[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-astronomer-debugging-dags":3,"mdc--ndipgv-key":55,"related-repo-astronomer-debugging-dags":997,"related-org-astronomer-debugging-dags":1094},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":50,"sourceUrl":53,"mdContent":54},"debugging-dags","debug Airflow DAG failures","Comprehensive DAG failure diagnosis and root-cause analysis  with structured investigation and prevention recommendations. Use when deep failure investigation is needed, a DAG fails to import\u002Fparse or 'airflow dags list' errors on a file; a task or run is failing and must be diagnosed and fixed; requests like 'why did X fail', 'my dag keeps failing — find and fix it', or fixing a broken DAG so it loads cleanly. 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Use when deep failure investigation is needed, a DAG fails to import\u002Fparse or 'airflow dags list' errors on a file; a task or run is failing and must be diagnosed and fixed; requests like 'why did X fail', 'my dag keeps failing — find and fix it', or fixing a broken DAG so it loads cleanly. For simple 'why did it fail \u002F show logs', the airflow skill handles it directly.\n---\n\n# DAG Diagnosis\n\nYou are a data engineer debugging a failed Airflow DAG. Follow this systematic approach to identify the root cause and provide actionable remediation.\n\n## Running the CLI\n\nThese commands assume `af` is on PATH. Run via `astro otto` to get it automatically, or install standalone with `uv tool install astro-airflow-mcp`.\n\n---\n\n## Step 1: Identify the Failure\n\nIf a specific DAG was mentioned:\n- Run `af runs diagnose \u003Cdag_id> \u003Cdag_run_id>` (if run_id is provided)\n- If no run_id specified, run `af dags stats` to find recent failures\n\nIf no DAG was specified:\n- Run `af health` to find recent failures across all DAGs\n- Check for import errors with `af dags errors`\n- Show DAGs with recent failures\n- Ask which DAG to investigate further\n\n## Step 2: Get the Error Details\n\nOnce you have identified a failed task:\n\n1. **Get task logs** using `af tasks logs \u003Cdag_id> \u003Cdag_run_id> \u003Ctask_id>`\n2. **Look for the actual exception** - scroll past the Airflow boilerplate to find the real error\n3. **Categorize the failure type**:\n   - **Data issue**: Missing data, schema change, null values, constraint violation\n   - **Code issue**: Bug, syntax error, import failure, type error\n   - **Infrastructure issue**: Connection timeout, resource exhaustion, permission denied\n   - **Dependency issue**: Upstream failure, external API down, rate limiting\n\n## Step 3: Check Context\n\nGather additional context to understand WHY this happened:\n\n1. **Recent changes**: Was there a code deploy? Check git history if available\n2. **Package version changes**: Was a package upgraded — in the image, in a venv-style operator, or at the index? See [Package version changes](#package-version-changes) below.\n3. **Data volume**: Did data volume spike? Run a quick count on source tables\n4. **Upstream health**: Did upstream tasks succeed but produce unexpected data?\n5. **Historical pattern**: Is this a recurring failure? Check if same task failed before\n6. **Timing**: Did this fail at an unusual time? (resource contention, maintenance windows)\n\nUse `af runs get \u003Cdag_id> \u003Cdag_run_id>` to compare the failed run against recent successful runs.