"][\"token\"]\npathway_id = \"R-HSA-69278\"\nurl = f\"https:\u002F\u002Freactome.org\u002FPathwayBrowser\u002F#{pathway_id}&DTAB=AN&ANALYSIS={token}\"\nprint(f\"View results: {url}\")\n```\n\n## Working with Analysis Tokens\n\n- Analysis tokens are valid for **7 days**\n- Tokens allow retrieval of previously computed results without re-submission\n- Store tokens to access results across sessions\n- Use `GET \u002Ftoken\u002F{TOKEN}` endpoint to retrieve results\n\n## Data Formats and Identifiers\n\n### Supported Identifier Types\n\nReactome accepts various identifier formats:\n- UniProt accessions (e.g., P04637)\n- Gene symbols (e.g., TP53)\n- Ensembl IDs (e.g., ENSG00000141510)\n- EntrezGene IDs (e.g., 7157)\n- ChEBI IDs for small molecules\n\nThe system automatically detects identifier types.\n\n### Input Format Requirements\n\n**For overrepresentation analysis:**\n- Plain text list of identifiers (one per line)\n- OR single column in TSV format\n\n**For expression analysis:**\n- TSV format with mandatory header row starting with \"#\"\n- Column 1: identifiers\n- Columns 2+: numeric expression values\n- Use period (.) as decimal separator\n\n### Output Format\n\nAll API responses return JSON containing:\n- `pathways`: Array of enriched pathways with statistical metrics\n- `summary`: Analysis metadata and token\n- `entities`: Matched and unmapped identifiers\n- Statistical values: pValue, FDR (false discovery rate)\n\n## Helper Scripts\n\nThis skill includes `scripts\u002Freactome_query.py`, a helper script for common Reactome operations:\n\n```bash\n# Query pathway information\npython scripts\u002Freactome_query.py query R-HSA-69278\n\n# Perform overrepresentation analysis\npython scripts\u002Freactome_query.py analyze gene_list.txt\n\n# Get database version\npython scripts\u002Freactome_query.py version\n```\n\n## Additional Resources\n\n- **API Documentation**: https:\u002F\u002Freactome.org\u002Fdev\n- **User Guide**: https:\u002F\u002Freactome.org\u002Fuserguide\n- **Documentation Portal**: https:\u002F\u002Freactome.org\u002Fdocumentation\n- **Data Downloads**: https:\u002F\u002Freactome.org\u002Fdownload-data\n- **reactome2py Docs**: https:\u002F\u002Freactome.github.io\u002Freactome2py\u002F\n\nFor comprehensive API endpoint documentation, see `references\u002Fapi_reference.md` in this skill.\n\n## Current Database Statistics (Version 94, September 2025)\n\n- 2,825 human pathways\n- 16,002 reactions\n- 11,630 proteins\n- 2,176 small molecules\n- 1,070 drugs\n- 41,373 literature references\n","# Reactome Database\n\n## Overview\n\nReactome is a free, open-source, curated pathway database with 2,825+ human pathways. Query biological pathways, perform overrepresentation and expression analysis, map genes to pathways, explore molecular interactions via REST API and Python client for systems biology research.\n\n## When to Use This Skill\n\nThis skill should be used when:\n- Performing pathway enrichment analysis on gene or protein lists\n- Analyzing gene expression data to identify relevant biological pathways\n- Querying specific pathway information, reactions, or molecular interactions\n- Mapping genes or proteins to biological pathways and processes\n- Exploring disease-related pathways and mechanisms\n- Visualizing analysis results in the Reactome Pathway Browser\n- Conducting comparative pathway analysis across species\n\n## Core Capabilities\n\nReactome provides two main API services and a Python client library:\n\n### 1. Content Service - Data Retrieval\n\nQuery and retrieve biological pathway data, molecular interactions, and entity information.\n\n**Common operations:**\n- Retrieve pathway information and hierarchies\n- Query specific entities (proteins, reactions, complexes)\n- Get participating molecules in pathways\n- Access database version and metadata\n- Explore pathway compartments and locations\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FContentService`\n\n### 2. Analysis Service - Pathway Analysis\n\nPerform computational analysis on gene lists and expression data.\n\n**Analysis types:**\n- **Overrepresentation Analysis**: Identify statistically significant pathways from gene\u002Fprotein lists\n- **Expression Data Analysis**: Analyze gene expression datasets to find relevant pathways\n- **Species Comparison**: Compare pathway data across different organisms\n\n**API Base URL:** `https:\u002F\u002Freactome.org\u002FAnalysisService`\n\n### 3. reactome2py Python Package\n\nPython client library that wraps Reactome API calls for easier programmatic access.\n\n**Installation:**\n```bash\nuv pip install reactome2py\n```\n\n**Note:** The reactome2py package (version 3.0.0, released January 2021) is functional but not actively maintained. For the most up-to-date functionality, consider using direct REST API calls.\n\n## Querying Pathway Data\n\n### Using Content Service REST API\n\nThe Content Service uses REST protocol and returns data in JSON or plain text formats.