[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M6AFC51VYX2FNMT8W2K572RX":3,"skill-same:01M6AFC51VYX2FNMT8W2K572RX":91},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":23,"publishedAt":44,"linkOnly":45,"markdownContent":46,"markdownBody":47,"frontMatter":48,"refName":49,"commitSha":50,"skillDirectory":51,"forksCount":52,"repositoryHtmlUrl":53,"repositoryHomepageUrl":54,"ownerHtmlUrl":55,"ownerBlogUrl":56,"ownerDescription":57,"updatedAt":58,"install":59,"resources":72},"01M6AFC51VYX2FNMT8W2K572RX","datacommons-client",null,"Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.","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\u002Fdatacommons-client\u002FSKILL.md",[18],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43],"datacommons client","client","resolution","entities","when","for","programmatic","figures","with","sources","platform","data","statistics","relationships","datasets","use","work","access","health","indicators","2026-11-10 10:12:30",false,"---\nname: datacommons-client\ndescription: Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.\n---\n\n# Data Commons Client\n\n## Overview\n\nProvides comprehensive access to the Data Commons Python API v2 for querying statistical observations, exploring the knowledge graph, and resolving entity identifiers. Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.\n\n## Installation\n\nInstall the Data Commons Python client with Pandas support:\n\n```bash\nuv pip install \"datacommons-client[Pandas]\"\n```\n\nFor basic usage without Pandas:\n```bash\nuv pip install datacommons-client\n```\n\n## Core Capabilities\n\nThe Data Commons API consists of three main endpoints, each detailed in dedicated reference files:\n\n### 1. Observation Endpoint - Statistical Data Queries\n\nQuery time-series statistical data for entities. See `references\u002Fobservation.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Retrieve population, economic, health, or environmental statistics\n- Access historical time-series data for trend analysis\n- Query data for hierarchies (all counties in a state, all countries in a region)\n- Compare statistics across multiple entities\n- Filter by data source for consistency\n\n**Common patterns:**\n```python\nfrom datacommons_client import DataCommonsClient\n\nclient = DataCommonsClient()\n\n# Get latest population data\nresponse = client.observation.fetch(\n    variable_dcids=[\"Count_Person\"],\n    entity_dcids=[\"geoId\u002F06\"],  # California\n    date=\"latest\"\n)\n\n# Get time series\nresponse = client.observation.fetch(\n    variable_dcids=[\"UnemploymentRate_Person\"],\n    entity_dcids=[\"country\u002FUSA\"],\n    date=\"all\"\n)\n\n# Query by hierarchy\nresponse = client.observation.fetch(\n    variable_dcids=[\"MedianIncome_Household\"],\n    entity_expression=\"geoId\u002F06\u003C-containedInPlace+{typeOf:County}\",\n    date=\"2020\"\n)\n```\n\n### 2. Node Endpoint - Knowledge Graph Exploration\n\nExplore entity relationships and properties within the knowledge graph. See `references\u002Fnode.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Discover available properties for entities\n- Navigate geographic hierarchies (parent\u002Fchild relationships)\n- Retrieve entity names and metadata\n- Explore connections between entities\n- List all entity types in the graph\n\n**Common patterns:**\n```python\n# Discover properties\nlabels = client.node.fetch_property_labels(\n    node_dcids=[\"geoId\u002F06\"],\n    out=True\n)\n\n# Navigate hierarchy\nchildren = client.node.fetch_place_children(\n    node_dcids=[\"country\u002FUSA\"]\n)\n\n# Get entity names\nnames = client.node.fetch_entity_names(\n    node_dcids=[\"geoId\u002F06\", \"geoId\u002F48\"]\n)\n```\n\n### 3. Resolve Endpoint - Entity Identification\n\nTranslate entity names, coordinates, or external IDs into Data Commons IDs (DCIDs). See `references\u002Fresolve.