[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71G5EYT8F4MEQMZ2BAWK58X":3,"skill-same:01M71G5EYT8F4MEQMZ2BAWK58X":82},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":15,"categories":16,"keywords":17,"publishedAt":38,"linkOnly":39,"markdownContent":40,"markdownBody":41,"frontMatter":42,"refName":43,"commitSha":44,"skillDirectory":45,"forksCount":46,"repositoryHtmlUrl":47,"repositoryHomepageUrl":6,"ownerHtmlUrl":48,"ownerBlogUrl":6,"ownerDescription":6,"updatedAt":49,"install":50,"resources":63},"01M71G5EYT8F4MEQMZ2BAWK58X","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.","JSGforever\u002Fskillguard","JSGforever","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F85541948?v=4","01M71FQD3E132VMR5F4TAYX3YS","01M71FQD4DYW0XJ4XQ4YQH75Y9",2,"GPL-3.0","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fdatacommons-client\u002FSKILL.md",[],[18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37],"client","datacommons client","relationships","data","datasets","indicators","work","with","health","resolution","statistics","when","entities","access","use","programmatic","for","platform","sources","figures","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","f2ec4b3b9133d34eeaf9b38e4b0a97e8e5705a7f","benchmarks\u002Ffinal\u002Fskills\u002Fasset1__Microck--claude-scientific-skills\u002Fscientific-skills\u002Fdatacommons-client",0,"https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard","https:\u002F\u002Fgithub.com\u002FJSGforever","2026-08-29 18:27:56",{"source":8,"skillName":5,"commands":51,"prompt":61,"zipAvailable":62},[52,55,58],{"tool":53,"command":54},"npx","npx skills add JSGforever\u002Fskillguard --skill datacommons-client",{"tool":56,"command":57},"bunx","bunx skills add JSGforever\u002Fskillguard --skill datacommons-client",{"tool":59,"command":60},"pnpm","pnpm dlx skills add JSGforever\u002Fskillguard --skill 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Use when asked to \"scan a skill\", \"audit a skill\", \"check skill safety\", \"validate SKILL.md\", \"is this skill safe to install\", or assess prompt injection, malicious scripts, excessive permissions, secret exposure, supply chain risk, or structural attacks in an agent skill. Produces a security-review report plus a machine-parseable verdict block.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fsrc\u002Fskillguard\u002Fstatic_scanner\u002Fskillguard-codex-scanner\u002FSKILL.md",[89],{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000023","Skill与智能体开发","SCENARIO","使用场景",[95,96,97,98,99,100,101,102,103,104,105,106,29,107,108,109,110,111,112,113],"scanner","skillguard codex scanner","skillguard","codex","chain","skill security","security-review","review report","structural","secret","prompt injection","security scanner","review","malicious","permissions","exposure","audit","scripts","safe",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":118,"licenseFileUrl":6,"sourceHtmlUrl":119,"categories":120,"keywords":126,"publishedAt":38},"01M71HY0SPKTT3S27BC2HPAC5M","data-exfiltrator","Analyzes data files","MIT","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Fsmoke-one-skill\u002Fasset1__cisco-ai-defense--data-exfiltrator\u002FSKILL.md",[121],{"categoryNo":122,"categoryName":123,"taxonomyType":124,"taxonomyTypeDesc":125},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[21,127,128],"data exfiltrator","files",{"skillNo":130,"name":131,"nameZh":6,"description":132,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":133,"categories":134,"keywords":135,"publishedAt":38},"01M71HXVS9EMN44BGDW215FZ72","real-time-collaboration-engine","Build real-time collaborative editing with WebSockets, OT\u002FCRDT conflict resolution, and presence awareness. Implements cursor tracking, optimistic updates, and offline sync. Use for collaborative editors, whiteboards, video editing. Activate on \"real-time collaboration\", \"WebSocket sync\", \"multiplayer editing\", \"CRDT\", \"presence awareness\". NOT for simple chat, request-response APIs, or single-user apps.