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Use when the user asks to profile a table, wants statistics about a dataset, asks about data quality, or needs to understand a table's structure and content. Requires a table name.\n---\n\n# Data Profile\n\nGenerate a comprehensive profile of a table that a new team member could use to understand the data.\n\n## Step 1: Basic Metadata\n\nQuery column metadata:\n\n```sql\nSELECT COLUMN_NAME, DATA_TYPE, COMMENT\nFROM \u003Cdatabase>.INFORMATION_SCHEMA.COLUMNS\nWHERE TABLE_SCHEMA = '\u003Cschema>' AND TABLE_NAME = '\u003Ctable>'\nORDER BY ORDINAL_POSITION\n```\n\nIf the table name isn't fully qualified, search INFORMATION_SCHEMA.TABLES to locate it first.\n\n## Step 2: Size and Shape\n\nRun via `run_sql`:\n\n```sql\nSELECT\n    COUNT(*) as total_rows,\n    COUNT(*) \u002F 1000000.0 as millions_of_rows\nFROM \u003Ctable>\n```\n\n## Step 3: Column-Level Statistics\n\nFor each column, gather appropriate statistics based on data type:\n\n### Numeric Columns\n```sql\nSELECT\n    MIN(column_name) as min_val,\n    MAX(column_name) as max_val,\n    AVG(column_name) as avg_val,\n    STDDEV(column_name) as std_dev,\n    PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY column_name) as median,\n    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count,\n    COUNT(DISTINCT column_name) as distinct_count\nFROM \u003Ctable>\n```\n\n### String Columns\n```sql\nSELECT\n    MIN(LEN(column_name)) as min_length,\n    MAX(LEN(column_name)) as max_length,\n    AVG(LEN(column_name)) as avg_length,\n    SUM(CASE WHEN column_name IS NULL OR column_name = '' THEN 1 ELSE 0 END) as empty_count,\n    COUNT(DISTINCT column_name) as distinct_count\nFROM \u003Ctable>\n```\n\n### Date\u002FTimestamp Columns\n```sql\nSELECT\n    MIN(column_name) as earliest,\n    MAX(column_name) as latest,\n    DATEDIFF('day', MIN(column_name), MAX(column_name)) as date_range_days,\n    SUM(CASE WHEN column_name IS NULL THEN 1 ELSE 0 END) as null_count\nFROM \u003Ctable>\n```\n\n## Step 4: Cardinality Analysis\n\nFor columns that look like categorical\u002Fdimension keys:\n\n```sql\nSELECT\n    column_name,\n    COUNT(*) as frequency,\n    ROUND(COUNT(*) * 100.0 \u002F SUM(COUNT(*)) OVER(), 2) as percentage\nFROM \u003Ctable>\nGROUP BY column_name\nORDER BY frequency DESC\nLIMIT 20\n```\n\nThis reveals:\n- High-cardinality columns (likely IDs or unique values)\n- Low-cardinality columns (likely categories or status fields)\n- Skewed distributions (one value dominates)\n\n## Step 5: Sample Data\n\nGet representative rows:\n\n```sql\nSELECT *\nFROM \u003Ctable>\nLIMIT 10\n```\n\nIf the table is large and you want variety, sample from different time periods or categories.\n\n## Step 6: Data Quality Assessment\n\nSummarize quality across dimensions:\n\n### Completeness\n- Which columns have NULLs? 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together?",{"type":61,"tag":514,"props":682,"children":683},{},[684],{"type":67,"value":685},"Are there logical contradictions?",{"type":61,"tag":76,"props":687,"children":689},{"id":688},"step-7-output-summary",[690],{"type":67,"value":691},"Step 7: Output Summary",{"type":61,"tag":70,"props":693,"children":694},{},[695],{"type":67,"value":696},"Provide a structured profile:",{"type":61,"tag":211,"props":698,"children":700},{"id":699},"overview",[701],{"type":67,"value":702},"Overview",{"type":61,"tag":70,"props":704,"children":705},{},[706],{"type":67,"value":707},"2-3 sentences describing what this table contains, who uses it, and how fresh it