\n\n### Package version changes\n\nA common cause of failures with no git activity is dependency drift — the user's code didn't change, but a package they depend on did. Check in this order:\n\n1. **Worker image diff** (preferred when available). Every Astro deploy = new image tag, so the registry has a \"before\" and \"after\". Diff `pip freeze` between current and previous image — that's ground truth for what changed:\n   ```\n   docker run --rm \u003Ccurrent_image> pip freeze > \u002Ftmp\u002Fnow.txt\n   docker run --rm \u003Cprevious_image> pip freeze > \u002Ftmp\u002Fprev.txt\n   diff \u002Ftmp\u002Fprev.txt \u002Ftmp\u002Fnow.txt\n   ```\n   Also compare `docker run --rm \u003Cimage> python --version` between the two — a Python minor-version bump (3.11 → 3.12, or even a patch) can break wheel compatibility even when `pip freeze` looks identical. `af config providers` lists currently installed provider versions, useful for cross-checking against modules named in the traceback.\n\n2. **Venv-style operators bypass the worker image.** `@task.virtualenv`, `PythonVirtualenvOperator`, `ExternalPythonOperator`, and `KubernetesPodOperator` build their environment per task run, so an image diff won't catch failures inside them. If the failed task is one of these, read its `requirements` \u002F `image` \u002F `python_version` \u002F `python` args directly:\n   - Unbounded specifier (e.g. `pandas>=2.0.0` with no upper bound, or no specifier at all) → a new upstream release is the prime suspect.\n   - `image=\"foo:latest\"` or no tag → the image moved underneath you.\n   - `python_version=\"3.11\"` (on `@task.virtualenv` \u002F `PythonVirtualenvOperator`) or a `python` path (on `ExternalPythonOperator`) resolving to a different interpreter than it used to — a Python minor-version change can break wheel compatibility for unchanged `requirements`. Same vector applies to the worker image itself if the base Python changed there.\n\n   Fix is to pin: `pandas>=2.0.0,\u003C3.0.0`, a lockfile, a specific image SHA, or a fully-qualified Python version (`python_version=\"3.11.7\"` instead of `\"3.11\"`).\n\n3. **Index lookup** when image diff isn't conclusive (no image history, or a venv-style operator). Identify the configured index first — it may not be PyPI:\n   - Env vars: `UV_INDEX_URL`, `PIP_INDEX_URL`, `PIP_EXTRA_INDEX_URL`\n   - `pyproject.toml` → `[[tool.uv.index]]`\n   - `~\u002F.pip\u002Fpip.conf`, `\u002Fetc\u002Fpip.conf`\n   - `Dockerfile` `--index-url` flags\n\n   Then query for releases of the suspect package since the first failure started. PyPI:\n   ```\n   curl -s https:\u002F\u002Fpypi.org\u002Fpypi\u002F\u003Cpkg>\u002Fjson | jq '.releases | to_entries | map({version: .key, uploaded: .value[0].upload_time}) | sort_by(.uploaded) | reverse | .[:5]'\n   ```\n   Private indexes usually expose the same `\u002Fpypi\u002F\u003Cpkg>\u002Fjson` shape; fall back to the Simple API (`\u002Fsimple\u002F\u003Cpkg>\u002F`) or ask the user if neither works.\n\nA release timestamp landing between the last green run and the first red run, for a package named in the traceback, is the answer.\n\n### On Astro\n\nIf you're running on Astro, these additional tools can help with diagnosis:\n\n- **Deployment activity log**: Check the Astro UI for recent deploys — a failed deploy or recent code change is often the cause of sudden failures\n- **Astro alerts**: Configure alerts in the Astro UI for proactive failure monitoring (DAG failure, task duration, SLA miss)\n- **Observability**: Use the Astro [observability dashboard](https:\u002F\u002Fwww.astronomer.io\u002Fdocs\u002Fastro\u002Fairflow-alerts) to track DAG health trends and spot recurring issues\n\n### On OSS Airflow\n\n- **Airflow UI**: Use the DAGs page, Graph view, and task logs to inspect recent runs and failures\n\n## Step 4: Provide Actionable Output\n\nStructure your diagnosis as:\n\n### Root Cause\nWhat actually broke? Be specific - not \"the task failed\" but \"the task failed because column X was null in 15% of rows when the code expected 0%\".