[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-astronomer-airflow-hitl":3,"mdc-23d7fv-key":52,"related-org-astronomer-airflow-hitl":1541,"related-repo-astronomer-airflow-hitl":1701},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":22,"repoUrl":23,"updatedAt":24,"license":25,"forks":26,"topics":27,"repo":47,"sourceUrl":50,"mdContent":51},"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},"astronomer","Astronomer","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fastronomer.png",[12,16,19],{"name":13,"slug":14,"type":15},"Airflow","airflow","tag",{"name":17,"slug":18,"type":15},"Data Pipeline","data-pipeline",{"name":20,"slug":21,"type":15},"Approvals","approvals",412,"https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fagents","2026-04-06T18:01:46.758548",null,55,[28,29,30,31,14,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"agentic-workflow","agents","ai","ai-agents","apache-airflow","claude","cursor","dag","data-engineering","data-pipelines","dbt","llm","mcp","orchestrator","skills","workflow-automation","workflow-management","workflow-orchestration","workflows",{"repoUrl":23,"stars":22,"forks":26,"topics":48,"description":49},[28,29,30,31,14,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46],"AI agent tooling for data engineering workflows.","https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fagents\u002Ftree\u002FHEAD\u002Fskills\u002Fairflow-hitl","---\nname: airflow-hitl\ndescription: 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).\n---\n\n# Airflow Human-in-the-Loop Operators\n\nPause a DAG until a human responds via the Airflow UI or REST API. HITL operators are deferrable — they release their worker slot while waiting.\n\n> **Requires Airflow 3.1+** (`af config version`).\n>\n> **UI location**: Browse → Required Actions. Respond from the task instance page's Required Actions tab.\n>\n> **Cross-references**: `migrating-ai-sdk-to-common-ai` for AI\u002FLLM task decorators; `airflow` for registry and API discovery commands used below.\n\n---\n\n## Step 1 — Pick the capability you need\n\n| Capability | Class (verify in Step 2) |\n|---|---|\n| Approve or reject; downstream skips on reject | `ApprovalOperator` |\n| Present N options and return which were chosen | `HITLOperator` |\n| Branch to one or more downstream tasks based on a choice | `HITLBranchOperator` |\n| Collect a form (no approve\u002Fselect step) | `HITLEntryOperator` |\n| Use the HITL trigger directly (advanced \u002F custom operators) | `HITLTrigger` |\n\nThis is the only place class names are hardcoded. The provider adds, renames, and removes params across releases — do not copy parameter lists from memory. Fetch the current signature before writing code.\n\n---\n\n## Step 2 — Discover the current signatures from the Airflow Registry\n\nBefore writing HITL code, run these to see the live roster and constructor params (see the `airflow` skill for the full `af registry` reference):\n\n```bash\n# Every HITL-related module in the standard provider\naf registry modules standard \\\n  | jq '.modules[] | select(.import_path | test(\"\\\\.hitl\\\\.\")) | {name, type, import_path, short_description, docs_url}'\n\n# Constructor signatures: name, type, default, required, description\naf registry parameters standard \\\n  | jq '.classes | to_entries[] | select(.key | test(\"\\\\.hitl\\\\.\")) | {fqn: .key, parameters: .value.parameters}'\n\n# Pin to the exact installed provider version\naf config providers \\\n  | jq '.providers[] | select(.package_name == \"apache-airflow-providers-standard\") | .version'\n# then: af registry parameters standard --version \u003CVERSION>\n```\n\nIf the registry shows a param that this skill does not mention, prefer the registry. If the registry shows a class that is not in Step 1, treat it as additive — the decision table above may be stale.\n\n---\n\n## Step 3 — Canonical example (approval gate)\n\nStarting point for any HITL task. Adapt by swapping the class name and params per Step 2.