is.",{"type":61,"tag":211,"props":709,"children":711},{"id":710},"schema",[712],{"type":67,"value":713},"Schema",{"type":61,"tag":715,"props":716,"children":717},"table",{},[718,752],{"type":61,"tag":719,"props":720,"children":721},"thead",{},[722],{"type":61,"tag":723,"props":724,"children":725},"tr",{},[726,732,737,742,747],{"type":61,"tag":727,"props":728,"children":729},"th",{},[730],{"type":67,"value":731},"Column",{"type":61,"tag":727,"props":733,"children":734},{},[735],{"type":67,"value":736},"Type",{"type":61,"tag":727,"props":738,"children":739},{},[740],{"type":67,"value":741},"Nulls%",{"type":61,"tag":727,"props":743,"children":744},{},[745],{"type":67,"value":746},"Distinct",{"type":61,"tag":727,"props":748,"children":749},{},[750],{"type":67,"value":751},"Description",{"type":61,"tag":753,"props":754,"children":755},"tbody",{},[756],{"type":61,"tag":723,"props":757,"children":758},{},[759,765,769,773,777],{"type":61,"tag":760,"props":761,"children":762},"td",{},[763],{"type":67,"value":764},"...",{"type":61,"tag":760,"props":766,"children":767},{},[768],{"type":67,"value":764},{"type":61,"tag":760,"props":770,"children":771},{},[772],{"type":67,"value":764},{"type":61,"tag":760,"props":774,"children":775},{},[776],{"type":67,"value":764},{"type":61,"tag":760,"props":778,"children":779},{},[780],{"type":67,"value":764},{"type":61,"tag":211,"props":782,"children":784},{"id":783},"key-statistics",[785],{"type":67,"value":786},"Key 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X\u002F10",{"type":61,"tag":211,"props":835,"children":837},{"id":836},"potential-issues",[838],{"type":67,"value":839},"Potential Issues",{"type":61,"tag":70,"props":841,"children":842},{},[843],{"type":67,"value":844},"List any data quality concerns discovered.",{"type":61,"tag":211,"props":846,"children":848},{"id":847},"recommended-queries",[849],{"type":67,"value":850},"Recommended Queries",{"type":61,"tag":70,"props":852,"children":853},{},[854],{"type":67,"value":855},"3-5 useful queries for common questions about this data.",{"type":61,"tag":857,"props":858,"children":859},"style",{},[860],{"type":67,"value":861},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":863,"total":962},[864,881,893,910,923,936,949],{"slug":35,"name":35,"fn":865,"description":866,"org":867,"tags":868,"stars":25,"repoUrl":26,"updatedAt":880},"manage and troubleshoot Airflow via CLI","Queries, manages, and troubleshoots Apache Airflow using the `af` CLI. Use when working with anything related to Airflow - a DAG, a DAG run, a task log, an import or parse error, a broken DAG, or any Airflow operation. Covers listing and triggering DAGs, retrying runs, reading task logs, diagnosing failures, debugging import and parse errors, checking connections, variables and pools, exploring the REST API, and monitoring health (for example \"trigger a pipeline\", \"retry a run\", \"list connections\", \"check Airflow health\", \"why did my DAG fail\"). This is the entrypoint that routes to sibling skills for authoring, testing, deploying, and migrating Airflow 2 to 3. Not for warehouse\u002FSQL analytics on Airflow metadata tables (use analyzing-data); for deep root-cause reports use debugging-dags or airflow-investigation.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[869,871,874,877],{"name":870,"slug":35,"type":15},"Airflow",{"name":872,"slug":873,"type":15},"CLI","cli",{"name":875,"slug":876,"type":15},"Data Pipeline","data-pipeline",{"name":878,"slug":879,"type":15},"Debugging","debugging","2026-04-06T18:01:43.992997",{"slug":882,"name":882,"fn":883,"description":884,"org":885,"tags":886,"stars":25,"repoUrl":26,"updatedAt":892},"airflow-hitl","add human-in-the-loop steps to Airflow DAGs","Builds human-in-the-loop (HITL) Airflow workflows - approval gates, form input, and human-driven branching. Use when a DAG needs a human in the loop - an approval or reject step, sign-off before a task runs, a decision or approval UI, branching on a human choice, or collecting form input mid-run; also on mentions of ApprovalOperator, HITLOperator, HITLBranchOperator, HITLEntryOperator, or HITLTrigger. Requires Airflow 3.1+. Not for AI\u002FLLM task calls (see migrating-ai-sdk-to-common-ai).