[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-clickhouse-chdb-datastore":3,"mdc-ykrptz-key":38,"related-repo-clickhouse-chdb-datastore":874,"related-org-clickhouse-chdb-datastore":987},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":26,"repoUrl":27,"updatedAt":28,"license":29,"forks":30,"topics":31,"repo":33,"sourceUrl":36,"mdContent":37},"chdb-datastore","accelerate pandas with chDB datastore","Use when the user has tabular data (pandas DataFrame, parquet, csv, Arrow, json) and wants to filter, group, aggregate, join, or speed up slow pandas. Provides chDB DataStore — same pandas API, ClickHouse engine underneath. Also handles reading from S3, MySQL, PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as DataFrames and joining across sources. TRIGGER when: user mentions DataFrame, parquet, csv, \"fast pandas\", \"speed up pandas\", or cross-source DataFrame joins; user imports `chdb.datastore` or `from datastore import DataStore`. SKIP this skill for raw SQL syntax (use chdb-sql instead), ClickHouse server administration, or non-Python DataStore API work.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},"clickhouse","ClickHouse","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fclickhouse.png",[12,14,17,20,23],{"name":9,"slug":8,"type":13},"tag",{"name":15,"slug":16,"type":13},"Performance","performance",{"name":18,"slug":19,"type":13},"Data Engineering","data-engineering",{"name":21,"slug":22,"type":13},"Data Analysis","data-analysis",{"name":24,"slug":25,"type":13},"Python","python",493,"https:\u002F\u002Fgithub.com\u002FClickHouse\u002Fagent-skills","2026-04-15T04:56:32.0629","Apache-2.0",32,[32,8],"agents",{"repoUrl":27,"stars":26,"forks":30,"topics":34,"description":35},[32,8],"The official Agent Skills for ClickHouse and ClickHouse Cloud","https:\u002F\u002Fgithub.com\u002FClickHouse\u002Fagent-skills\u002Ftree\u002FHEAD\u002Fskills\u002Fchdb-datastore","---\nname: chdb-datastore\ndescription: >-\n  Use when the user has tabular data (pandas DataFrame, parquet, csv,\n  Arrow, json) and wants to filter, group, aggregate, join, or speed\n  up slow pandas. Provides chDB DataStore — same pandas API,\n  ClickHouse engine underneath. Also handles reading from S3, MySQL,\n  PostgreSQL, MongoDB, ClickHouse Cloud, Iceberg, Delta Lake as\n  DataFrames and joining across sources.\n  TRIGGER when: user mentions DataFrame, parquet, csv, \"fast pandas\",\n  \"speed up pandas\", or cross-source DataFrame joins; user imports\n  `chdb.datastore` or `from datastore import DataStore`.\n  SKIP this skill for raw SQL syntax (use chdb-sql instead),\n  ClickHouse server administration, or non-Python DataStore API work.\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 DataStore — It's Just Faster Pandas\n\n## The Key Insight\n\n```python\n# Change this:\nimport pandas as pd\n# To this:\nimport chdb.datastore as pd\n# Everything else stays the same.\n```\n\nDataStore is a **lazy, ClickHouse-backed pandas replacement**. Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., `print()`, `len()`, iteration).\n\n```bash\npip install chdb\n```\n\n## Decision Tree: Pick the Right Approach\n\n```\n1. \"I have a file\u002Fdatabase and want to analyze it with pandas\"\n   → DataStore.from_file() \u002F from_mysql() \u002F from_s3() etc.