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Use when the user asks what depends on this data, what breaks if something changes, downstream dependencies, or needs to assess change risk before modifying a table or DAG.\n---\n\n# Downstream Lineage: Impacts\n\nAnswer the critical question: \"What breaks if I change this?\"\n\nUse this BEFORE making changes to understand the blast radius.\n\n## Impact Analysis\n\n### Step 1: Identify Direct Consumers\n\nFind everything that reads from this target:\n\n**For Tables:**\n\n1. **Search DAG source code**: Look for DAGs that SELECT from this table\n   - Use `af dags list` to get all DAGs\n   - Use `af dags source \u003Cdag_id>` to search for table references\n   - Look for: `FROM target_table`, `JOIN target_table`\n\n2. **Check for dependent views**:\n   ```sql\n   -- Snowflake\n   SELECT * FROM information_schema.view_table_usage\n   WHERE table_name = '\u003Ctarget_table>'\n\n   -- Or check SHOW VIEWS and search definitions\n   ```\n\n3. **Look for BI tool connections**:\n   - Dashboards often query tables directly\n   - Check for common BI patterns in table naming (rpt_, dashboard_)\n\n### On Astro\n\nIf you're running on Astro, the **Lineage tab** in the Astro UI provides visual dependency graphs across DAGs and datasets, making downstream impact analysis faster. It shows which DAGs consume a given dataset and their current status, reducing the need for manual source code searches.\n\n**For DAGs:**\n\n1. **Check what the DAG produces**: Use `af dags source \u003Cdag_id>` to find output tables\n2. **Then trace those tables' consumers** (recursive)\n\n### Step 2: Build Dependency Tree\n\nMap the full downstream impact:\n\n```\nSOURCE: fct.orders\n    |\n    +-- TABLE: agg.daily_sales --> Dashboard: Executive KPIs\n    |       |\n    |       +-- TABLE: rpt.monthly_summary --> Email: Monthly Report\n    |\n    +-- TABLE: ml.order_features --> Model: Demand Forecasting\n    |\n    +-- DIRECT: Looker Dashboard \"Sales Overview\"\n```\n\n### Step 3: Categorize by Criticality\n\n**Critical** (breaks production):\n- Production dashboards\n- Customer-facing applications\n- Automated reports to executives\n- ML models in production\n- Regulatory\u002Fcompliance reports\n\n**High** (causes significant issues):\n- Internal operational dashboards\n- Analyst workflows\n- Data science experiments\n- Downstream ETL jobs\n\n**Medium** (inconvenient):\n- Ad-hoc analysis tables\n- Development\u002Fstaging copies\n- Historical archives\n\n**Low** (minimal impact):\n- Deprecated tables\n- Unused datasets\n- Test data\n\n### Step 4: Assess Change Risk\n\nFor the proposed change, evaluate:\n\n**Schema Changes** (adding\u002Fremoving\u002Frenaming columns):\n- Which downstream queries will break?\n- Are there SELECT * patterns that will pick up new columns?\n- Which transformations reference the changing columns?\n\n**Data Changes** (values, volumes, timing):\n- Will downstream aggregations still be valid?\n- Are there NULL handling assumptions that will break?\n- Will timing changes affect SLAs?\n\n**Deletion\u002FDeprecation**:\n- Full dependency tree must be migrated first\n- Communication needed for all stakeholders\n\n### Step 5: Find Stakeholders\n\nIdentify who owns downstream assets:\n\n1. **DAG owners**: Check `owners` field in DAG definitions\n2. **Dashboard owners**: Usually in BI tool metadata\n3. **Team ownership**: Look for team naming patterns or documentation\n\n## Output: Impact Report\n\n### Summary\n\"Changing `fct.orders` will impact X tables, Y DAGs, and Z dashboards\"\n\n### Impact Diagram\n```\n                    +--> [agg.daily_sales] --> [Executive Dashboard]\n                    |\n[fct.orders] -------+--> [rpt.order_details] --> [Ops Team Email]\n                    |\n                    +--> [ml.features] --> [Demand Model]\n```\n\n### Detailed Impacts\n\n| Downstream | Type | Criticality | Owner | Notes |\n|------------|------|-------------|-------|-------|\n| agg.daily_sales | Table | Critical | data-eng | Updated hourly |\n| Executive Dashboard | Dashboard | Critical | analytics | CEO views daily |\n| ml.order_features | Table | High | ml-team | Retraining weekly |\n\n### Risk Assessment\n\n| Change Type | Risk Level | Mitigation |\n|-------------|------------|------------|\n| Add column | Low | No action needed |\n| Rename column | High | Update 3 DAGs, 2 dashboards |\n| Delete column | Critical | Full migration plan required |\n| Change data type | Medium | Test downstream aggregations |\n\n### Recommended Actions\n\nBefore making changes:\n1. 