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[887,888,891],{"name":870,"slug":35,"type":15},{"name":889,"slug":890,"type":15},"Approvals","approvals",{"name":875,"slug":876,"type":15},"2026-04-06T18:01:46.758548",{"slug":894,"name":894,"fn":895,"description":896,"org":897,"tags":898,"stars":25,"repoUrl":26,"updatedAt":909},"airflow-plugins","build Airflow UI plugins","Builds Airflow 3.1+ plugins that embed FastAPI apps, custom UI pages, React components, middleware, macros, and operator links directly into the Airflow UI. Use when building anything custom inside Airflow 3.1+ that involves Python and a browser-facing interface - creating an Airflow plugin, adding a custom UI page or nav entry, building FastAPI-backed endpoints inside Airflow, serving static assets from a plugin, embedding a React app, adding middleware to the API server, creating custom operator extra links, or calling the Airflow REST API from inside a plugin; also when AirflowPlugin, fastapi_apps, external_views, react_apps, or plugin registration come up.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[899,900,903,906],{"name":870,"slug":35,"type":15},{"name":901,"slug":902,"type":15},"Plugin Development","plugin-development",{"name":904,"slug":905,"type":15},"Python","python",{"name":907,"slug":908,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":911,"name":911,"fn":912,"description":913,"org":914,"tags":915,"stars":25,"repoUrl":26,"updatedAt":922},"airflow-state-store","persist Airflow task and asset state","Persists task and asset state across retries and DAG runs using Airflow 3.3's AIP-103 key\u002Fvalue stores (`task_state_store`, `asset_state_store`) and the crash-safe `ResumableJobMixin`. Use when the user asks about task state store, checkpointing in tasks, persisting state across retries, job IDs surviving worker crashes, watermarks, asset metadata, resumable tasks, crash-safe operators, or \"what's new in Airflow 3.3\". Also use proactively when reading a DAG that uses Variables or XCom for intra-task coordination state — flag the anti-pattern and recommend task_state_store or asset_state_store instead. Requires Airflow 3.3+.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[916,917,918,919],{"name":870,"slug":35,"type":15},{"name":17,"slug":18,"type":15},{"name":875,"slug":876,"type":15},{"name":920,"slug":921,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":924,"name":924,"fn":925,"description":926,"org":927,"tags":928,"stars":25,"repoUrl":26,"updatedAt":935},"analyzing-data","query data warehouses for business questions","Queries the data warehouse with SQL and answers business questions about data. Use when answering anything that needs warehouse data - counts, metrics, trends, aggregations, joins across tables, data lookups, or ad-hoc SQL analysis (for example \"who uses X\", \"how many Y\", \"show me Z\", \"find customers\", \"what is the count\").",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[929,932,933,934],{"name":930,"slug":931,"type":15},"Analytics","analytics",{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},"2026-04-06T18:01:49.599775",{"slug":937,"name":937,"fn":938,"description":939,"org":940,"tags":941,"stars":25,"repoUrl":26,"updatedAt":948},"annotating-task-lineage","annotate Airflow tasks with data lineage","Annotate Airflow tasks with data lineage using inlets and outlets. Use when the user wants to add lineage metadata to tasks, specify input\u002Foutput datasets, or enable lineage tracking for operators without built-in OpenLineage extraction.