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Convert place names to DCIDs for queries\n- Resolve coordinates to places\n- Map Wikidata IDs to Data Commons entities\n- Handle ambiguous entity names\n\n**Common patterns:**\n```python\n# Resolve by name\nresponse = client.resolve.fetch_dcids_by_name(\n    names=[\"California\", \"Texas\"],\n    entity_type=\"State\"\n)\n\n# Resolve by coordinates\ndcid = client.resolve.fetch_dcid_by_coordinates(\n    latitude=37.7749,\n    longitude=-122.4194\n)\n\n# Resolve Wikidata IDs\nresponse = client.resolve.fetch_dcids_by_wikidata_id(\n    wikidata_ids=[\"Q30\", \"Q99\"]\n)\n```\n\n## Typical Workflow\n\nMost Data Commons queries follow this pattern:\n\n1. **Resolve entities** (if starting with names):\n   ```python\n   resolve_response = client.resolve.fetch_dcids_by_name(\n       names=[\"California\", \"Texas\"]\n   )\n   dcids = [r[\"candidates\"][0][\"dcid\"]\n            for r in resolve_response.to_dict().values()\n            if r[\"candidates\"]]\n   ```\n\n2. **Discover available variables** (optional):\n   ```python\n   variables = client.observation.fetch_available_statistical_variables(\n       entity_dcids=dcids\n   )\n   ```\n\n3. **Query statistical data**:\n   ```python\n   response = client.observation.fetch(\n       variable_dcids=[\"Count_Person\", \"UnemploymentRate_Person\"],\n       entity_dcids=dcids,\n       date=\"latest\"\n   )\n   ```\n\n4. **Process results**:\n   ```python\n   # As dictionary\n   data = response.to_dict()\n\n   # As Pandas DataFrame\n   df = response.to_observations_as_records()\n   ```\n\n## Finding Statistical Variables\n\nStatistical variables use specific naming patterns in Data Commons:\n\n**Common variable patterns:**\n- `Count_Person` - Total population\n- `Count_Person_Female` - Female population\n- `UnemploymentRate_Person` - Unemployment rate\n- `Median_Income_Household` - Median household income\n- `Count_Death` - Death count\n- `Median_Age_Person` - Median age\n\n**Discovery methods:**\n```python\n# Check what variables are available for an entity\navailable = client.observation.fetch_available_statistical_variables(\n    entity_dcids=[\"geoId\u002F06\"]\n)\n\n# Or explore via the web interface\n# https:\u002F\u002Fdatacommons.org\u002Ftools\u002Fstatvar\n```\n\n## Working with Pandas\n\nAll observation responses integrate with Pandas:\n\n```python\nresponse = client.observation.fetch(\n    variable_dcids=[\"Count_Person\"],\n    entity_dcids=[\"geoId\u002F06\", \"geoId\u002F48\"],\n    date=\"all\"\n)\n\n# Convert to DataFrame\ndf = response.to_observations_as_records()\n# Columns: date, entity, variable, value\n\n# Reshape for analysis\npivot = df.pivot_table(\n    values='value',\n    index='date',\n    columns='entity'\n)\n```\n\n## API Authentication\n\n**For datacommons.org (default):**\n- An API key is required\n- Set via environment variable: `export DC_API_KEY=\"your_key\"`\n- Or pass when initializing: `client = DataCommonsClient(api_key=\"your_key\")`\n- Request keys at: https:\u002F\u002Fapikeys.datacommons.org\u002F\n\n**For custom Data Commons instances:**\n- No API key required\n- Specify custom endpoint: `client = DataCommonsClient(url=\"https:\u002F\u002Fcustom.datacommons.org\")`\n\n## Reference Documentation\n\nComprehensive documentation for each endpoint is available in the `references\u002F` directory:\n\n- **`references\u002Fobservation.md`**: Complete Observation API documentation with all methods, parameters, response formats, and common use cases\n- **`references\u002Fnode.md`**: Complete Node API documentation for graph exploration, property queries, and hierarchy navigation\n- **`references\u002Fresolve.md`**: Complete Resolve API documentation for entity identification and DCID resolution\n- **`references\u002Fgetting_started.md`**: Quickstart guide with end-to-end examples and common patterns\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.datacommons.org\u002Fapi\u002Fpython\u002Fv2\u002F\n- **Statistical Variable Explorer**: https:\u002F\u002Fdatacommons.org\u002Ftools\u002Fstatvar\n- **Data Commons Browser**: https:\u002F\u002Fdatacommons.org\u002Fbrowser\u002F\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Fdatacommonsorg\u002Fapi-python\n\n## Tips for Effective Use\n\n1. **Always start with resolution**: Convert names to DCIDs before querying data\n2. **Use relation expressions for hierarchies**: Query all children at once instead of individual queries\n3. **Check data availability first**: Use `fetch_available_statistical_variables()` to see what's queryable\n4. **Leverage Pandas integration**: Convert responses to DataFrames for analysis\n5. **Cache resolutions**: If querying the same entities repeatedly, store name→DCID mappings\n6. **Filter by facet for consistency**: Use `filter_facet_domains` to ensure data from the same source\n7. **Read reference docs**: Each endpoint has extensive documentation in the `references\u002F` directory\n","# Data Commons Client\n\n## Overview\n\nProvides comprehensive access to the Data Commons Python API v2 for querying statistical observations, exploring the knowledge graph, and resolving entity identifiers. Data Commons aggregates data from census bureaus, health organizations, environmental agencies, and other authoritative sources into a unified knowledge graph.