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Use when the user asks where data comes from, what feeds a table, upstream dependencies, data sources, or needs to understand data origins.\n---\n\n# Upstream Lineage: Sources\n\nTrace the origins of data - answer \"Where does this data come from?\"\n\n## Lineage Investigation\n\n### Step 1: Identify the Target Type\n\nDetermine what we're tracing:\n- **Table**: Trace what populates this table\n- **Column**: Trace where this specific column comes from\n- **DAG**: Trace what data sources this DAG reads from\n\n### Step 2: Find the Producing DAG\n\nTables are typically populated by Airflow DAGs. Find the connection:\n\n1. **Search DAGs by name**: Use `af dags list` and look for DAG names matching the table name\n   - `load_customers` -> `customers` table\n   - `etl_daily_orders` -> `orders` table\n\n2. **Explore DAG source code**: Use `af dags source \u003Cdag_id>` to read the DAG definition\n   - Look for INSERT, MERGE, CREATE TABLE statements\n   - Find the target table in the code\n\n3. **Check DAG tasks**: Use `af tasks list \u003Cdag_id>` to see what operations the DAG performs\n\n### On Astro\n\nIf you're running on Astro, the **Lineage tab** in the Astro UI provides visual lineage exploration across DAGs and datasets. Use it to quickly trace upstream dependencies without manually searching DAG source code.\n\n### On OSS Airflow\n\nUse DAG source code and task logs to trace lineage (no built-in cross-DAG UI).\n\n### Step 3: Trace Data Sources\n\nFrom the DAG code, identify source tables and systems:\n\n**SQL Sources** (look for FROM clauses):\n```python\n# In DAG code:\nSELECT * FROM source_schema.source_table  # \u003C- This is an upstream source\n```\n\n**External Sources** (look for connection references):\n- `S3Operator` -> S3 bucket source\n- `PostgresOperator` -> Postgres database source\n- `SalesforceOperator` -> Salesforce API source\n- `HttpOperator` -> REST API source\n\n**File Sources**:\n- CSV\u002FParquet files in object storage\n- SFTP drops\n- Local file paths\n\n### Step 4: Build the Lineage Chain\n\nRecursively trace each source:\n\n```\nTARGET: analytics.orders_daily\n    ^\n    +-- DAG: etl_daily_orders\n            ^\n            +-- SOURCE: raw.orders (table)\n            |       ^\n            |       +-- DAG: ingest_orders\n            |               ^\n            |               +-- SOURCE: Salesforce API (external)\n            |\n            +-- SOURCE: dim.customers (table)\n                    ^\n                    +-- DAG: load_customers\n                            ^\n                            +-- SOURCE: PostgreSQL (external DB)\n```\n\n### Step 5: Check Source Health\n\nFor each upstream source:\n- **Tables**: Check freshness with the **checking-freshness** skill\n- **DAGs**: Check recent run status with `af dags stats`\n- **External systems**: Note connection info from DAG code\n\n## Lineage for Columns\n\nWhen tracing a specific column:\n\n1. Find the column in the target table schema\n2. Search DAG source code for references to that column name\n3. Trace through transformations:\n   - Direct mappings: `source.col AS target_col`\n   - Transformations: `COALESCE(a.col, b.col) AS target_col`\n   - Aggregations: `SUM(detail.amount) AS total_amount`\n\n## Output: Lineage Report\n\n### Summary\nOne-line answer: \"This table is populated by DAG X from sources Y and Z\"\n\n### Lineage Diagram\n```\n[Salesforce] --> [raw.opportunities] --> [stg.opportunities] --> [fct.sales]\n                        |                        |\n                   DAG: ingest_sfdc         DAG: transform_sales\n```\n\n### Source Details\n\n| Source | Type | Connection | Freshness | Owner |\n|--------|------|------------|-----------|-------|\n| raw.orders | Table | Internal | 2h ago | data-team |\n| Salesforce | API | salesforce_conn | Real-time | sales-ops |\n\n### Transformation Chain\nDescribe how data flows and transforms:\n1. 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",{"type":60,"tag":102,"props":796,"children":797},{},[798],{"type":66,"value":464},{"type":66,"value":466},{"type":60,"tag":98,"props":801,"children":802},{},[803,805,810],{"type":66,"value":804},"Debug source DAG: ",{"type":60,"tag":102,"props":806,"children":807},{},[808],{"type":66,"value":809},"debugging-dags",{"type":66,"value":466},{"type":60,"tag":98,"props":812,"children":813},{},[814,816,821],{"type":66,"value":815},"Trace downstream impacts: ",{"type":60,"tag":102,"props":817,"children":818},{},[819],{"type":66,"value":820},"tracing-downstream-lineage",{"type":66,"value":466},{"type":60,"tag":98,"props":823,"children":824},{},[825,827,832],{"type":66,"value":826},"Add manual lineage annotations: ",{"type":60,"tag":102,"props":828,"children":829},{},[830],{"type":66,"value":831},"annotating-task-lineage",{"type":66,"value":466},{"type":60,"tag":98,"props":834,"children":835},{},[836,838,843],{"type":66,"value":837},"Build custom lineage extractors: ",{"type":60,"tag":102,"props":839,"children":840},{},[841],{"type":66,"value":842},"creating-openlineage-extractors",{"type":66,"value":466},{"type":60,"tag":845,"props":846,"children":847},"style",{},[848],{"type":66,"value":849},"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":851,"total":1011},[852,866,878,894,907,924,934,947,962,976,986,999],{"slug":18,"name":18,"fn":853,"description":854,"org":855,"tags":856,"stars":25,"repoUrl":26,"updatedAt":865},"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},[857,858,861,862],{"name":17,"slug":18,"type":15},{"name":859,"slug":860,"type":15},"CLI","cli",{"name":23,"slug":24,"type":15},{"name":863,"slug":864,"type":15},"Debugging","debugging","2026-04-06T18:01:43.992997",{"slug":867,"name":867,"fn":868,"description":869,"org":870,"tags":871,"stars":25,"repoUrl":26,"updatedAt":877},"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},[872,873,876],{"name":17,"slug":18,"type":15},{"name":874,"slug":875,"type":15},"Approvals","approvals",{"name":23,"slug":24,"type":15},"2026-04-06T18:01:46.758548",{"slug":879,"name":879,"fn":880,"description":881,"org":882,"tags":883,"stars":25,"repoUrl":26,"updatedAt":893},"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},[884,885,888,890],{"name":17,"slug":18,"type":15},{"name":886,"slug":887,"type":15},"Plugin