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[942,943,944,945],{"name":870,"slug":35,"type":15},{"name":17,"slug":18,"type":15},{"name":875,"slug":876,"type":15},{"name":946,"slug":947,"type":15},"Observability","observability","2026-04-06T18:02:03.487365",{"slug":950,"name":950,"fn":951,"description":952,"org":953,"tags":954,"stars":25,"repoUrl":26,"updatedAt":961},"authoring-dags","author Airflow DAGs","Workflow and best practices for writing Apache Airflow DAGs. Use when creating a new DAG, write pipeline code, handling questions about DAG patterns and conventions or extending an existing DAG with a follow-up\u002Fdownstream task. ANY request shaped like 'add a DAG named X', 'write a pipeline', 'add a task that runs after Y', or 'extend the DAG'. For testing and debugging DAGs, see the testing-dags skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[955,956,957,960],{"name":870,"slug":35,"type":15},{"name":875,"slug":876,"type":15},{"name":958,"slug":959,"type":15},"ETL","etl",{"name":904,"slug":905,"type":15},"2026-04-06T18:01:52.679888",34,{"items":964,"total":962},[965,972,978,985,992,999,1006,1013,1028,1042,1052,1065],{"slug":35,"name":35,"fn":865,"description":866,"org":966,"tags":967,"stars":25,"repoUrl":26,"updatedAt":880},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[968,969,970,971],{"name":870,"slug":35,"type":15},{"name":872,"slug":873,"type":15},{"name":875,"slug":876,"type":15},{"name":878,"slug":879,"type":15},{"slug":882,"name":882,"fn":883,"description":884,"org":973,"tags":974,"stars":25,"repoUrl":26,"updatedAt":892},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[975,976,977],{"name":870,"slug":35,"type":15},{"name":889,"slug":890,"type":15},{"name":875,"slug":876,"type":15},{"slug":894,"name":894,"fn":895,"description":896,"org":979,"tags":980,"stars":25,"repoUrl":26,"updatedAt":909},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[981,982,983,984],{"name":870,"slug":35,"type":15},{"name":901,"slug":902,"type":15},{"name":904,"slug":905,"type":15},{"name":907,"slug":908,"type":15},{"slug":911,"name":911,"fn":912,"description":913,"org":986,"tags":987,"stars":25,"repoUrl":26,"updatedAt":922},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[988,989,990,991],{"name":870,"slug":35,"type":15},{"name":17,"slug":18,"type":15},{"name":875,"slug":876,"type":15},{"name":920,"slug":921,"type":15},{"slug":924,"name":924,"fn":925,"description":926,"org":993,"tags":994,"stars":25,"repoUrl":26,"updatedAt":935},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[995,996,997,998],{"name":930,"slug":931,"type":15},{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"slug":937,"name":937,"fn":938,"description":939,"org":1000,"tags":1001,"stars":25,"repoUrl":26,"updatedAt":948},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1002,1003,1004,1005],{"name":870,"slug":35,"type":15},{"name":17,"slug":18,"type":15},{"name":875,"slug":876,"type":15},{"name":946,"slug":947,"type":15},{"slug":950,"name":950,"fn":951,"description":952,"org":1007,"tags":1008,"stars":25,"repoUrl":26,"updatedAt":961},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1009,1010,1011,1012],{"name":870,"slug":35,"type":15},{"name":875,"slug":876,"type":15},{"name":958,"slug":959,"type":15},{"name":904,"slug":905,"type":15},{"slug":1014,"name":1014,"fn":1015,"description":1016,"org":1017,"tags":1018,"stars":25,"repoUrl":26,"updatedAt":1027},"authoring-go-sdk-tasks","implement Airflow tasks in Go","Writes Airflow task logic in Go using the Airflow Go SDK. Use when the user wants to implement Airflow tasks in Go, asks about `BundleProvider`\u002F`RegisterDags`, the `bundlev1` Registry\u002FDag interfaces, registering Go tasks (`AddTask`\u002F`AddTaskWithName`), dependency injection by parameter type (`context.Context`, `sdk.TIRunContext`, `*slog.Logger`, `sdk.Client`), or reading connections\u002Fvariables\u002FXComs from Go. This skill covers the Go-specific native API; the shared Python-stub pattern and conceptual model