[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M71MDCC80M8V7KKAHGPRA07F":3,"skill-same:01M71MDCC80M8V7KKAHGPRA07F":82},{"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":49,"linkOnly":50,"markdownContent":51,"markdownBody":52,"frontMatter":53,"refName":59,"commitSha":60,"skillDirectory":61,"forksCount":62,"repositoryHtmlUrl":63,"repositoryHomepageUrl":6,"ownerHtmlUrl":64,"ownerBlogUrl":65,"ownerDescription":66,"updatedAt":67,"install":68,"resources":81},"01M71MDCC80M8V7KKAHGPRA07F","chdb-sql",null,"In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries directly on local files, remote databases, and cloud storage without a server. Use when the user wants to write SQL queries against Parquet\u002FCSV\u002F JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(), iceberg(), deltaLake() etc.), build stateful analytical pipelines with Session, use parametrized queries, window functions, or other advanced ClickHouse SQL features. Also use when the user explicitly mentions chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins. Do NOT use for pandas-style DataFrame operations — use chdb-datastore instead.","iDorgham\u002FWorkspace-Factory","iDorgham","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F136521493?v=4","01M6CNGBDAHE4ARW3YJEF4X2FD","Dorgham","01M71E8H3CXZ7DJX59MTM9JEB2",1,"Apache-2.0","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_clickhouse_chdb_sql\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},"01JBSEEDCAT000000000000033","数据分析师","PROFESSION","适用职业",[29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48],"chdb sql","sql","query","when","window","stateful","Cloud Storage","advanced","remote","io","local","files","databases","server","pandas","dataframe","iceberg","postgresql","mysql","run","2026-11-10 10:56:23",false,"---\nname: chdb-sql\ndescription: >-\n  In-process ClickHouse SQL engine for Python — run ClickHouse SQL queries\n  directly on local files, remote databases, and cloud storage without a\n  server. Use when the user wants to write SQL queries against Parquet\u002FCSV\u002F\n  JSON files, use ClickHouse table functions (mysql(), s3(), postgresql(),\n  iceberg(), deltaLake() etc.), build stateful analytical pipelines with\n  Session, use parametrized queries, window functions, or other advanced\n  ClickHouse SQL features. Also use when the user explicitly mentions\n  chdb.query(), ClickHouse SQL syntax, or wants cross-source SQL joins.\n  Do NOT use for pandas-style DataFrame operations — use chdb-datastore\n  instead.\nlicense: Apache-2.0\ncompatibility: Requires Python 3.9+, macOS or Linux. pip install chdb.\nmetadata:\n  author: chdb-io\n  version: \"4.1\"\n  homepage: https:\u002F\u002Fclickhouse.com\u002Fdocs\u002Fchdb\n---\n\n# chdb SQL — ClickHouse in Your Python Process\n\nRun ClickHouse SQL directly in Python — no server needed. Query local files, remote databases, and cloud storage with full ClickHouse SQL power.\n\n```bash\npip install chdb\n```\n\n## Decision Tree: Pick the Right API\n\n```\n1. One-off query on files or databases → chdb.query()\n2. Multi-step analysis with tables      → Session\n3. DB-API 2.0 connection                → chdb.connect()\n4. Pandas-style DataFrame operations    → Use chdb-datastore skill instead\n```\n\n## chdb.query() — One Line, Any Data\n\n```python\nimport chdb\n\nchdb.query(\"SELECT * FROM file('data.parquet', Parquet) WHERE price > 100 LIMIT 10\")       # local files\nchdb.query(\"SELECT * FROM mysql('db:3306', 'shop', 'orders', 'root', 'pass')\")              # databases\nchdb.query(\"SELECT * FROM s3('s3:\u002F\u002Fbucket\u002Fdata.parquet', NOSIGN) LIMIT 10\")                 # cloud storage\nchdb.query(\"SELECT * FROM deltaLake('s3:\u002F\u002Fbucket\u002Fdelta\u002Ftable', NOSIGN) LIMIT 10\")           # data lakes\n\n# Cross-source join\nchdb.query(\"\"\"\n    SELECT u.name, o.amount FROM mysql('db:3306', 'crm', 'users', 'root', 'pass') AS u\n    JOIN file('orders.parquet', Parquet) AS o ON u.id = o.user_id ORDER BY o.amount DESC\n\"\"\")\n\ndata = {\"name\": [\"Alice\", \"Bob\"], \"score\": [95, 87]}\nchdb.query(\"SELECT * FROM Python(data) ORDER BY score DESC\")                                # Python data\ndf = chdb.query(\"SELECT * FROM numbers(10)\", \"DataFrame\")                                   # output formats\nchdb.query(\"SELECT toDate({d:String}) + number FROM numbers({n:UInt64})\",\n    \"DataFrame\", params={\"d\": \"2025-01-01\", \"n\": 30})                                      # parametrized\n```\n\nTable functions → table_functions.md (`references\u002Ftable_functions.md`) | SQL functions → sql_functions.md (`references\u002Fsql_functions.md`) | Full API → api_reference.md (`references\u002Fapi_reference.md`)\n\n## Session — Stateful Analysis Pipelines\n\n```python\nfrom chdb import session as chs\nsess = chs.Session(\".\u002Fanalytics_db\")   # persistent; Session() for in-memory\n\nsess.query(\"CREATE TABLE users ENGINE=MergeTree() ORDER BY id AS SELECT * FROM mysql('db:3306','crm','users','root','pass')\")\nsess.query(\"CREATE TABLE events ENGINE=MergeTree() ORDER BY (ts,user_id) AS SELECT * FROM s3('s3:\u002F\u002Flogs\u002Fevents\u002F*.parquet',NOSIGN)\")\nsess.query(\"\"\"\n    SELECT u.country, count() AS cnt, uniqExact(e.user_id) AS users\n    FROM events e JOIN users u ON e.user_id = u.id\n    WHERE e.ts >= today() - 7 GROUP BY u.country ORDER BY cnt DESC\n\"\"\", \"Pretty\").show()\nsess.close()\n```\n\n## Connection API (DB-API 2.0)\n\n```python\nfrom chdb import dbapi\nconn = dbapi.connect()\ncur = conn.cursor()\ncur.execute(\"SELECT * FROM file('data.parquet', Parquet) WHERE value > 100\")\nprint(cur.fetchall())\ncur.close()\nconn.close()\n```\n\n## Troubleshooting\n\n| Problem | Fix |\n|---------|-----|\n| `ImportError: No module named 'chdb'` | `pip install chdb` |\n| `DB::Exception: FILE_NOT_FOUND` | Check file path; use absolute path or verify cwd |\n| `DB::Exception: Unknown table function` | Check function name spelling (e.g., `deltaLake` not `deltalake`) |\n| Connection refused to remote DB | Check host:port format; ensure remote DB allows connections |\n| Environment check | Run `python scripts\u002Fverify_install.py` (from skill directory) |\n\n## References\n\n- API Reference (`references\u002Fapi_reference.md`) — query\u002FSession\u002Fconnect signatures\n- Table Functions (`references\u002Ftable_functions.md`) — All ClickHouse table functions\n- SQL Functions (`references\u002Fsql_functions.md`) — Commonly used SQL functions\n- Examples (`examples\u002Fexamples.md`) — 9 runnable examples with expected output\n- [Official Docs](https:\u002F\u002Fclickhouse.com\u002Fdocs\u002Fchdb)\n\n> Note: This skill teaches how to *use* chdb SQL.\n> For pandas-style operations, use the `chdb-datastore` skill.\n> For contributing to chdb source code, see CLAUDE.md in the project root.\n","# chdb SQL — ClickHouse in Your Python Process\n\nRun ClickHouse SQL directly in Python — no server needed. Query local files, remote databases, and cloud storage with full ClickHouse SQL power.