\n\n## Installation\n\nInstall the Data Commons Python client with Pandas support:\n\n```bash\nuv pip install \"datacommons-client[Pandas]\"\n```\n\nFor basic usage without Pandas:\n```bash\nuv pip install datacommons-client\n```\n\n## Core Capabilities\n\nThe Data Commons API consists of three main endpoints, each detailed in dedicated reference files:\n\n### 1. Observation Endpoint - Statistical Data Queries\n\nQuery time-series statistical data for entities. See `references\u002Fobservation.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Retrieve population, economic, health, or environmental statistics\n- Access historical time-series data for trend analysis\n- Query data for hierarchies (all counties in a state, all countries in a region)\n- Compare statistics across multiple entities\n- Filter by data source for consistency\n\n**Common patterns:**\n```python\nfrom datacommons_client import DataCommonsClient\n\nclient = DataCommonsClient()\n\n# Get latest population data\nresponse = client.observation.fetch(\n    variable_dcids=[\"Count_Person\"],\n    entity_dcids=[\"geoId\u002F06\"],  # California\n    date=\"latest\"\n)\n\n# Get time series\nresponse = client.observation.fetch(\n    variable_dcids=[\"UnemploymentRate_Person\"],\n    entity_dcids=[\"country\u002FUSA\"],\n    date=\"all\"\n)\n\n# Query by hierarchy\nresponse = client.observation.fetch(\n    variable_dcids=[\"MedianIncome_Household\"],\n    entity_expression=\"geoId\u002F06\u003C-containedInPlace+{typeOf:County}\",\n    date=\"2020\"\n)\n```\n\n### 2. Node Endpoint - Knowledge Graph Exploration\n\nExplore entity relationships and properties within the knowledge graph. See `references\u002Fnode.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Discover available properties for entities\n- Navigate geographic hierarchies (parent\u002Fchild relationships)\n- Retrieve entity names and metadata\n- Explore connections between entities\n- List all entity types in the graph\n\n**Common patterns:**\n```python\n# Discover properties\nlabels = client.node.fetch_property_labels(\n    node_dcids=[\"geoId\u002F06\"],\n    out=True\n)\n\n# Navigate hierarchy\nchildren = client.node.fetch_place_children(\n    node_dcids=[\"country\u002FUSA\"]\n)\n\n# Get entity names\nnames = client.node.fetch_entity_names(\n    node_dcids=[\"geoId\u002F06\", \"geoId\u002F48\"]\n)\n```\n\n### 3. Resolve Endpoint - Entity Identification\n\nTranslate entity names, coordinates, or external IDs into Data Commons IDs (DCIDs). See `references\u002Fresolve.md` for comprehensive documentation.\n\n**Primary use cases:**\n- Convert place names to DCIDs for queries\n- Resolve coordinates to places\n- Map Wikidata IDs to Data Commons entities\n- Handle ambiguous entity names\n\n**Common patterns:**\n```python\n# Resolve by name\nresponse = client.resolve.fetch_dcids_by_name(\n    names=[\"California\", \"Texas\"],\n    entity_type=\"State\"\n)\n\n# Resolve by coordinates\ndcid = client.resolve.fetch_dcid_by_coordinates(\n    latitude=37.7749,\n    longitude=-122.4194\n)\n\n# Resolve Wikidata IDs\nresponse = client.resolve.fetch_dcids_by_wikidata_id(\n    wikidata_ids=[\"Q30\", \"Q99\"]\n)\n```\n\n## Typical Workflow\n\nMost Data Commons queries follow this pattern:\n\n1. **Resolve entities** (if starting with names):\n   ```python\n   resolve_response = client.resolve.fetch_dcids_by_name(\n       names=[\"California\", \"Texas\"]\n   )\n   dcids = [r[\"candidates\"][0][\"dcid\"]\n            for r in resolve_response.to_dict().values()\n            if r[\"candidates\"]]\n   ```\n\n2. **Discover available variables** (optional):\n   ```python\n   variables = client.observation.fetch_available_statistical_variables(\n       entity_dcids=dcids\n   )\n   ```\n\n3. **Query statistical data**:\n   ```python\n   response = client.observation.fetch(\n       