\n\n### Impact Assessment\n- What data is affected? Which tables didn't get updated?\n- What downstream processes are blocked?\n- Is this blocking production dashboards or reports?\n\n### Immediate Fix\nSpecific steps to resolve RIGHT NOW:\n1. If it's a data issue: SQL to fix or skip bad records\n2. If it's a code issue: The exact code change needed\n3. 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Identify the configured index first — it may not be PyPI:",{"type":61,"tag":128,"props":645,"children":646},{},[647,672,689,705],{"type":61,"tag":132,"props":648,"children":649},{},[650,652,658,659,665,666],{"type":67,"value":651},"Env vars: ",{"type":61,"tag":88,"props":653,"children":655},{"className":654},[],[656],{"type":67,"value":657},"UV_INDEX_URL",{"type":67,"value":479},{"type":61,"tag":88,"props":660,"children":662},{"className":661},[],[663],{"type":67,"value":664},"PIP_INDEX_URL",{"type":67,"value":479},{"type":61,"tag":88,"props":667,"children":669},{"className":668},[],[670],{"type":67,"value":671},"PIP_EXTRA_INDEX_URL",{"type":61,"tag":132,"props":673,"children":674},{},[675,681,683],{"type":61,"tag":88,"props":676,"children":678},{"className":677},[],[679],{"type":67,"value":680},"pyproject.toml",{"type":67,"value":682}," → ",{"type":61,"tag":88,"props":684,"children":686},{"className":685},[],[687],{"type":67,"value":688},"[[tool.uv.index]]",{"type":61,"tag":132,"props":690,"children":691},{},[692,698,699],{"type":61,"tag":88,"props":693,"children":695},{"className":694},[],[696],{"type":67,"value":697},"~\u002F.pip\u002Fpip.conf",{"type":67,"value":479},{"type":61,"tag":88,"props":700,"children":702},{"className":701},[],[703],{"type":67,"value":704},"\u002Fetc\u002Fpip.conf",{"type":61,"tag":132,"props":706,"children":707},{},[708,714,715,721],{"type":61,"tag":88,"props":709,"children":711},{"className":710},[],[712],{"type":67,"value":713},"Dockerfile",{"type":67,"value":471},{"type":61,"tag":88,"props":716,"children":718},{"className":717},[],[719],{"type":67,"value":720},"--index-url",{"type":67,"value":722}," flags",{"type":61,"tag":434,"props":724,"children":725},{},[],{"type":67,"value":727},"Then query for releases of the suspect package since the first failure started. PyPI:",{"type":61,"tag":422,"props":729,"children":732},{"className":730,"code":731,"language":67},[425],"curl -s https:\u002F\u002Fpypi.org\u002Fpypi\u002F\u003Cpkg>\u002Fjson | jq '.releases | to_entries | map({version: .key, uploaded: .value[0].upload_time}) | sort_by(.uploaded) | reverse | .[:5]'\n",[733],{"type":61,"tag":88,"props":734,"children":735},{"__ignoreMap":430},[736],{"type":67,"value":731},{"type":61,"tag":434,"props":738,"children":739},{},[],{"type":67,"value":741},"Private indexes usually expose the same ",{"type":61,"tag":88,"props":743,"children":745},{"className":744},[],[746],{"type":67,"value":747},"\u002Fpypi\u002F\u003Cpkg>\u002Fjson",{"type":67,"value":749}," shape; fall back to the Simple API (",{"type":61,"tag":88,"props":751,"children":753},{"className":752},[],[754],{"type":67,"value":755},"\u002Fsimple\u002F\u003Cpkg>\u002F",{"type":67,"value":757},") or ask the user if neither works.",{"type":61,"tag":70,"props":759,"children":760},{},[761],{"type":67,"value":762},"A release timestamp landing between the last green run and the first red run, for a package named in the traceback, is the answer.",{"type":61,"tag":390,"props":764,"children":766},{"id":765},"on-astro",[767],{"type":67,"value":768},"On Astro",{"type":61,"tag":70,"props":770,"children":771},{},[772],{"type":67,"value":773},"If you're running on Astro, these additional tools can help with