\n\n```bash\npip install chdb\n```\n\n## Decision Tree: Pick the Right API\n\n```\n1. One-off query on files or databases → chdb.query()\n2. Multi-step analysis with tables      → Session\n3. DB-API 2.0 connection                → chdb.connect()\n4. Pandas-style DataFrame operations    → Use chdb-datastore skill instead\n```\n\n## chdb.query() — One Line, Any Data\n\n```python\nimport chdb\n\nchdb.query(\"SELECT * FROM file('data.parquet', Parquet) WHERE price > 100 LIMIT 10\")       # local files\nchdb.query(\"SELECT * FROM mysql('db:3306', 'shop', 'orders', 'root', 'pass')\")              # databases\nchdb.query(\"SELECT * FROM s3('s3:\u002F\u002Fbucket\u002Fdata.parquet', NOSIGN) LIMIT 10\")                 # cloud storage\nchdb.query(\"SELECT * FROM deltaLake('s3:\u002F\u002Fbucket\u002Fdelta\u002Ftable', NOSIGN) LIMIT 10\")           # data lakes\n\n# Cross-source join\nchdb.query(\"\"\"\n    SELECT u.name, o.amount FROM mysql('db:3306', 'crm', 'users', 'root', 'pass') AS u\n    JOIN file('orders.parquet', Parquet) AS o ON u.id = o.user_id ORDER BY o.amount DESC\n\"\"\")\n\ndata = {\"name\": [\"Alice\", \"Bob\"], \"score\": [95, 87]}\nchdb.query(\"SELECT * FROM Python(data) ORDER BY score DESC\")                                # Python data\ndf = chdb.query(\"SELECT * FROM numbers(10)\", \"DataFrame\")                                   # output formats\nchdb.query(\"SELECT toDate({d:String}) + number FROM numbers({n:UInt64})\",\n    \"DataFrame\", params={\"d\": \"2025-01-01\", \"n\": 30})                                      # parametrized\n```\n\nTable functions → table_functions.md (`references\u002Ftable_functions.md`) | SQL functions → sql_functions.md (`references\u002Fsql_functions.md`) | Full API → api_reference.md (`references\u002Fapi_reference.md`)\n\n## Session — Stateful Analysis Pipelines\n\n```python\nfrom chdb import session as chs\nsess = chs.Session(\".\u002Fanalytics_db\")   # persistent; Session() for in-memory\n\nsess.query(\"CREATE TABLE users ENGINE=MergeTree() ORDER BY id AS SELECT * FROM mysql('db:3306','crm','users','root','pass')\")\nsess.query(\"CREATE TABLE events ENGINE=MergeTree() ORDER BY (ts,user_id) AS SELECT * FROM s3('s3:\u002F\u002Flogs\u002Fevents\u002F*.parquet',NOSIGN)\")\nsess.query(\"\"\"\n    SELECT u.country, count() AS cnt, uniqExact(e.user_id) AS users\n    FROM events e JOIN users u ON e.user_id = u.id\n    WHERE e.ts >= today() - 7 GROUP BY u.country ORDER BY cnt DESC\n\"\"\", \"Pretty\").show()\nsess.close()\n```\n\n## Connection API (DB-API 2.0)\n\n```python\nfrom chdb import dbapi\nconn = dbapi.connect()\ncur = conn.cursor()\ncur.execute(\"SELECT * FROM file('data.parquet', Parquet) WHERE value > 100\")\nprint(cur.fetchall())\ncur.close()\nconn.close()\n```\n\n## Troubleshooting\n\n| Problem | Fix |\n|---------|-----|\n| `ImportError: No module named 'chdb'` | `pip install chdb` |\n| `DB::Exception: FILE_NOT_FOUND` | Check file path; use absolute path or verify cwd |\n| `DB::Exception: Unknown table function` | Check function name spelling (e.g., `deltaLake` not `deltalake`) |\n| Connection refused to remote DB | Check host:port format; ensure remote DB allows connections |\n| Environment check | Run `python scripts\u002Fverify_install.py` (from skill directory) |\n\n## References\n\n- API Reference (`references\u002Fapi_reference.md`) — query\u002FSession\u002Fconnect signatures\n- Table Functions (`references\u002Ftable_functions.md`) — All ClickHouse table functions\n- SQL Functions (`references\u002Fsql_functions.md`) — Commonly used SQL functions\n- Examples (`examples\u002Fexamples.md`) — 9 runnable examples with expected output\n- [Official Docs](https:\u002F\u002Fclickhouse.com\u002Fdocs\u002Fchdb)\n\n> Note: This skill teaches how to *use* chdb SQL.\n> For pandas-style operations, use the `chdb-datastore` skill.\n> For contributing to chdb source code, see CLAUDE.md in the project root.