variable_dcids=[\"Count_Person\", \"UnemploymentRate_Person\"],\n       entity_dcids=dcids,\n       date=\"latest\"\n   )\n   ```\n\n4. **Process results**:\n   ```python\n   # As dictionary\n   data = response.to_dict()\n\n   # As Pandas DataFrame\n   df = response.to_observations_as_records()\n   ```\n\n## Finding Statistical Variables\n\nStatistical variables use specific naming patterns in Data Commons:\n\n**Common variable patterns:**\n- `Count_Person` - Total population\n- `Count_Person_Female` - Female population\n- `UnemploymentRate_Person` - Unemployment rate\n- `Median_Income_Household` - Median household income\n- `Count_Death` - Death count\n- `Median_Age_Person` - Median age\n\n**Discovery methods:**\n```python\n# Check what variables are available for an entity\navailable = client.observation.fetch_available_statistical_variables(\n    entity_dcids=[\"geoId\u002F06\"]\n)\n\n# Or explore via the web interface\n# https:\u002F\u002Fdatacommons.org\u002Ftools\u002Fstatvar\n```\n\n## Working with Pandas\n\nAll observation responses integrate with Pandas:\n\n```python\nresponse = client.observation.fetch(\n    variable_dcids=[\"Count_Person\"],\n    entity_dcids=[\"geoId\u002F06\", \"geoId\u002F48\"],\n    date=\"all\"\n)\n\n# Convert to DataFrame\ndf = response.to_observations_as_records()\n# Columns: date, entity, variable, value\n\n# Reshape for analysis\npivot = df.pivot_table(\n    values='value',\n    index='date',\n    columns='entity'\n)\n```\n\n## API Authentication\n\n**For datacommons.org (default):**\n- An API key is required\n- Set via environment variable: `export DC_API_KEY=\"your_key\"`\n- Or pass when initializing: `client = DataCommonsClient(api_key=\"your_key\")`\n- Request keys at: https:\u002F\u002Fapikeys.datacommons.org\u002F\n\n**For custom Data Commons instances:**\n- No API key required\n- Specify custom endpoint: `client = DataCommonsClient(url=\"https:\u002F\u002Fcustom.datacommons.org\")`\n\n## Reference Documentation\n\nComprehensive documentation for each endpoint is available in the `references\u002F` directory:\n\n- **`references\u002Fobservation.md`**: Complete Observation API documentation with all methods, parameters, response formats, and common use cases\n- **`references\u002Fnode.md`**: Complete Node API documentation for graph exploration, property queries, and hierarchy navigation\n- **`references\u002Fresolve.md`**: Complete Resolve API documentation for entity identification and DCID resolution\n- **`references\u002Fgetting_started.md`**: Quickstart guide with end-to-end examples and common patterns\n\n## Additional Resources\n\n- **Official Documentation**: https:\u002F\u002Fdocs.datacommons.org\u002Fapi\u002Fpython\u002Fv2\u002F\n- **Statistical Variable Explorer**: https:\u002F\u002Fdatacommons.org\u002Ftools\u002Fstatvar\n- **Data Commons Browser**: https:\u002F\u002Fdatacommons.org\u002Fbrowser\u002F\n- **GitHub Repository**: https:\u002F\u002Fgithub.com\u002Fdatacommonsorg\u002Fapi-python\n\n## Tips for Effective Use\n\n1. **Always start with resolution**: Convert names to DCIDs before querying data\n2. **Use relation expressions for hierarchies**: Query all children at once instead of individual queries\n3. **Check data availability first**: Use `fetch_available_statistical_variables()` to see what's queryable\n4. **Leverage Pandas integration**: Convert responses to DataFrames for analysis\n5. **Cache resolutions**: If querying the same entities repeatedly, store name→DCID mappings\n6. **Filter by facet for consistency**: Use `filter_facet_domains` to ensure data from the same source\n7. **Read reference docs**: Each endpoint has extensive documentation in the `references\u002F` directory\n",{"name":5,"description":7},"main","2b749c82b5d11fac39d9a6e021ce3f5b8a541bd1","cli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdatacommons-client",3931,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates","https:\u002F\u002Faitmpl.com","https:\u002F\u002Fgithub.com\u002Fdavila7","https:\u002F\u002Fdanielavila.me","Building AI dev tools with LLMs","2026-11-14 06:14:46",{"source":8,"skillName":5,"commands":60,"prompt":70,"zipAvailable":71},[61,64,67],{"tool":62,"command":63},"npx","npx skills add davila7\u002Fclaude-code-templates --skill datacommons-client",{"tool":65,"command":66},"bunx","bunx skills add davila7\u002Fclaude-code-templates --skill