Development","plugin-development",{"name":889,"slug":304,"type":15},"Python",{"name":891,"slug":892,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":895,"name":895,"fn":896,"description":897,"org":898,"tags":899,"stars":25,"repoUrl":26,"updatedAt":906},"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},[900,901,902,903],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":904,"slug":905,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":908,"name":908,"fn":909,"description":910,"org":911,"tags":912,"stars":25,"repoUrl":26,"updatedAt":923},"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},[913,916,919,920],{"name":914,"slug":915,"type":15},"Analytics","analytics",{"name":917,"slug":918,"type":15},"Data Analysis","data-analysis",{"name":20,"slug":21,"type":15},{"name":921,"slug":922,"type":15},"SQL","sql","2026-04-06T18:01:49.599775",{"slug":831,"name":831,"fn":925,"description":926,"org":927,"tags":928,"stars":25,"repoUrl":26,"updatedAt":933},"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},[929,930,931,932],{"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":935,"name":935,"fn":936,"description":937,"org":938,"tags":939,"stars":25,"repoUrl":26,"updatedAt":946},"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},[940,941,942,945],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":943,"slug":944,"type":15},"ETL","etl",{"name":889,"slug":304,"type":15},"2026-04-06T18:01:52.679888",{"slug":948,"name":948,"fn":949,"description":950,"org":951,"tags":952,"stars":25,"repoUrl":26,"updatedAt":961},"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},[953,954,957,958],{"name":17,"slug":18,"type":15},{"name":955,"slug":956,"type":15},"API Development","api-development",{"name":23,"slug":24,"type":15},{"name":959,"slug":960,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":963,"name":963,"fn":964,"description":965,"org":966,"tags":967,"stars":25,"repoUrl":26,"updatedAt":975},"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},[968,969,972],{"name":23,"slug":24,"type":15},{"name":970,"slug":971,"type":15},"Engineering","engineering",{"name":973,"slug":974,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":977,"name":977,"fn":978,"description":979,"org":980,"tags":981,"stars":25,"repoUrl":26,"updatedAt":985},"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},[982,983,984],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":970,"slug":971,"type":15},"2026-07-18T05:11:54.496539",{"slug":987,"name":987,"fn":988,"description":989,"org":990,"tags":991,"stars":25,"repoUrl":26,"updatedAt":998},"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},[992,993,994,995],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":943,"slug":944,"type":15},{"name":996,"slug":997,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":464,"name":464,"fn":1000,"description":1001,"org":1002,"tags":1003,"stars":25,"repoUrl":26,"updatedAt":1010},"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},[1004,1005,1006,1009],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":1007,"slug":1008,"type":15},"Data Quality","data-quality",{"name":943,"slug":944,"type":15},"2026-04-06T18:02:02.138565",34,{"items":1013,"total":1011},[1014,1021,1027,1034,1041,1048,1055],{"slug":18,"name":18,"fn":853,"description":854,"org":1015,"tags":1016,"stars":25,"repoUrl":26,"updatedAt":865},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1017,1018,1019,1020],{"name":17,"slug":18,"type":15},{"name":859,"slug":860,"type":15},{"name":23,"slug":24,"type":15},{"name":863,"slug":864,"type":15},{"slug":867,"name":867,"fn":868,"description":869,"org":1022,"tags":1023,"stars":25,"repoUrl":26,"updatedAt":877},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1024,1025,1026],{"name":17,"slug":18,"type":15},{"name":874,"slug":875,"type":15},{"name":23,"slug":24,"type":15},{"slug":879,"name":879,"fn":880,"description":881,"org":1028,"tags":1029,"stars":25,"repoUrl":26,"updatedAt":893},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1030,1031,1032,1033],{"name":17,"slug":18,"type":15},{"name":886,"slug":887,"type":15},{"name":889,"slug":304,"type":15},{"name":891,"slug":892,"type":15},{"slug":895,"name":895,"fn":896,"description":897,"org":1035,"tags":1036,"stars":25,"repoUrl":26,"updatedAt":906},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1037,1038,1039,1040],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"name":904,"slug":905,"type":15},{"slug":908,"name":908,"fn":909,"description":910,"org":1042,"tags":1043,"stars":25,"repoUrl":26,"updatedAt":923},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1044,1045,1046,1047],{"name":914,"slug":915,"type":15},{"name":917,"slug":918,"type":15},{"name":20,"slug":21,"type":15},{"name":921,"slug":922,"type":15},{"slug":831,"name":831,"fn":925,"description":926,"org":1049,"tags":1050,"stars":25,"repoUrl":26,"updatedAt":933},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1051,1052,1053,1054],{"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":935,"name":935,"fn":936,"description":937,"org":1056,"tags":1057,"stars":25,"repoUrl":26,"updatedAt":946},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1058,1059,1060,1061],{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":943,"slug":944,"type":15},{"name":889,"slug":304,"type":15}]