\n",{"name":5,"license":15,"metadata":54,"description":7,"compatibility":58},{"author":55,"version":56,"homepage":57},"chdb-io","4.1","https:\u002F\u002Fclickhouse.com\u002Fdocs\u002Fchdb","Requires Python 3.9+, macOS or Linux. pip install chdb.","master","c2e410e11df4d80d885e0ecf0583ef3c926df736","factory\u002Flibrary\u002Fskills\u002Fofficial_clickhouse_chdb_sql",0,"https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory","https:\u002F\u002Fgithub.com\u002FiDorgham","Dorgham.me","hey I'm Dorgham \r\nI code, I create, and AI codes back. \r\nMaybe we can give AI the chance!! now we begin. 🚀🤖\r\n... . . . git commit -m \"Hello, Future\" ","2026-05-04 18:39:09",{"source":8,"skillName":5,"commands":69,"prompt":79,"zipAvailable":80},[70,73,76],{"tool":71,"command":72},"npx","npx skills add iDorgham\u002FWorkspace-Factory --skill chdb-sql",{"tool":74,"command":75},"bunx","bunx skills add iDorgham\u002FWorkspace-Factory --skill chdb-sql",{"tool":77,"command":78},"pnpm","pnpm dlx skills add iDorgham\u002FWorkspace-Factory --skill chdb-sql","安装技能，技能地址：https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_clickhouse_chdb_sql\u002Fskill.md",true,[],[83,114,137,159,179,199],{"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":87,"licenseFileUrl":6,"sourceHtmlUrl":88,"categories":89,"keywords":93,"publishedAt":113},"01M71P8WVBMB2X3D24R0YT0B0E","vercel-react-view-transitions","Guide for implementing smooth, native-feeling animations using React's View Transition API (`\u003CViewTransition>` component, `addTransitionType`, and CSS view transition pseudo-elements). Use this skill whenever the user wants to add page transitions, animate route changes, create shared element animations, animate enter\u002Fexit of components, animate list reorder, implement directional (forward\u002Fback) navigation animations, or integrate view transitions in Next.js. Also use when the user mentions view transitions, `startViewTransition`, `ViewTransition`, transition types, or asks about animating between UI states in React without third-party animation libraries.","MIT","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_view_transitions\u002Fskill.md",[90],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},"01JBSEEDCAT000000000000009","前端开发",[94,95,96,97,98,99,100,101,102,103,104,105,106,107,32,108,109,110,111,112],"react","vercel react view transitions","view","react view transitions","vercel","transitions","shared","back","Animation","css","types","states","element","implementing","components","api","route","create","libraries","2026-11-10 13:02:02",{"skillNo":115,"name":116,"nameZh":6,"description":117,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":6,"sourceHtmlUrl":118,"categories":119,"keywords":121,"publishedAt":113},"01M71P8TDGHZ289TSAC5VTG2EW","vercel-react-native-skills","React Native and Expo best practices for building performant mobile apps. Use when building React Native components, optimizing list performance, implementing animations, or working with native modules. Triggers on tasks involving React Native, Expo, mobile performance, or native platform APIs.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_native_skills\u002Fskill.md",[120],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[94,122,123,98,124,125,126,127,107,128,32,129,108,130,131,132,133,134,135,136],"react native","native","vercel react native skills","react native skills","modules","performance","apps","tasks","mobile","expo","list","mobile performance","platform","animations","for",{"skillNo":138,"name":139,"nameZh":6,"description":140,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":6,"sourceHtmlUrl":141,"categories":142,"keywords":144,"publishedAt":113},"01M71P8QK4JF5BYRGYDDGPZQG9","vercel-react-best-practices","React and Next.js performance optimization guidelines from Vercel Engineering. This skill should be used when writing, reviewing, or refactoring React\u002FNext.js code to ensure optimal performance patterns. Triggers on tasks involving React components, Next.js pages, data fetching, bundle optimization, or performance improvements.