datacommons-client",{"tool":68,"command":69},"pnpm","pnpm dlx skills add davila7\u002Fclaude-code-templates --skill datacommons-client","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdatacommons-client\u002FSKILL.md",true,[73,79,83,87],{"path":74,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":77,"sourceHtmlUrl":78},"references\u002Fgetting_started.md","REFERENCE","参考文档",10145,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdatacommons-client\u002Freferences\u002Fgetting_started.md",{"path":80,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":81,"sourceHtmlUrl":82},"references\u002Fnode.md",6126,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdatacommons-client\u002Freferences\u002Fnode.md",{"path":84,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":85,"sourceHtmlUrl":86},"references\u002Fobservation.md",5498,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdatacommons-client\u002Freferences\u002Fobservation.md",{"path":88,"resourceType":75,"resourceTypeDesc":76,"sizeBytes":89,"sourceHtmlUrl":90},"references\u002Fresolve.md",7204,"https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fscientific\u002Fdatacommons-client\u002Freferences\u002Fresolve.md",[92,116,142,174,209,233],{"skillNo":93,"name":94,"nameZh":6,"description":95,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":96,"categories":97,"keywords":99,"publishedAt":44},"01M70NHBTQ8BV0A2SCAZXEPHCX","wealthville-pool-scoring","Score DeFi liquidity pools before providing liquidity. Returns a 0-100 pool score and an Enter\u002FHold\u002FExit\u002FReduce\u002FAvoid verdict with calibrated confidence for ~68,800 Solana pools and 575 EVM pools, plus a public miss-inclusive track record. Use when asked whether a pool is worth LPing into, which pools look best right now, or how accurate the scores have actually been.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fweb-data\u002Fwealthville-pool-scoring\u002FSKILL.md",[98],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[100,101,102,103,104,29,105,106,39,28,107,32,108,109,110,111,112,113,114,115],"scoring","pool","wealthville pool scoring","liquidity","which","exit","now","reduce","record","track","verdict","evm","solana","how","public","look",{"skillNo":117,"name":118,"nameZh":6,"description":119,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":120,"categories":121,"keywords":123,"publishedAt":44},"01M70MB6VR8ERJ4EM8D5KHBMEV","technical-debt-auditor","Scans a codebase and produces a quantified technical debt report with a prioritized remediation plan. Analyzes dead code, TODO\u002FFIXME\u002FHACK markers, dependency staleness, complexity hotspots, code duplication signals, test coverage gaps, unused exports, and outdated patterns. Use this skill when the user asks to audit their codebase for tech debt, wants a health check of their code quality, says things like \"where should I focus refactoring\", \"find code smells\", \"audit my codebase\", \"what technical debt do we have\", \"prioritize cleanup work\", or asks about code quality metrics. Also trigger when the user mentions \"code rot\", \"legacy cleanup\", \"refactoring priorities\", or wants to understand the overall health of a project before a major effort. Trigger even if the user doesn't say \"technical debt\" explicitly — any request to systematically assess code quality and identify improvement priorities belongs here.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdevelopment\u002Ftechnical-debt-auditor\u002FSKILL.md",[122],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[124,125,126,127,128,129,130,131,132,133,134,28,135,136,40,137,138,139,140,141],"technical debt","debt","technical","technical debt auditor","auditor","coverage","complexity","markers","project","cleanup","unused","code","todo","plan","focus","signals","audit","dependency",{"skillNo":143,"name":144,"nameZh":6,"description":145,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":146,"categories":147,"keywords":157,"publishedAt":44},"01M70KPYH9QNHCNME5GJQGE22K","readonly-sql-guard","Make an agent's database access provably read-only. Use when an agent runs caller-supplied SQL against a production\u002FERP\u002Fanalytics database (a \"query\" MCP tool, text-to-SQL, an admin console), or when reviewing DB-access code for write\u002FDDL\u002Fside-effect\u002Ftransaction-escape risks. Checks what a statement CALLS, not just its shape.