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changes:",{"type":60,"tag":108,"props":895,"children":898},{"className":896},[897],"contains-task-list",[899,911,926,935],{"type":60,"tag":112,"props":900,"children":903},{"className":901},[902],"task-list-item",[904,909],{"type":60,"tag":905,"props":906,"children":908},"input",{"disabled":227,"type":907},"checkbox",[],{"type":66,"value":910}," Notify owners: @data-eng, @analytics, @ml-team",{"type":60,"tag":112,"props":912,"children":914},{"className":913},[902],[915,918,920],{"type":60,"tag":905,"props":916,"children":917},{"disabled":227,"type":907},[],{"type":66,"value":919}," Update downstream DAG: ",{"type":60,"tag":132,"props":921,"children":923},{"className":922},[],[924],{"type":66,"value":925},"transform_daily_sales",{"type":60,"tag":112,"props":927,"children":929},{"className":928},[902],[930,933],{"type":60,"tag":905,"props":931,"children":932},{"disabled":227,"type":907},[],{"type":66,"value":934}," Test dashboard: Executive 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",{"type":60,"tag":102,"props":971,"children":972},{},[973],{"type":66,"value":974},"checking-freshness",{"type":66,"value":964},{"type":60,"tag":112,"props":977,"children":978},{},[979,981,986],{"type":66,"value":980},"Debug any broken DAGs: ",{"type":60,"tag":102,"props":982,"children":983},{},[984],{"type":66,"value":985},"debugging-dags",{"type":66,"value":964},{"type":60,"tag":112,"props":988,"children":989},{},[990,992,997],{"type":66,"value":991},"Add manual lineage annotations: ",{"type":60,"tag":102,"props":993,"children":994},{},[995],{"type":66,"value":996},"annotating-task-lineage",{"type":66,"value":964},{"type":60,"tag":112,"props":999,"children":1000},{},[1001,1003,1008],{"type":66,"value":1002},"Build custom lineage extractors: ",{"type":60,"tag":102,"props":1004,"children":1005},{},[1006],{"type":66,"value":1007},"creating-openlineage-extractors",{"type":66,"value":964},{"type":60,"tag":1010,"props":1011,"children":1012},"style",{},[1013],{"type":66,"value":1014},"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":1016,"total":1175},[1017,1031,1043,1060,1073,1088,1098,1111,1126,1140,1150,1163],{"slug":18,"name":18,"fn":1018,"description":1019,"org":1020,"tags":1021,"stars":25,"repoUrl":26,"updatedAt":1030},"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},[1022,1023,1026,1027],{"name":17,"slug":18,"type":15},{"name":1024,"slug":1025,"type":15},"CLI","cli",{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},"Debugging","debugging","2026-04-06T18:01:43.992997",{"slug":1032,"name":1032,"fn":1033,"description":1034,"org":1035,"tags":1036,"stars":25,"repoUrl":26,"updatedAt":1042},"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},[1037,1038,1041],{"name":17,"slug":18,"type":15},{"name":1039,"slug":1040,"type":15},"Approvals","approvals",{"name":23,"slug":24,"type":15},"2026-04-06T18:01:46.758548",{"slug":1044,"name":1044,"fn":1045,"description":1046,"org":1047,"tags":1048,"stars":25,"repoUrl":26,"updatedAt":1059},"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},[1049,1050,1053,1056],{"name":17,"slug":18,"type":15},{"name":1051,"slug":1052,"type":15},"Plugin Development","plugin-development",{"name":1054,"slug":1055,"type":15},"Python","python",{"name":1057,"slug":1058,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":1061,"name":1061,"fn":1062,"description":1063,"org":1064,"tags":1065,"stars":25,"repoUrl":26,"updatedAt":1072},"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},[1066,1067,1068,1069],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1070,"slug":1071,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":1074,"name":1074,"fn":1075,"description":1076,"org":1077,"tags":1078,"stars":25,"repoUrl":26,"updatedAt":1087},"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},[1079,1081,1084,1085],{"name":1080,"slug":750,"type":15},"Analytics",{"name":1082,"slug":1083,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},{"name":1086,"slug":186,"type":15},"SQL","2026-04-06T18:01:49.599775",{"slug":996,"name":996,"fn":1089,"description":1090,"org":1091,"tags":1092,"stars":25,"repoUrl":26,"updatedAt":1097},"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},[1093,1094,1095,1096],