\n\n**Get database version:**\n```python\nimport requests\n\nresponse = requests.get(\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fdatabase\u002Fversion\")\nversion = response.text\nprint(f\"Reactome version: {version}\")\n```\n\n**Query a specific entity:**\n```python\nimport requests\n\nentity_id = \"R-HSA-69278\"  # Example pathway ID\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fquery\u002F{entity_id}\")\ndata = response.json()\n```\n\n**Get participating molecules in a pathway:**\n```python\nimport requests\n\nevent_id = \"R-HSA-69278\"\nresponse = requests.get(\n    f\"https:\u002F\u002Freactome.org\u002FContentService\u002Fdata\u002Fevent\u002F{event_id}\u002FparticipatingPhysicalEntities\"\n)\nmolecules = response.json()\n```\n\n### Using reactome2py Package\n\n```python\nimport reactome2py\nfrom reactome2py import content\n\n# Query pathway information\npathway_info = content.query_by_id(\"R-HSA-69278\")\n\n# Get database version\nversion = content.get_database_version()\n```\n\n**For detailed API endpoints and parameters**, refer to `references\u002Fapi_reference.md` in this skill.\n\n## Performing Pathway Analysis\n\n### Overrepresentation Analysis\n\nSubmit a list of gene\u002Fprotein identifiers to find enriched pathways.\n\n**Using REST API:**\n```python\nimport requests\n\n# Prepare identifier list\nidentifiers = [\"TP53\", \"BRCA1\", \"EGFR\", \"MYC\"]\ndata = \"\\n\".join(identifiers)\n\n# Submit analysis\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\ntoken = result[\"summary\"][\"token\"]  # Save token to retrieve results later\n\n# Access pathways\nfor pathway in result[\"pathways\"]:\n    print(f\"{pathway['stId']}: {pathway['name']} (p-value: {pathway['entities']['pValue']})\")\n```\n\n**Retrieve analysis by token:**\n```python\n# Token is valid for 7 days\nresponse = requests.get(f\"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Ftoken\u002F{token}\")\nresults = response.json()\n```\n\n### Expression Data Analysis\n\nAnalyze gene expression datasets with quantitative values.\n\n**Input format (TSV with header starting with #):**\n```\n#Gene\tSample1\tSample2\tSample3\nTP53\t2.5\t3.1\t2.8\nBRCA1\t1.2\t1.5\t1.3\nEGFR\t4.5\t4.2\t4.8\n```\n\n**Submit expression data:**\n```python\nimport requests\n\n# Read TSV file\nwith open(\"expression_data.tsv\", \"r\") as f:\n    data = f.read()\n\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n\nresult = response.json()\n```\n\n### Species Projection\n\nMap identifiers to human pathways exclusively using the `\u002Fprojection\u002F` endpoint:\n\n```python\nresponse = requests.post(\n    \"https:\u002F\u002Freactome.org\u002FAnalysisService\u002Fidentifiers\u002Fprojection\u002F\",\n    headers={\"Content-Type\": \"text\u002Fplain\"},\n    data=data\n)\n```\n\n## Visualizing Results\n\nAnalysis results can be visualized in the Reactome Pathway Browser by constructing URLs with the analysis token:\n\n```python\ntoken = result[\"summary\"][\"token\"]\npathway_id = \"R-HSA-69278\"\nurl = f\"https:\u002F\u002Freactome.org\u002FPathwayBrowser\u002F#{pathway_id}&DTAB=AN&ANALYSIS={token}\"\nprint(f\"View results: {url}\")\n```\n\n## Working with Analysis Tokens\n\n- Analysis tokens are valid for **7 days**\n- Tokens allow retrieval of previously computed results without re-submission\n- Store tokens to access results across sessions\n- Use `GET \u002Ftoken\u002F{TOKEN}` endpoint to retrieve results\n\n## Data Formats and Identifiers\n\n### Supported Identifier Types\n\nReactome accepts various identifier formats:\n- UniProt accessions (e.g., P04637)\n- Gene symbols (e.g., TP53)\n- Ensembl IDs (e.g., ENSG00000141510)\n- EntrezGene IDs (e.g., 7157)\n- ChEBI IDs for small molecules\n\nThe system automatically detects identifier types.\n\n### Input Format Requirements\n\n**For overrepresentation analysis:**\n- Plain text list of identifiers (one per line)\n- OR single column in TSV format\n\n**For expression analysis:**\n- TSV format with mandatory header row starting with \"#\"\n- Column 1: identifiers\n- Columns 2+: numeric expression values\n- Use period (.) as decimal separator\n\n### Output Format\n\nAll API responses return JSON containing:\n- `pathways`: Array of enriched pathways with statistical metrics\n- `summary`: Analysis metadata and token\n- `entities`: Matched and unmapped identifiers\n- Statistical values: pValue, FDR (false discovery rate)\n\n## Helper Scripts\n\nThis skill includes `scripts\u002Freactome_query.py`, a helper script for common Reactome operations:\n\n```bash\n# Query pathway information\npython scripts\u002Freactome_query.py query R-HSA-69278\n\n# Perform overrepresentation analysis\npython scripts\u002Freactome_query.py analyze gene_list.txt\n\n# Get database version\npython scripts\u002Freactome_query.py version\n```\n\n## Additional Resources\n\n- **API Documentation**: https:\u002F\u002Freactome.org\u002Fdev\n- **User Guide**: https:\u002F\u002Freactome.org\u002Fuserguide\n- **Documentation Portal**: https:\u002F\u002Freactome.org\u002Fdocumentation\n- **Data Downloads**: https:\u002F\u002Freactome.org\u002Fdownload-data\n- **reactome2py Docs**: https:\u002F\u002Freactome.github.io\u002Freactome2py\u002F\n\nFor comprehensive API endpoint documentation, see `references\u002Fapi_reference.md` in this skill.