\n\n```python\nfrom airflow.providers.standard.operators.hitl import ApprovalOperator\nfrom airflow.sdk import dag, task, chain, Param\nfrom pendulum import datetime\n\n@dag(start_date=datetime(2025, 1, 1), schedule=\"@daily\")\ndef approval_example():\n    @task\n    def prepare():\n        return \"Review quarterly report\"\n\n    approval = ApprovalOperator(\n        task_id=\"approve_report\",\n        subject=\"Report Approval\",\n        body=\"{{ ti.xcom_pull(task_ids='prepare') }}\",\n        defaults=\"Approve\",              # Auto-selected on timeout\n        params={\"comments\": Param(\"\", type=\"string\")},\n    )\n\n    @task\n    def after_approval(result):\n        print(f\"Decision: {result['chosen_options']}\")\n\n    chain(prepare(), approval)\n    after_approval(approval.output)\n\napproval_example()\n```\n\nFor the other classes in Step 1, the shape is the same (`task_id`, `subject`, plus class-specific params). Verify each constructor through Step 2 — for example, `HITLBranchOperator` requires every option either to match a downstream task id directly or to be resolved via a mapping param surfaced in the registry.\n\n---\n\n## Step 4 — Behavior contracts (stable across versions)\n\n### Timeout\n- With `defaults` set: task succeeds on timeout, default option(s) selected.\n- Without `defaults`: task fails on timeout.\n\n### Markdown + Jinja in `body`\n`body` supports Markdown and is Jinja-templatable. Render XCom context directly:\n\n```python\nbody = \"\"\"**Total Budget:** {{ ti.xcom_pull(task_ids='get_budget') }}\n\n| Category | Amount |\n|----------|--------|\n| Marketing | $1M |\n\"\"\"\n```\n\n### Callbacks\nAll HITL operators accept the standard Airflow callback kwargs (`on_success_callback`, `on_failure_callback`, etc.).\n\n### Notifiers\nHITL operators accept a `notifiers` list. Inside a notifier's `notify(context)` method, build a link to the pending task with `HITLOperator.generate_link_to_ui_from_context(context, base_url=...)`.\n\n### Restricting who can respond\nThe parameter name and accepted identifier format depend on the active auth manager. Do **not** hardcode — check which one is active and which kwarg the current provider exposes:\n\n```bash\naf config show | jq '.auth_manager \u002F\u002F .core.auth_manager'\n```\n\nThen look up the current kwarg in Step 2 (at the time of writing it is `assigned_users`, accepting identifiers in whatever format the active auth manager uses — Astro uses the Astro user ID, FabAuthManager uses email, SimpleAuthManager uses username).\n\n---\n\n## Step 5 — Responding from external integrations\n\nFor Slack bots, custom apps, or scripts. Discover the live endpoint rather than hardcoding a path:\n\n```bash\naf api ls --filter hitl           # live endpoint list\naf api spec \\\n  | jq '.paths | to_entries[] | select(.key | test(\"hitl\"))'   # request\u002Fresponse schemas\n```\n\nThe PATCH-to-respond pattern is stable; the exact path is discovered. Typical shape:\n\n```python\nimport os, requests\n\nHOST = os.environ[\"AIRFLOW_HOST\"]\nTOKEN = os.environ[\"AIRFLOW_API_TOKEN\"]\nHEADERS = {\"Authorization\": f\"Bearer {TOKEN}\"}\n\n# List pending — use the path from `af api ls --filter hitl`\nrequests.get(f\"{HOST}\u002F\u003Cpath>\", headers=HEADERS, params={\"state\": \"pending\"})\n\n# Respond — same discovered path family, PATCH\nrequests.patch(\n    f\"{HOST}\u002F\u003Cpath>\u002F{dag_id}\u002F{run_id}\u002F{task_id}\",\n    headers=HEADERS,\n    json={\"chosen_options\": [\"Approve\"], \"params_input\": {\"comments\": \"ok\"}},\n)\n```\n\n---\n\n## Step 6 — Safety checks\n\n- [ ] Airflow version ≥ 3.1 (`af config version`).\n- [ ] Constructor kwargs match the current registry output from Step 2 — no `respondents`-vs-`assigned_users` style drift.\n- [ ] For branching: every option resolves to a downstream task id (directly or via the mapping kwarg from Step 2).\n- [ ] Every value in `defaults` is also in `options`.\n- [ ] `execution_timeout` set; `defaults` configured if timeout should succeed rather than fail.\n- [ ] API token configured if external responders are part of the flow.