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},"2026-04-06T18:02:03.487365",{"slug":1099,"name":1099,"fn":1100,"description":1101,"org":1102,"tags":1103,"stars":25,"repoUrl":26,"updatedAt":1110},"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},[1104,1105,1106,1109],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1107,"slug":1108,"type":15},"ETL","etl",{"name":1054,"slug":1055,"type":15},"2026-04-06T18:01:52.679888",{"slug":1112,"name":1112,"fn":1113,"description":1114,"org":1115,"tags":1116,"stars":25,"repoUrl":26,"updatedAt":1125},"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},[1117,1118,1121,1122],{"name":17,"slug":18,"type":15},{"name":1119,"slug":1120,"type":15},"API Development","api-development",{"name":23,"slug":24,"type":15},{"name":1123,"slug":1124,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":1127,"name":1127,"fn":1128,"description":1129,"org":1130,"tags":1131,"stars":25,"repoUrl":26,"updatedAt":1139},"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},[1132,1133,1136],{"name":23,"slug":24,"type":15},{"name":1134,"slug":1135,"type":15},"Engineering","engineering",{"name":1137,"slug":1138,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":1141,"name":1141,"fn":1142,"description":1143,"org":1144,"tags":1145,"stars":25,"repoUrl":26,"updatedAt":1149},"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},[1146,1147,1148],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1134,"slug":1135,"type":15},"2026-07-18T05:11:54.496539",{"slug":1151,"name":1151,"fn":1152,"description":1153,"org":1154,"tags":1155,"stars":25,"repoUrl":26,"updatedAt":1162},"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},[1156,1157,1158,1159],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1107,"slug":1108,"type":15},{"name":1160,"slug":1161,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":974,"name":974,"fn":1164,"description":1165,"org":1166,"tags":1167,"stars":25,"repoUrl":26,"updatedAt":1174},"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},[1168,1169,1170,1173],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":1171,"slug":1172,"type":15},"Data Quality","data-quality",{"name":1107,"slug":1108,"type":15},"2026-04-06T18:02:02.138565",34,{"items":1177,"total":1175},[1178,1185,1191,1198,1205,1212,1219],{"slug":18,"name":18,"fn":1018,"description":1019,"org":1179,"tags":1180,"stars":25,"repoUrl":26,"updatedAt":1030},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1181,1182,1183,1184],{"name":17,"slug":18,"type":15},{"name":1024,"slug":1025,"type":15},{"name":23,"slug":24,"type":15},{"name":1028,"slug":1029,"type":15},{"slug":1032,"name":1032,"fn":1033,"description":1034,"org":1186,"tags":1187,"stars":25,"repoUrl":26,"updatedAt":1042},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1188,1189,1190],{"name":17,"slug":18,"type":15},{"name":1039,"slug":1040,"type":15},{"name":23,"slug":24,"type":15},{"slug":1044,"name":1044,"fn":1045,"description":1046,"org":1192,"tags":1193,"stars":25,"repoUrl":26,"updatedAt":1059},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1194,1195,1196,1197],{"name":17,"slug":18,"type":15},{"name":1051,"slug":1052,"type":15},{"name":1054,"slug":1055,"type":15},{"name":1057,"slug":1058,"type":15},{"slug":1061,"name":1061,"fn":1062,"description":1063,"org":1199,"tags":1200,"stars":25,"repoUrl":26,"updatedAt":1072},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1201,1202,1203,1204],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":1070,"slug":1071,"type":15},{"slug":1074,"name":1074,"fn":1075,"description":1076,"org":1206,"tags":1207,"stars":25,"repoUrl":26,"updatedAt":1087},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1208,1209,1210,1211],{"name":1080,"slug":750,"type":15},{"name":1082,"slug":1083,"type":15},{"name":20,"slug":21,"type":15},{"name":1086,"slug":186,"type":15},{"slug":996,"name":996,"fn":1089,"description":1090,"org":1213,"tags":1214,"stars":25,"repoUrl":26,"updatedAt":1097},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1215,1216,1217,1218],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":13,"slug":14,"type":15},{"slug":1099,"name":1099,"fn":1100,"description":1101,"org":1220,"tags":1221,"stars":25,"repoUrl":26,"updatedAt":1110},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1222,1223,1224,1225],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1107,"slug":1108,"type":15},{"name":1054,"slug":1055,"type":15}]