\n   → See references\u002Fconnectors.md\n\n2. \"I need to join data from different sources\"\n   → Create DataStores from each source, use .join()\n   → See examples\u002Fexamples.md #3-5\n\n3. \"My pandas code is too slow\"\n   → import chdb.datastore as pd — change one line, keep the rest\n\n4. \"I need raw SQL queries\"\n   → Use the chdb-sql skill instead\n```\n\n## Connect to Any Data Source — One Pattern\n\n```python\nfrom datastore import DataStore\n\n# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)\nds = DataStore.from_file(\"sales.parquet\")\n\n# Database\nds = DataStore.from_mysql(host=\"db:3306\", database=\"shop\", table=\"orders\", user=\"root\", password=\"pass\")\n\n# Cloud storage\nds = DataStore.from_s3(\"s3:\u002F\u002Fbucket\u002Fdata.parquet\", nosign=True)\n\n# URI shorthand — auto-detects source type\nds = DataStore.uri(\"mysql:\u002F\u002Froot:pass@db:3306\u002Fshop\u002Forders\")\n```\n\nAll 16+ sources and URI schemes → [connectors.md](references\u002Fconnectors.md)\n\n## After Connecting — Full Pandas API\n\n```python\nresult = ds[ds[\"age\"] > 25]                                          # filter\nresult = ds[[\"name\", \"city\"]]                                        # select columns\nresult = ds.sort_values(\"revenue\", ascending=False)                  # sort\nresult = ds.groupby(\"dept\")[\"salary\"].mean()                         # groupby\nresult = ds.assign(margin=lambda x: x[\"profit\"] \u002F x[\"revenue\"])     # computed column\nds[\"name\"].str.upper()                                               # string accessor\nds[\"date\"].dt.year                                                   # datetime accessor\nresult = ds1.join(ds2, on=\"id\")                                      # join\nresult = ds.head(10)                                                 # preview\nprint(ds.to_sql())                                                   # see generated SQL\n```\n\n209 DataFrame methods supported. Full API → [api-reference.md](references\u002Fapi-reference.md)\n\n## Cross-Source Join — The Killer Feature\n\n```python\nfrom datastore import DataStore\n\ncustomers = DataStore.from_mysql(host=\"db:3306\", database=\"crm\", table=\"customers\", user=\"root\", password=\"pass\")\norders = DataStore.from_file(\"orders.parquet\")\n\nresult = (orders\n    .join(customers, left_on=\"customer_id\", right_on=\"id\")\n    .groupby(\"country\")\n    .agg({\"amount\": \"sum\", \"rating\": \"mean\"})\n    .sort_values(\"sum\", ascending=False))\nprint(result)\n```\n\nMore join examples → [examples.md](examples\u002Fexamples.md)\n\n## Writing Data\n\n```python\nsource = DataStore.from_mysql(host=\"db:3306\", database=\"shop\", table=\"orders\", user=\"root\", password=\"pass\")\ntarget = DataStore(\"file\", path=\"summary.parquet\", format=\"Parquet\")\n\ntarget.insert_into(\"category\", \"total\", \"count\").select_from(\n    source.groupby(\"category\").select(\"category\", \"sum(amount) AS total\", \"count() AS count\")\n).execute()\n```\n\n## Troubleshooting\n\n| Problem | Fix |\n|---------|-----|\n| `ImportError: No module named 'chdb'` | `pip install chdb` |\n| `ImportError: cannot import 'DataStore'` | Use `from datastore import DataStore` or `from chdb.datastore import DataStore` |\n| Database connection timeout | Include port in host: `host=\"db:3306\"` not `host=\"db\"` |\n| Join returns empty result | Check key types match (both int or both string); use `.to_sql()` to inspect |\n| Unexpected results | Call `ds.to_sql()` to see the generated SQL and debug |\n| Environment check | Run `python scripts\u002Fverify_install.py` (from skill directory) |\n\n## References\n\n- [API Reference](references\u002Fapi-reference.md) — Full DataStore method signatures\n- [Connectors](references\u002Fconnectors.md) — All 16+ data source connection methods\n- [Examples](examples\u002Fexamples.md) — 10+ runnable examples with expected output\n- [Verify Install](scripts\u002Fverify_install.py) — Environment verification script\n- [Official Docs](https:\u002F\u002Fclickhouse.com\u002Fdocs\u002Fchdb)\n\n> Note: This skill teaches how to *use* chdb DataStore.