live in authoring-language-sdk-tasks. For building\u002Fpacking\u002Fshipping the bundle see deploying-go-sdk-bundles; for coordinator config see configuring-airflow-language-sdks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1019,1020,1023,1024],{"name":870,"slug":35,"type":15},{"name":1021,"slug":1022,"type":15},"API Development","api-development",{"name":875,"slug":876,"type":15},{"name":1025,"slug":1026,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":1029,"name":1029,"fn":1030,"description":1031,"org":1032,"tags":1033,"stars":25,"repoUrl":26,"updatedAt":1041},"authoring-java-sdk-tasks","implement Airflow tasks in Java","Writes Airflow task logic in Java, Kotlin, or any JVM language using the Airflow Java SDK. Use when the user wants to implement Airflow tasks in Java\u002FJVM, asks about `@Builder.Dag`\u002F`@Builder.Task`\u002F`@Builder.XCom`, the `Task`\u002F`BundleBuilder` interfaces, reading connections\u002Fvariables\u002FXComs from Java, the JSON-to-Java type mapping, or logging from Java tasks. This skill covers the Java-specific native API; the shared Python-stub pattern and conceptual model live in authoring-language-sdk-tasks. For building\u002Fshipping the bundle see deploying-java-sdk-bundles; for coordinator config see configuring-airflow-language-sdks.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1034,1035,1038],{"name":875,"slug":876,"type":15},{"name":1036,"slug":1037,"type":15},"Engineering","engineering",{"name":1039,"slug":1040,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":1043,"name":1043,"fn":1044,"description":1045,"org":1046,"tags":1047,"stars":25,"repoUrl":26,"updatedAt":1051},"authoring-language-sdk-tasks","implement non-Python Airflow tasks","The language-neutral foundation for Airflow language SDKs — implement task logic in a non-Python language while the DAG stays in Python. Use when the user wants to run an Airflow task in another language (Java, Kotlin, Go, or other JVM\u002Fnative languages), asks how the Python `@task.stub` pairs with native task code, how task\u002FDAG IDs must match across the two sides, how data passes via XCom as JSON, or which language SDKs exist. This skill owns the shared Python-stub pattern and conceptual model; for a specific language's native API, build, and runtime, use that language's skill (e.g. authoring-java-sdk-tasks, authoring-go-sdk-tasks).",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1048,1049,1050],{"name":870,"slug":35,"type":15},{"name":875,"slug":876,"type":15},{"name":1036,"slug":1037,"type":15},"2026-07-18T05:11:54.496539",{"slug":1053,"name":1053,"fn":1054,"description":1055,"org":1056,"tags":1057,"stars":25,"repoUrl":26,"updatedAt":1064},"blueprint","build reusable Airflow task group templates","Define reusable Airflow task group templates with Pydantic validation and compose DAGs from YAML. Use when creating blueprint templates, composing DAGs from YAML, validating configurations, or enabling no-code DAG authoring for non-engineers.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1058,1059,1060,1061],{"name":870,"slug":35,"type":15},{"name":875,"slug":876,"type":15},{"name":958,"slug":959,"type":15},{"name":1062,"slug":1063,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":1066,"name":1066,"fn":1067,"description":1068,"org":1069,"tags":1070,"stars":25,"repoUrl":26,"updatedAt":1075},"checking-freshness","check data freshness in warehouses","Quick data freshness check. Use when the user asks if data is up to date, when a table was last updated, if data is stale, or needs to verify data currency before using it.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1071,1072,1073,1074],{"name":870,"slug":35,"type":15},{"name":17,"slug":18,"type":15},{"name":13,"slug":14,"type":15},{"name":958,"slug":959,"type":15},"2026-04-06T18:02:02.138565"]