\n\n---\n\n## References\n\nThe upstream docs URL is surfaced per-module by the registry — do not hardcode:\n\n```bash\naf registry modules standard \\\n  | jq '.modules[] | select(.import_path | test(\"\\\\.hitl\\\\.\")) | {name, docs_url}'\n```\n\n## Related skills\n\n- **airflow** — `af registry`, `af api`, `af config` command reference.\n- **migrating-ai-sdk-to-common-ai** — AI\u002FLLM task decorators and GenAI patterns (common-ai provider).\n- **authoring-dags** — general DAG writing best practices.\n- **testing-dags** — iterative test → debug → fix cycles.\n",{"data":53,"body":54},{"name":4,"description":6},{"type":55,"children":56},"root",[57,66,72,131,135,142,255,260,263,269,289,516,521,524,530,535,760,788,791,797,804,834,846,856,910,916,936,942,970,976,988,1033,1046,1049,1055,1060,1149,1154,1278,1281,1287,1399,1402,1408,1413,1467,1473,1535],{"type":58,"tag":59,"props":60,"children":62},"element","h1",{"id":61},"airflow-human-in-the-loop-operators",[63],{"type":64,"value":65},"text","Airflow Human-in-the-Loop Operators",{"type":58,"tag":67,"props":68,"children":69},"p",{},[70],{"type":64,"value":71},"Pause a DAG until a human responds via the Airflow UI or REST API. 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Adapt by swapping the class name and params per Step 2.",{"type":58,"tag":290,"props":536,"children":540},{"className":537,"code":538,"language":539,"meta":295,"style":295},"language-python shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","from airflow.providers.standard.operators.hitl import ApprovalOperator\nfrom airflow.sdk import dag, task, chain, Param\nfrom pendulum import datetime\n\n@dag(start_date=datetime(2025, 1, 1), schedule=\"@daily\")\ndef approval_example():\n    @task\n    def prepare():\n        return \"Review quarterly report\"\n\n    approval = ApprovalOperator(\n        task_id=\"approve_report\",\n        subject=\"Report Approval\",\n        body=\"{{ ti.xcom_pull(task_ids='prepare') }}\",\n        defaults=\"Approve\",              # Auto-selected on timeout\n        params={\"comments\": Param(\"\", type=\"string\")},\n    )\n\n    @task\n    def after_approval(result):\n        print(f\"Decision: {result['chosen_options']}\")\n\n    chain(prepare(), approval)\n    after_approval(approval.output)\n\napproval_example()\n","python",[541],{"type":58,"tag":88,"props":542,"children":543},{"__ignoreMap":295},[544,552,560,568,575,583,591,599,607,615,622,630,638,647,656,665,674,683,691,699,708,717,725,734,743,751],{"type":58,"tag":301,"props":545,"children":546},{"class":303,"line":304},[547],{"type":58,"tag":301,"props":548,"children":549},{},[550],{"type":64,"value":551},"from airflow.providers.standard.operators.hitl import ApprovalOperator\n",{"type":58,"tag":301,"props":553,"children":554},{"class":303,"line":314},[555],{"type":58,"tag":301,"props":556,"children":557},{},[558],{"type":64,"value":559},"from airflow.sdk import dag, task, chain, Param\n",{"type":58,"tag":301,"props":561,"children":562},{"class":303,"line":346},[563],{"type":58,"tag":301,"props":564,"children":565},{},[566],{"type":64,"value":567},"from pendulum import datetime\n",{"type":58,"tag":301,"props":569,"children":570},{"class":303,"line":376},[571],{"type":58,"tag":301,"props":572,"children":573},{"emptyLinePlaceholder":380},[574],{"type":64,"value":383},{"type":58,"tag":301,"props":576,"children":577},{"class":303,"line":386},[578],{"type":58,"tag":301,"props":579,"children":580},{},[581],{"type":64,"value":582},"@dag(start_date=datetime(2025, 1, 1), schedule=\"@daily\")\n",{"type":58,"tag":301,"props":584,"children":585},{"class":303,"line":395},[586],{"type":58,"tag":301,"props":587,"children":588},{},[589],{"type":64,"value":590},"def approval_example():\n",{"type":58,"tag":301,"props":592,"children":593},{"class":303,"line":420},[594],{"type":58,"tag":301,"props":595,"children":596},{},[597],{"type":64,"value":598},"    @task\n",{"type":58,"tag":301,"props":600,"children":601},{"class":303,"line":445},[602],{"type":58,"tag":301,"props":603,"children":604},{},[605],{"type":64,"value":606},"    def prepare():\n",{"type":58,"tag":301,"props":608,"children":609},{"class":303,"line":453},[610],{"type":58,"tag":301,"props":611,"children":612},{},[613],{"type":64,"value":614},"        