\n> For raw SQL queries, use the `chdb-sql` skill.\n> For contributing to chdb source code, see CLAUDE.md in the project root.\n",{"data":39,"body":45},{"name":4,"description":6,"license":29,"compatibility":40,"metadata":41},"Requires Python 3.9+, macOS or Linux. pip install chdb.",{"author":42,"version":43,"homepage":44},"chdb-io","4.1","https:\u002F\u002Fclickhouse.com\u002Fdocs\u002Fchdb",{"type":46,"children":47},"root",[48,57,64,122,152,181,187,197,203,320,332,338,425,436,442,534,545,551,605,611,781,787,843,868],{"type":49,"tag":50,"props":51,"children":53},"element","h1",{"id":52},"chdb-datastore-its-just-faster-pandas",[54],{"type":55,"value":56},"text","chdb DataStore — It's Just Faster Pandas",{"type":49,"tag":58,"props":59,"children":61},"h2",{"id":60},"the-key-insight",[62],{"type":55,"value":63},"The Key Insight",{"type":49,"tag":65,"props":66,"children":70},"pre",{"className":67,"code":68,"language":25,"meta":69,"style":69},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# Change this:\nimport pandas as pd\n# To this:\nimport chdb.datastore as pd\n# Everything else stays the same.\n","",[71],{"type":49,"tag":72,"props":73,"children":74},"code",{"__ignoreMap":69},[75,86,95,104,113],{"type":49,"tag":76,"props":77,"children":80},"span",{"class":78,"line":79},"line",1,[81],{"type":49,"tag":76,"props":82,"children":83},{},[84],{"type":55,"value":85},"# Change this:\n",{"type":49,"tag":76,"props":87,"children":89},{"class":78,"line":88},2,[90],{"type":49,"tag":76,"props":91,"children":92},{},[93],{"type":55,"value":94},"import pandas as pd\n",{"type":49,"tag":76,"props":96,"children":98},{"class":78,"line":97},3,[99],{"type":49,"tag":76,"props":100,"children":101},{},[102],{"type":55,"value":103},"# To this:\n",{"type":49,"tag":76,"props":105,"children":107},{"class":78,"line":106},4,[108],{"type":49,"tag":76,"props":109,"children":110},{},[111],{"type":55,"value":112},"import chdb.datastore as pd\n",{"type":49,"tag":76,"props":114,"children":116},{"class":78,"line":115},5,[117],{"type":49,"tag":76,"props":118,"children":119},{},[120],{"type":55,"value":121},"# Everything else stays the same.\n",{"type":49,"tag":123,"props":124,"children":125},"p",{},[126,128,134,136,142,144,150],{"type":55,"value":127},"DataStore is a ",{"type":49,"tag":129,"props":130,"children":131},"strong",{},[132],{"type":55,"value":133},"lazy, ClickHouse-backed pandas replacement",{"type":55,"value":135},". Your existing pandas code works unchanged — but operations compile to optimized SQL and execute only when results are needed (e.g., ",{"type":49,"tag":72,"props":137,"children":139},{"className":138},[],[140],{"type":55,"value":141},"print()",{"type":55,"value":143},", ",{"type":49,"tag":72,"props":145,"children":147},{"className":146},[],[148],{"type":55,"value":149},"len()",{"type":55,"value":151},", iteration).",{"type":49,"tag":65,"props":153,"children":157},{"className":154,"code":155,"language":156,"meta":69,"style":69},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","pip install chdb\n","bash",[158],{"type":49,"tag":72,"props":159,"children":160},{"__ignoreMap":69},[161],{"type":49,"tag":76,"props":162,"children":163},{"class":78,"line":79},[164,170,176],{"type":49,"tag":76,"props":165,"children":167},{"style":166},"--shiki-light:#E2931D;--shiki-default:#FFCB6B;--shiki-dark:#FFCB6B",[168],{"type":55,"value":169},"pip",{"type":49,"tag":76,"props":171,"children":173},{"style":172},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[174],{"type":55,"value":175}," install",{"type":49,"tag":76,"props":177,"children":178},{"style":172},[179],{"type":55,"value":180}," chdb\n",{"type":49,"tag":58,"props":182,"children":184},{"id":183},"decision-tree-pick-the-right-approach",[185],{"type":55,"value":186},"Decision Tree: Pick the Right Approach",{"type":49,"tag":65,"props":188,"children":192},{"className":189,"code":191,"language":55},[190],"language-text","1. \"I have a file\u002Fdatabase and want to analyze it with pandas\"\n   → DataStore.from_file() \u002F from_mysql() \u002F from_s3() etc.