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_react_best_practices\u002Fskill.md",[143],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[94,145,146,98,147,127,148,32,149,129,108,150,151,152,153,154,155,156,157,158],"react best practices","vercel react best practices","best practices","writing","code","data fetching","react components","next","refactoring","data","patterns","Optimization","guidelines","triggers",{"skillNo":160,"name":161,"nameZh":6,"description":162,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":6,"sourceHtmlUrl":163,"categories":164,"keywords":166,"publishedAt":113},"01M71P8HVEA70K5N6ZVJCG2ED3","vercel-composition-patterns","React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_vercel_labs_composition_patterns\u002Fskill.md",[165],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[167,98,168,155,169,170,171,172,158,173,174,175,32,176,129,108,177,178,109,94],"Composition","composition patterns","vercel composition patterns","react composition patterns","react 19","render","boolean","component architecture","use","reusable","architecture","react composition",{"skillNo":180,"name":181,"nameZh":6,"description":182,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":6,"sourceHtmlUrl":183,"categories":184,"keywords":186,"publishedAt":113},"01M71P60XXKKP222ZFJRG7R373","supabase-postgres-best-practices","Postgres performance optimization and best practices from Supabase. Use this skill when writing, reviewing, or optimizing Postgres queries, schema designs, or database configurations.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_supabase_supabase_postgres_best_practices\u002Fskill.md",[185],{"categoryNo":19,"categoryName":20,"taxonomyType":21,"taxonomyTypeDesc":22},[187,188,189,190,147,191,192,36,136,156,127,193,194,148,195,175,196,197,198,32],"postgres best practices","supabase","postgres","supabase postgres best practices","categories","postgres performance","correct","schema","impact","Rules","critical","database",{"skillNo":200,"name":201,"nameZh":6,"description":202,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":87,"licenseFileUrl":6,"sourceHtmlUrl":203,"categories":204,"keywords":206,"publishedAt":49},"01M71P2ZCEJD0PKC458YTP1FJW","portable-text-serialization","Render and serialize Portable Text to React, Svelte, Vue, Astro, HTML, Markdown, and plain text. Use when implementing Portable Text rendering in any frontend framework, building custom serializers for non-standard block types, converting Portable Text to HTML strings server-side, converting Portable Text to Markdown, extracting plain text from Portable Text, or troubleshooting rendering issues with marks, blocks, lists, or custom types.","https:\u002F\u002Fgithub.com\u002FiDorgham\u002FWorkspace-Factory\u002Fblob\u002Fmaster\u002Ffactory\u002Flibrary\u002Fskills\u002Fofficial_sanity_io_portable_text_serialization\u002Fskill.md",[205],{"categoryNo":91,"categoryName":92,"taxonomyType":21,"taxonomyTypeDesc":22},[207,208,209,210,211,104,212,172,213,107,214,215,32,94,216,217,42,218,219,220],"text","portable text serialization","portable","serialization","extracting","strings","issues","frontend","svelte","sanity","astro","markdown","rendering","vue"]