return \"Review quarterly report\"\n",{"type":58,"tag":301,"props":616,"children":617},{"class":303,"line":462},[618],{"type":58,"tag":301,"props":619,"children":620},{"emptyLinePlaceholder":380},[621],{"type":64,"value":383},{"type":58,"tag":301,"props":623,"children":624},{"class":303,"line":484},[625],{"type":58,"tag":301,"props":626,"children":627},{},[628],{"type":64,"value":629},"    approval = ApprovalOperator(\n",{"type":58,"tag":301,"props":631,"children":632},{"class":303,"line":509},[633],{"type":58,"tag":301,"props":634,"children":635},{},[636],{"type":64,"value":637},"        task_id=\"approve_report\",\n",{"type":58,"tag":301,"props":639,"children":641},{"class":303,"line":640},13,[642],{"type":58,"tag":301,"props":643,"children":644},{},[645],{"type":64,"value":646},"        subject=\"Report Approval\",\n",{"type":58,"tag":301,"props":648,"children":650},{"class":303,"line":649},14,[651],{"type":58,"tag":301,"props":652,"children":653},{},[654],{"type":64,"value":655},"        body=\"{{ ti.xcom_pull(task_ids='prepare') }}\",\n",{"type":58,"tag":301,"props":657,"children":659},{"class":303,"line":658},15,[660],{"type":58,"tag":301,"props":661,"children":662},{},[663],{"type":64,"value":664},"        defaults=\"Approve\",              # 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.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":1542,"total":1700},[1543,1557,1563,1579,1593,1610,1623,1635,1650,1664,1674,1687],{"slug":14,"name":14,"fn":1544,"description":1545,"org":1546,"tags":1547,"stars":22,"repoUrl":23,"updatedAt":1556},"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},[1548,1549,1552,1553],{"name":13,"slug":14,"type":15},{"name":1550,"slug":1551,"type":15},"CLI","cli",{"name":17,"slug":18,"type":15},{"name":1554,"slug":1555,"type":15},"Debugging","debugging","2026-04-06T18:01:43.992997",{"slug":4,"name":4,"fn":5,"description":6,"org":1558,"tags":1559,"stars":22,"repoUrl":23,"updatedAt":24},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1560,1561,1562],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"slug":1564,"name":1564,"fn":1565,"description":1566,"org":1567,"tags":1568,"stars":22,"repoUrl":23,"updatedAt":1578},"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},[1569,1570,1573,1575],{"name":13,"slug":14,"type":15},{"name":1571,"slug":1572,"type":15},"Plugin Development","plugin-development",{"name":1574,"slug":539,"type":15},"Python",{"name":1576,"slug":1577,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":1580,"name":1580,"fn":1581,"description":1582,"org":1583,"tags":1584,"stars":22,"repoUrl":23,"updatedAt":1592},"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},[1585,1586,1588,1589],{"name":13,"slug":14,"type":15},{"name":1587,"slug":36,"type":15},"Data Engineering",{"name":17,"slug":18,"type":15},{"name":1590,"slug":1591,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":1594,"name":1594,"fn":1595,"description":1596,"org":1597,"tags":1598,"stars":22,"repoUrl":23,"updatedAt":1609},"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},[1599,1602,1605,1606],{"name":1600,"slug":1601,"type":15},"Analytics","analytics",{"name":1603,"slug":1604,"type":15},"Data Analysis","data-analysis",{"name":1587,"slug":36,"type":15},{"name":1607,"slug":1608,"type":15},"SQL","sql","2026-04-06T18:01:49.599775",{"slug":1611,"name":1611,"fn":1612,"description":1613,"org":1614,"tags":1615,"stars":22,"repoUrl":23,"updatedAt":1622},"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},[1616,1617,1618,1619],{"name":13,"slug":14,"type":15},{"name":1587,"slug":36,"type":15},{"name":17,"slug":18,"type":15},{"name":1620,"slug":1621,"type":15},"Observability","observability","2026-04-06T18:02:03.487365",{"slug":1522,"name":1522,"fn":1624,"description":1625,"org":1626,"tags":1627,"stars":22,"repoUrl":23,"updatedAt":1634},"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},[1628,1629,1630,1633],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":1631,"slug":1632,"type":15},"ETL","etl",{"name":1574,"slug":539,"type":15},"2026-04-06T18:01:52.679888",{"slug":1636,"name":1636,"fn":1637,"description":1638,"org":1639,"tags":1640,"stars":22,"repoUrl":23,"updatedAt":1649},"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},[1641,1642,1645,1646],{"name":13,"slug":14,"type":15},{"name":1643,"slug":1644,"type":15},"API