\n   → See references\u002Fconnectors.md\n\n2. \"I need to join data from different sources\"\n   → Create DataStores from each source, use .join()\n   → See examples\u002Fexamples.md #3-5\n\n3. \"My pandas code is too slow\"\n   → import chdb.datastore as pd — change one line, keep the rest\n\n4. \"I need raw SQL queries\"\n   → Use the chdb-sql skill instead\n",[193],{"type":49,"tag":72,"props":194,"children":195},{"__ignoreMap":69},[196],{"type":55,"value":191},{"type":49,"tag":58,"props":198,"children":200},{"id":199},"connect-to-any-data-source-one-pattern",[201],{"type":55,"value":202},"Connect to Any Data Source — One Pattern",{"type":49,"tag":65,"props":204,"children":206},{"className":67,"code":205,"language":25,"meta":69,"style":69},"from datastore import DataStore\n\n# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)\nds = DataStore.from_file(\"sales.parquet\")\n\n# Database\nds = DataStore.from_mysql(host=\"db:3306\", database=\"shop\", table=\"orders\", user=\"root\", password=\"pass\")\n\n# Cloud storage\nds = DataStore.from_s3(\"s3:\u002F\u002Fbucket\u002Fdata.parquet\", nosign=True)\n\n# URI shorthand — auto-detects source type\nds = DataStore.uri(\"mysql:\u002F\u002Froot:pass@db:3306\u002Fshop\u002Forders\")\n",[207],{"type":49,"tag":72,"props":208,"children":209},{"__ignoreMap":69},[210,218,227,235,243,250,259,268,276,285,294,302,311],{"type":49,"tag":76,"props":211,"children":212},{"class":78,"line":79},[213],{"type":49,"tag":76,"props":214,"children":215},{},[216],{"type":55,"value":217},"from datastore import DataStore\n",{"type":49,"tag":76,"props":219,"children":220},{"class":78,"line":88},[221],{"type":49,"tag":76,"props":222,"children":224},{"emptyLinePlaceholder":223},true,[225],{"type":55,"value":226},"\n",{"type":49,"tag":76,"props":228,"children":229},{"class":78,"line":97},[230],{"type":49,"tag":76,"props":231,"children":232},{},[233],{"type":55,"value":234},"# Local file (auto-detects .parquet, .csv, .json, .arrow, .orc, .avro, .tsv, .xml)\n",{"type":49,"tag":76,"props":236,"children":237},{"class":78,"line":106},[238],{"type":49,"tag":76,"props":239,"children":240},{},[241],{"type":55,"value":242},"ds = DataStore.from_file(\"sales.parquet\")\n",{"type":49,"tag":76,"props":244,"children":245},{"class":78,"line":115},[246],{"type":49,"tag":76,"props":247,"children":248},{"emptyLinePlaceholder":223},[249],{"type":55,"value":226},{"type":49,"tag":76,"props":251,"children":253},{"class":78,"line":252},6,[254],{"type":49,"tag":76,"props":255,"children":256},{},[257],{"type":55,"value":258},"# Database\n",{"type":49,"tag":76,"props":260,"children":262},{"class":78,"line":261},7,[263],{"type":49,"tag":76,"props":264,"children":265},{},[266],{"type":55,"value":267},"ds = DataStore.from_mysql(host=\"db:3306\", database=\"shop\", table=\"orders\", user=\"root\", password=\"pass\")\n",{"type":49,"tag":76,"props":269,"children":271},{"class":78,"line":270},8,[272],{"type":49,"tag":76,"props":273,"children":274},{"emptyLinePlaceholder":223},[275],{"type":55,"value":226},{"type":49,"tag":76,"props":277,"children":279},{"class":78,"line":278},9,[280],{"type":49,"tag":76,"props":281,"children":282},{},[283],{"type":55,"value":284},"# Cloud