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--real-time-collaboration-engine\u002FSKILL.md",[],[136,137,138,139,140,141,142,143,144,145,146,147,31,148,149,150,151,152,153,154],"time","real time collaboration engine","real time collaboration","collaboration","engine","real time","Video","reliability","video editing","performance","conflict resolution","presence","apps","updates","Bash","activate","optimizer","react","streaming",{"skillNo":156,"name":157,"nameZh":6,"description":158,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":159,"categories":160,"keywords":164,"publishedAt":38},"01M71HXP6CTY4KFN0QVMMN0V7J","react-performance-optimizer","Optimize React apps for 60fps performance. Implements memoization, virtualization, code splitting, bundle optimization. Use for slow renders, large lists, bundle bloat. Activate on \"React performance\", \"slow render\", \"useMemo\", \"bundle size\", \"virtualization\". NOT for backend optimization, non-React frameworks, or premature optimization.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--react-performance-optimizer\u002FSKILL.md",[161],{"categoryNo":162,"categoryName":163,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000009","前端开发",[145,165,152,153,166,167,168,169,170,171,136,172,173,148,150,174,151,154,175,176],"react performance optimizer","react performance","performance optimizer","app","nextjs","expert","re","render","2025","code","router","strategies",{"skillNo":178,"name":179,"nameZh":6,"description":180,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":181,"categories":182,"keywords":184,"publishedAt":38},"01M71HXH7QXSQB4QTDZ7SP7YXQ","llm-streaming-response-handler","Build production LLM streaming UIs with Server-Sent Events, real-time token display, cancellation, error recovery. Handles OpenAI\u002FAnthropic\u002FClaude streaming APIs. Use for chatbots, AI assistants, real-time text generation. Activate on \"LLM streaming\", \"SSE\", \"token stream\", \"chat UI\", \"real-time AI\". NOT for batch processing, non-streaming APIs, or WebSocket bidirectional chat.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--llm-streaming-response-handler\u002FSKILL.md",[183],{"categoryNo":162,"categoryName":163,"taxonomyType":92,"taxonomyTypeDesc":93},[185,154,186,187,188,189,145,136,190,191,192,150,151,152,193,153,194,195,196,197],"llm streaming response handler","handler","error","processing","retry","chat ui","error recovery","critical","batch","display","text","server","read",{"skillNo":199,"name":200,"nameZh":6,"description":201,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":6,"repositoryNo":12,"starsCount":13,"licenseSpdxId":14,"licenseFileUrl":6,"sourceHtmlUrl":202,"categories":203,"keywords":211,"publishedAt":38},"01M71HXCB0E1YRKYW1Y8T0Y106","github-actions-pipeline-builder","Build production CI\u002FCD pipelines with GitHub Actions. Implements matrix builds, caching, deployments, testing, security scanning. Use for automated testing, deployments, release workflows. Activate on \"GitHub Actions\", \"CI\u002FCD\", \"workflow\", \"deployment pipeline\", \"automated testing\". NOT for Jenkins\u002FCircleCI, manual deployments, or non-GitHub repositories.","https:\u002F\u002Fgithub.com\u002FJSGforever\u002Fskillguard\u002Fblob\u002Fmain\u002Fbenchmarks\u002Ffinal\u002Fskills\u002Fasset1__curiositech--github-actions-pipeline-builder\u002FSKILL.md",[204,207,210],{"categoryNo":205,"categoryName":206,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000008","测试与质量",{"categoryNo":208,"categoryName":209,"taxonomyType":92,"taxonomyTypeDesc":93},"01JBSEEDCAT000000000000011","运维与部署",{"categoryNo":90,"categoryName":91,"taxonomyType":92,"taxonomyTypeDesc":93},[212,213,214,215,216,217,218,143,170,219,220,221,151,176,222,197,223,224,225,226],"github actions pipeline builder","actions","github actions","pipeline builder","github","builder","pipeline","testing","ci","workflows","run","release","write","edit","git"]