Development","api-development",{"name":17,"slug":18,"type":15},{"name":1647,"slug":1648,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":1651,"name":1651,"fn":1652,"description":1653,"org":1654,"tags":1655,"stars":22,"repoUrl":23,"updatedAt":1663},"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},[1656,1657,1660],{"name":17,"slug":18,"type":15},{"name":1658,"slug":1659,"type":15},"Engineering","engineering",{"name":1661,"slug":1662,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":1665,"name":1665,"fn":1666,"description":1667,"org":1668,"tags":1669,"stars":22,"repoUrl":23,"updatedAt":1673},"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},[1670,1671,1672],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":1658,"slug":1659,"type":15},"2026-07-18T05:11:54.496539",{"slug":1675,"name":1675,"fn":1676,"description":1677,"org":1678,"tags":1679,"stars":22,"repoUrl":23,"updatedAt":1686},"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},[1680,1681,1682,1683],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":1631,"slug":1632,"type":15},{"name":1684,"slug":1685,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":1688,"name":1688,"fn":1689,"description":1690,"org":1691,"tags":1692,"stars":22,"repoUrl":23,"updatedAt":1699},"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},[1693,1694,1695,1698],{"name":13,"slug":14,"type":15},{"name":1587,"slug":36,"type":15},{"name":1696,"slug":1697,"type":15},"Data Quality","data-quality",{"name":1631,"slug":1632,"type":15},"2026-04-06T18:02:02.138565",34,{"items":1702,"total":1700},[1703,1710,1716,1723,1730,1737,1744],{"slug":14,"name":14,"fn":1544,"description":1545,"org":1704,"tags":1705,"stars":22,"repoUrl":23,"updatedAt":1556},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1706,1707,1708,1709],{"name":13,"slug":14,"type":15},{"name":1550,"slug":1551,"type":15},{"name":17,"slug":18,"type":15},{"name":1554,"slug":1555,"type":15},{"slug":4,"name":4,"fn":5,"description":6,"org":1711,"tags":1712,"stars":22,"repoUrl":23,"updatedAt":24},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1713,1714,1715],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"slug":1564,"name":1564,"fn":1565,"description":1566,"org":1717,"tags":1718,"stars":22,"repoUrl":23,"updatedAt":1578},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1719,1720,1721,1722],{"name":13,"slug":14,"type":15},{"name":1571,"slug":1572,"type":15},{"name":1574,"slug":539,"type":15},{"name":1576,"slug":1577,"type":15},{"slug":1580,"name":1580,"fn":1581,"description":1582,"org":1724,"tags":1725,"stars":22,"repoUrl":23,"updatedAt":1592},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1726,1727,1728,1729],{"name":13,"slug":14,"type":15},{"name":1587,"slug":36,"type":15},{"name":17,"slug":18,"type":15},{"name":1590,"slug":1591,"type":15},{"slug":1594,"name":1594,"fn":1595,"description":1596,"org":1731,"tags":1732,"stars":22,"repoUrl":23,"updatedAt":1609},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1733,1734,1735,1736],{"name":1600,"slug":1601,"type":15},{"name":1603,"slug":1604,"type":15},{"name":1587,"slug":36,"type":15},{"name":1607,"slug":1608,"type":15},{"slug":1611,"name":1611,"fn":1612,"description":1613,"org":1738,"tags":1739,"stars":22,"repoUrl":23,"updatedAt":1622},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1740,1741,1742,1743],{"name":13,"slug":14,"type":15},{"name":1587,"slug":36,"type":15},{"name":17,"slug":18,"type":15},{"name":1620,"slug":1621,"type":15},{"slug":1522,"name":1522,"fn":1624,"description":1625,"org":1745,"tags":1746,"stars":22,"repoUrl":23,"updatedAt":1634},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1747,1748,1749,1750],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":1631,"slug":1632,"type":15},{"name":1574,"slug":539,"type":15}]