storage\n",{"type":49,"tag":76,"props":286,"children":288},{"class":78,"line":287},10,[289],{"type":49,"tag":76,"props":290,"children":291},{},[292],{"type":55,"value":293},"ds = DataStore.from_s3(\"s3:\u002F\u002Fbucket\u002Fdata.parquet\", nosign=True)\n",{"type":49,"tag":76,"props":295,"children":297},{"class":78,"line":296},11,[298],{"type":49,"tag":76,"props":299,"children":300},{"emptyLinePlaceholder":223},[301],{"type":55,"value":226},{"type":49,"tag":76,"props":303,"children":305},{"class":78,"line":304},12,[306],{"type":49,"tag":76,"props":307,"children":308},{},[309],{"type":55,"value":310},"# URI shorthand — auto-detects source type\n",{"type":49,"tag":76,"props":312,"children":314},{"class":78,"line":313},13,[315],{"type":49,"tag":76,"props":316,"children":317},{},[318],{"type":55,"value":319},"ds = DataStore.uri(\"mysql:\u002F\u002Froot:pass@db:3306\u002Fshop\u002Forders\")\n",{"type":49,"tag":123,"props":321,"children":322},{},[323,325],{"type":55,"value":324},"All 16+ sources and URI schemes → ",{"type":49,"tag":326,"props":327,"children":329},"a",{"href":328},"references\u002Fconnectors.md",[330],{"type":55,"value":331},"connectors.md",{"type":49,"tag":58,"props":333,"children":335},{"id":334},"after-connecting-full-pandas-api",[336],{"type":55,"value":337},"After Connecting — Full Pandas API",{"type":49,"tag":65,"props":339,"children":341},{"className":67,"code":340,"language":25,"meta":69,"style":69},"result = ds[ds[\"age\"] > 25]                                          # filter\nresult = ds[[\"name\", \"city\"]]                                        # select columns\nresult = ds.sort_values(\"revenue\", ascending=False)                  # sort\nresult = ds.groupby(\"dept\")[\"salary\"].mean()                         # groupby\nresult = ds.assign(margin=lambda x: x[\"profit\"] \u002F x[\"revenue\"])     # computed column\nds[\"name\"].str.upper()                                               # string accessor\nds[\"date\"].dt.year                                                   # datetime accessor\nresult = ds1.join(ds2, on=\"id\")                                      # join\nresult = ds.head(10)                                                 # preview\nprint(ds.to_sql())                                                   # see generated SQL\n",[342],{"type":49,"tag":72,"props":343,"children":344},{"__ignoreMap":69},[345,353,361,369,377,385,393,401,409,417],{"type":49,"tag":76,"props":346,"children":347},{"class":78,"line":79},[348],{"type":49,"tag":76,"props":349,"children":350},{},[351],{"type":55,"value":352},"result = ds[ds[\"age\"] > 25]                                          # filter\n",{"type":49,"tag":76,"props":354,"children":355},{"class":78,"line":88},[356],{"type":49,"tag":76,"props":357,"children":358},{},[359],{"type":55,"value":360},"result = ds[[\"name\", \"city\"]]                                        # select columns\n",{"type":49,"tag":76,"props":362,"children":363},{"class":78,"line":97},[364],{"type":49,"tag":76,"props":365,"children":366},{},[367],{"type":55,"value":368},"result = ds.sort_values(\"revenue\", ascending=False)                  # sort\n",{"type":49,"tag":76,"props":370,"children":371},{"class":78,"line":106},[372],{"type":49,"tag":76,"props":373,"children":374},{},[375],{"type":55,"value":376},"result = ds.groupby(\"dept\")[\"salary\"].mean()                         # groupby\n",{"type":49,"tag":76,"props":378,"children":379},{"class":78,"line":115},[380],{"type":49,"tag":76,"props":381,"children":382},{},[383],{"type":55,"value":384},"result = ds.assign(margin=lambda x: x[\"profit\"] \u002F x[\"revenue\"])     # computed column\n",{"type":49,"tag":76,"props":386,"children":387},{"class":78,"line":252},[388],{"type":49,"tag":76,"props":389,"children":390},{},[391],{"type":55,"value":392},"ds[\"name\"].str.upper()                                               # string accessor\n",{"type":49,"tag":76,"props":394,"children":395},{"class":78,"line":261},[396],{"type":49,"tag":76,"props":397,"children":398},{},[399],{"type":55,"value":400},"ds[\"date\"].dt.year                                                   # datetime accessor\n",{"type":49,"tag":76,"props":402,"children":403},{"class":78,"line":270},[404],{"type":49,"tag":76,"props":405,"children":406},{},[407],{"type":55,"value":408},"result = ds1.join(ds2, on=\"id\")                                      # join\n",{"type":49,"tag":76,"props":410,"children":411},{"class":78,"line":278},[412],{"type":49,"tag":76,"props":413,"children":414},{},[415],{"type":55,"value":416},"result = ds.head(10)                                                 # preview\n",{"type":49,"tag":76,"props":418,"children":419},{"class":78,"line":287},[420],{"type":49,"tag":76,"props":421,"children":422},{},[423],{"type":55,"value":424},"print(ds.to_sql())                                                   # see generated SQL\n",{"type":49,"tag":123,"props":426,"children":427},{},[428,430],{"type":55,"value":429},"209 DataFrame methods supported. Full API → ",{"type":49,"tag":326,"props":431,"children":433},{"href":432},"references\u002Fapi-reference.md",[434],{"type":55,"value":435},"api-reference.md",{"type":49,"tag":58,"props":437,"children":439},{"id":438},"cross-source-join-the-killer-feature",[440],{"type":55,"value":441},"Cross-Source Join — The Killer Feature",{"type":49,"tag":65,"props":443,"children":445},{"className":67,"code":444,"language":25,"meta":69,"style":69},"from datastore import DataStore\n\ncustomers = DataStore.from_mysql(host=\"db:3306\", database=\"crm\", table=\"customers\", user=\"root\", password=\"pass\")\norders = DataStore.from_file(\"orders.parquet\")\n\nresult = (orders\n    .join(customers, left_on=\"customer_id\", right_on=\"id\")\n    .groupby(\"country\")\n    .agg({\"amount\": \"sum\", \"rating\": \"mean\"})\n    .sort_values(\"sum\", ascending=False))\nprint(result)\n",[446],{"type":49,"tag":72,"props":447,"children":448},{"__ignoreMap":69},[449,456,463,471,479,486,494,502,510,518,526],{"type":49,"tag":76,"props":450,"children":451},{"class":78,"line":79},[452],{"type":49,"tag":76,"props":453,"children":454},{},[455],{"type":55,"value":217},{"type":49,"tag":76,"props":457,"children":458},{"class":78,"line":88},[459],{"type":49,"tag":76,"props":460,"children":461},{"emptyLinePlaceholder":223},[462],{"type":55,"value":226},{"type":49,"tag":76,"props":464,"children":465},{"class":78,"line":97},[466],{"type":49,"tag":76,"props":467,"children":468},{},[469],{"type":55,"value":470},"customers = DataStore.from_mysql(host=\"db:3306\", database=\"crm\", table=\"customers\", user=\"root\", password=\"pass\")\n",{"type":49,"tag":76,"props":472,"children":473},{"class":78,"line":106},[474],{"type":49,"tag":76,"props":475,"children":476},{},[477],{"type":55,"value":478},"orders = DataStore.from_file(\"orders.parquet\")\n",{"type":49,"tag":76,"props":480,"children":481},{"class":78,"line":115},[482],{"type":49,"tag":76,"props":483,"children":484},{"emptyLinePlaceholder":223},[485],{"type":55,"value":226},{"type":49,"tag":76,"props":487,"children":488},{"class":78,"line":252},[489],{"type":49,"tag":76,"props":490,"children":491},{},[492],{"type":55,"value":493},"result = (orders\n",{"type":49,"tag":76,"props":495,"children":496},{"class":78,"line":261},[497],{"type":49,"tag":76,"props":498,"children":499},{},[500],{"type":55,"value":501},"    .join(customers, left_on=\"customer_id\", right_on=\"id\")\n",{"type":49,"tag":76,"props":503,"children":504},{"class":78,"line":270},[505],{"type":49,"tag":76,"props":506,"children":507},{},[508],{"type":55,"value":509},"    .groupby(\"country\")\n",{"type":49,"tag":76,"props":511,"children":512},{"class":78,"line":278},[513],{"type":49,"tag":76,"props":514,"children":515},{},[516],{"type":55,"value":517},"    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SKIP this skill for pandas-style DataFrame method-chaining (use chdb-datastore instead) or ClickHouse server administration.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[889,890,891,892,893],{"name":9,"slug":8,"type":13},{"name":21,"slug":22,"type":13},{"name":18,"slug":19,"type":13},{"name":24,"slug":25,"type":13},{"name":894,"slug":895,"type":13},"SQL","sql","2026-04-15T04:56:33.299509",{"slug":898,"name":898,"fn":899,"description":900,"org":901,"tags":902,"stars":26,"repoUrl":27,"updatedAt":912},"clickhouse-architecture-advisor","advise on ClickHouse architectures","MUST USE when designing ClickHouse architectures, selecting between ingestion or modeling patterns, or translating best practices into workload-specific system designs. 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Always read relevant rule files and cite specific rules in responses.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[919,920,923,924,925],{"name":9,"slug":8,"type":13},{"name":921,"slug":922,"type":13},"Code Review","code-review",{"name":909,"slug":910,"type":13},{"name":15,"slug":16,"type":13},{"name":894,"slug":895,"type":13},"2026-04-06T18:07:18.953931",{"slug":928,"name":928,"fn":929,"description":930,"org":931,"tags":932,"stars":26,"repoUrl":27,"updatedAt":944},"clickhouse-js-node-rowbinary","encode and decode ClickHouse RowBinary streams","Generate TypeScript\u002FJavaScript code that reads\u002Fdecodes AND writes\u002Fencodes ClickHouse RowBinary streams for the ClickHouse HTTP server. Use this skill whenever a user wants to parse or produce `RowBinary`, `RowBinaryWithNames`, or `RowBinaryWithNamesAndTypes`. Node.js only, doesn't cover browsers.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[933,936,937,938,941],{"name":934,"slug":935,"type":13},"API Development","api-development",{"name":9,"slug":8,"type":13},{"name":909,"slug":910,"type":13},{"name":939,"slug":940,"type":13},"JavaScript","javascript",{"name":942,"slug":943,"type":13},"TypeScript","typescript","2026-07-01T08:16:19.314713",{"slug":946,"name":946,"fn":947,"description":948,"org":949,"tags":950,"stars":26,"repoUrl":27,"updatedAt":968},"clickhouse-managed-postgres-rca","diagnose performance issues on ClickHouse-managed Postgres","MUST USE when investigating performance issues on a ClickHouse-managed Postgres instance. 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Use when the user wants a Postgres or PostgreSQL database for their application, a local Postgres dev environment, psql access, or a managed\u002Fproduction Postgres in ClickHouse Cloud, or mentions moving a local Postgres to production.