\n\n## Current Database Statistics (Version 94, September 2025)\n\n- 2,825 human pathways\n- 16,002 reactions\n- 11,630 proteins\n- 2,176 small molecules\n- 1,070 drugs\n- 41,373 literature references\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Freactome-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":64,"prompt":74,"zipAvailable":75},[65,68,71],{"tool":66,"command":67},"npx","npx skills add davila7\u002Fclaude-code-templates --skill reactome-database",{"tool":69,"command":70},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill reactome-database",{"tool":72,"command":73},"pnpm","pnpm dlx skills 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reactome-database","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Freactome-database\u002FSKILL.md",true,[77,83],{"path":78,"resourceType":79,"resourceTypeDesc":80,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fapi_reference.md","REFERENCE","参考文档",10507,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Freactome-database\u002Freferences\u002Fapi_reference.md",{"path":84,"resourceType":85,"resourceTypeDesc":86,"sizeBytes":87,"sourceHtmlUrl":88},"scripts\u002Freactome_query.py","SCRIPT","脚本",9254,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Freactome-database\u002Fscripts\u002Freactome_query.py",[90,119,146,173,208,232],{"skillNo":91,"name":92,"nameZh":6,"description":93,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":94,"categories":95,"keywords":99,"publishedAt":48},"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",[96],{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发",[100,101,102,103,104,34,105,106,107,108,109,110,111,112,113,114,115,116,117,118],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","use","when","reduce","with","record","track","verdict","evm","solana","how","public","look",{"skillNo":120,"name":121,"nameZh":6,"description":122,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":123,"categories":124,"keywords":126,"publishedAt":48},"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",[125],{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},[127,128,129,130,131,132,133,134,135,136,137,108,138,139,140,141,142,143,144,145],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","work","plan","focus","signals","audit","dependency",{"skillNo":147,"name":148,"nameZh":6,"description":149,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":150,"categories":151,"keywords":157,"publishedAt":48},"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",[152,153,154],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":155,"categoryName":156,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000033","数据分析师",[158,159,160,161,162,163,164,34,165,166,167,168,169,170,171,107,172,31,108,43],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","access",{"skillNo":174,"name":175,"nameZh":6,"description":176,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":177,"licenseFileUrl":178,"sourceHtmlUrl":179,"categories":180,"keywords":184,"publishedAt":48},"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",[181],{"categoryNo":182,"categoryName":183,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,34,207],"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":209,"name":210,"nameZh":6,"description":211,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":177,"licenseFileUrl":212,"sourceHtmlUrl":213,"categories":214,"keywords":219,"publishedAt":48},"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",[215,218],{"categoryNo":216,"categoryName":217,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":182,"categoryName":183,"taxonomyType":21,"taxonomyTypeDesc":22},[220,221,222,223,224,186,225,195,196,226,227,228,229,34,230,110,231],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":233,"name":234,"nameZh":6,"description":235,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":177,"licenseFileUrl":236,"sourceHtmlUrl":237,"categories":238,"keywords":246,"publishedAt":48},"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",[239,242,243],{"categoryNo":240,"categoryName":241,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":97,"categoryName":98,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":244,"categoryName":245,"taxonomyType":26,"taxonomyTypeDesc":27},"01JBSEEDCAT000000000000036","内容创作者",[247,248,249,250,251,252,253,254,188,255,198,138,256,257,258,259,260,170,261,108,262,45,263,264,265,116,266],"discernment nudge","nudge","interpretation","help","run","reasoning","data","writing","lookup","content","review","conversation","short","up","invoke","you","claims","argument","assemble","recommendations"]