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fdatabase\u002Freadonly-sql-guard\u002FSKILL.md",[148,151,152],{"categoryNo":149,"categoryName":150,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000010","后端与接口",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":153,"categoryName":154,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[158,159,160,161,162,163,164,29,165,166,167,168,169,170,171,39,41,172,28,173],"readonly","guard","sql guard","sql","readonly sql guard","erp","effect","read-only","db","just","tool","calls","only","MCP","database","query",{"skillNo":175,"name":176,"nameZh":6,"description":177,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":178,"licenseFileUrl":179,"sourceHtmlUrl":180,"categories":181,"keywords":185,"publishedAt":44},"01M6AFY6F7CC2BBZP1QD6VTNVC","web-artifacts-builder","Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn\u002Fui). Use for complex artifacts requiring state management, routing, or shadcn\u002Fui components - not for simple single-file HTML\u002FJSX artifacts.","Apache-2.0","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Futilities\u002Fweb-artifacts-builder\u002FSKILL.md",[182],{"categoryNo":183,"categoryName":184,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,29,208],"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":210,"name":211,"nameZh":6,"description":212,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":178,"licenseFileUrl":213,"sourceHtmlUrl":214,"categories":215,"keywords":220,"publishedAt":44},"01M6AFWF33SFV9Z3SF4PEEE4BF","webapp-testing","Toolkit for interacting with and testing local web applications using Playwright. Supports verifying frontend functionality, debugging UI behavior, capturing browser screenshots, and viewing browser logs.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fsecurity\u002Fwebapp-testing\u002FSKILL.md",[216,219],{"categoryNo":217,"categoryName":218,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":183,"categoryName":184,"taxonomyType":21,"taxonomyTypeDesc":22},[221,222,223,224,225,187,226,196,197,227,228,229,230,29,231,32,232],"webapp testing","testing","webapp","browser","playwright","applications","debugging","toolkit","logs","screenshots","local","behavior",{"skillNo":234,"name":235,"nameZh":6,"description":236,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":178,"licenseFileUrl":237,"sourceHtmlUrl":238,"categories":239,"keywords":247,"publishedAt":44},"01M6AF67Q67JJJ5V51JR2P52KA","discernment-nudge","After you give a substantive answer or draft that the user may act on — advice or recommendations, drafted artifacts such as goals, plans, pitches, proposals, or emails, estimates or projections, analysis or interpretation of data, factual claims they may rely on, or a multi-step argument — invoke this skill BEFORE finalizing your reply and then, if it applies, append 2-3 short follow-up questions, each tied to something specific in what you just produced, that help the user check key facts, probe the reasoning or assumptions, and notice missing context. Do this at most once per conversation. Skip it when the user asked a trivial how-to or simple lookup, wants a purely educational explanation, asked you only to format, convert, or assemble a file from content they provided, is writing code they will run, is doing creative writing or casual chat, or already asked you to double-check, cite, or review — the skill file explains these boundaries and the exact output format.","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FLICENSE.txt","https:\u002F\u002Fgithub.com\u002Fdavila7\u002Fclaude-code-templates\u002Fblob\u002Fmain\u002Fcli-tool\u002Fcomponents\u002Fskills\u002Fproductivity\u002Fdiscernment-nudge\u002FSKILL.md",[240,243,244],{"categoryNo":241,"categoryName":242,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000018","写作与文案",{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},{"categoryNo":245,"categoryName":246,"taxonomyType":155,"taxonomyTypeDesc":156},"01JBSEEDCAT000000000000036","内容创作者",[248,249,250,251,252,253,35,254,189,255,199,135,256,257,258,259,260,170,261,28,262,263,264,265,266,113,267],"discernment nudge","nudge","interpretation","help","run","reasoning","writing","lookup","content","review","conversation","short","up","invoke","you","analysis","claims","argument","assemble","recommendations"]