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1053,1054,1055,1058,1059],{"name":909,"slug":910,"type":13},{"name":981,"slug":982,"type":13},{"name":1056,"slug":1057,"type":13},"Docker","docker",{"name":984,"slug":985,"type":13},{"name":963,"slug":964,"type":13},"2026-07-28T06:06:07.151976",{"slug":1062,"name":1062,"fn":1063,"description":1064,"org":1065,"tags":1066,"stars":1075,"repoUrl":1076,"updatedAt":1077},"clickhouse-js-node-coding","build Node.js applications with ClickHouse","Write idiomatic application code with the ClickHouse Node.js client (`@clickhouse\u002Fclient`). Use this skill whenever a user is *building* against the Node.js client — configuring the client, pinging, inserting rows in JSON or raw formats, selecting and parsing results, binding query parameters, managing sessions and temporary tables, working with data types or customizing JSON parsing. Do NOT use for browser\u002FWeb client code.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1067,1070,1071,1072],{"name":1068,"slug":1069,"type":13},"Backend","backend",{"name":9,"slug":8,"type":13},{"name":909,"slug":910,"type":13},{"name":1073,"slug":1074,"type":13},"Node.js","node-js",321,"https:\u002F\u002Fgithub.com\u002FClickHouse\u002Fclickhouse-js","2026-05-06T05:41:40.923776",{"slug":1079,"name":1079,"fn":1080,"description":1081,"org":1082,"tags":1083,"stars":1075,"repoUrl":1076,"updatedAt":1089},"clickhouse-js-node-troubleshooting","troubleshoot ClickHouse Node.js client","Troubleshoot and resolve common issues with the ClickHouse Node.js client (@clickhouse\u002Fclient). Use this skill whenever a user reports errors, unexpected behavior, or configuration questions involving the Node.js client specifically — including socket hang-up errors, Keep-Alive problems, stream handling issues, data type mismatches, read-only user restrictions, proxy\u002FTLS setup problems, or long-running query timeouts. Trigger even when the user hasn't precisely named the issue; vague symptoms like \"my inserts keep failing\" or \"connection drops randomly\" in a Node.js context are strong signals to use this skill. Do NOT use for browser\u002FWeb client issues.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1084,1085,1086,1088],{"name":9,"slug":8,"type":13},{"name":953,"slug":954,"type":13},{"name":1073,"slug":1087,"type":13},"nodejs",{"name":942,"slug":943,"type":13},"2026-04-14T04:56:39.447406",{"slug":1091,"name":1091,"fn":1092,"description":1093,"org":1094,"tags":1095,"stars":1107,"repoUrl":1108,"updatedAt":1109},"clickstack-otel-collector","set up OpenTelemetry collector for ClickHouse","Use when a user wants to wire an OpenTelemetry collector into a Managed ClickStack service on ClickHouse Cloud, either by deploying a new local collector (Docker run or Docker Compose) or by configuring their own existing collector, then send rich synthetic telemetry and verify it is visible in ClickStack.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1096,1097,1100,1103,1104],{"name":9,"slug":8,"type":13},{"name":1098,"slug":1099,"type":13},"Logs","logs",{"name":1101,"slug":1102,"type":13},"Metrics","metrics",{"name":959,"slug":960,"type":13},{"name":1105,"slug":1106,"type":13},"OpenTelemetry","opentelemetry",196,"https:\u002F\u002Fgithub.com\u002FClickHouse\u002Fclickhouse-docs","2026-06-11T08:25:17.959323",{"slug":1111,"name":1111,"fn":1112,"description":1113,"org":1114,"tags":1115,"stars":106,"repoUrl":1124,"updatedAt":1125},"setup","set up ClickHouse MCP server connections","Guides users through setting up the ClickHouse MCP server connection bundled with this plugin. Use when the user first installs the plugin or has trouble connecting to ClickHouse.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":9},[1116,1117,1118,1121],{"name":9,"slug":8,"type":13},{"name":909,"slug":910,"type":13},{"name":1119,"slug":1120,"type":13},"Local Development","local-development",{"name":1122,"slug":1123,"type":13},"MCP","mcp","https:\u002F\u002Fgithub.com\u002FClickHouse\u002Fclickhouse-claude-code-plugin","2026-04-19T04:59:29.849185"]