diagnosis:",{"type":61,"tag":128,"props":775,"children":776},{},[777,787,797],{"type":61,"tag":132,"props":778,"children":779},{},[780,785],{"type":61,"tag":218,"props":781,"children":782},{},[783],{"type":67,"value":784},"Deployment activity log",{"type":67,"value":786},": Check the Astro UI for recent deploys — a failed deploy or recent code change is often the cause of sudden failures",{"type":61,"tag":132,"props":788,"children":789},{},[790,795],{"type":61,"tag":218,"props":791,"children":792},{},[793],{"type":67,"value":794},"Astro alerts",{"type":67,"value":796},": Configure alerts in the Astro UI for proactive failure monitoring (DAG failure, task duration, SLA miss)",{"type":61,"tag":132,"props":798,"children":799},{},[800,805,807,815],{"type":61,"tag":218,"props":801,"children":802},{},[803],{"type":67,"value":804},"Observability",{"type":67,"value":806},": Use the Astro ",{"type":61,"tag":329,"props":808,"children":812},{"href":809,"rel":810},"https:\u002F\u002Fwww.astronomer.io\u002Fdocs\u002Fastro\u002Fairflow-alerts",[811],"nofollow",[813],{"type":67,"value":814},"observability dashboard",{"type":67,"value":816}," to track DAG health trends and spot recurring issues",{"type":61,"tag":390,"props":818,"children":820},{"id":819},"on-oss-airflow",[821],{"type":67,"value":822},"On OSS Airflow",{"type":61,"tag":128,"props":824,"children":825},{},[826],{"type":61,"tag":132,"props":827,"children":828},{},[829,834],{"type":61,"tag":218,"props":830,"children":831},{},[832],{"type":67,"value":833},"Airflow UI",{"type":67,"value":835},": Use the DAGs page, Graph view, and task logs to inspect recent runs and failures",{"type":61,"tag":76,"props":837,"children":839},{"id":838},"step-4-provide-actionable-output",[840],{"type":67,"value":841},"Step 4: Provide Actionable Output",{"type":61,"tag":70,"props":843,"children":844},{},[845],{"type":67,"value":846},"Structure your diagnosis as:",{"type":61,"tag":390,"props":848,"children":850},{"id":849},"root-cause",[851],{"type":67,"value":852},"Root Cause",{"type":61,"tag":70,"props":854,"children":855},{},[856],{"type":67,"value":857},"What actually broke? Be specific - not \"the task failed\" but \"the task failed because column X was null in 15% of rows when the code expected 0%\".",{"type":61,"tag":390,"props":859,"children":861},{"id":860},"impact-assessment",[862],{"type":67,"value":863},"Impact Assessment",{"type":61,"tag":128,"props":865,"children":866},{},[867,872,877],{"type":61,"tag":132,"props":868,"children":869},{},[870],{"type":67,"value":871},"What data is affected? Which tables didn't get updated?",{"type":61,"tag":132,"props":873,"children":874},{},[875],{"type":67,"value":876},"What downstream processes are blocked?",{"type":61,"tag":132,"props":878,"children":879},{},[880],{"type":67,"value":881},"Is this blocking production dashboards or reports?",{"type":61,"tag":390,"props":883,"children":885},{"id":884},"immediate-fix",[886],{"type":67,"value":887},"Immediate Fix",{"type":61,"tag":70,"props":889,"children":890},{},[891],{"type":67,"value":892},"Specific steps to resolve RIGHT NOW:",{"type":61,"tag":211,"props":894,"children":895},{},[896,901,906],{"type":61,"tag":132,"props":897,"children":898},{},[899],{"type":67,"value":900},"If it's a data issue: SQL to fix or skip bad records",{"type":61,"tag":132,"props":902,"children":903},{},[904],{"type":67,"value":905},"If it's a code issue: The exact code change needed",{"type":61,"tag":132,"props":907,"children":908},{},[909],{"type":67,"value":910},"If it's infra: Who to contact or what to restart",{"type":61,"tag":390,"props":912,"children":914},{"id":913},"prevention",[915],{"type":67,"value":916},"Prevention",{"type":61,"tag":70,"props":918,"children":919},{},[920],{"type":67,"value":921},"How to prevent this from happening again:",{"type":61,"tag":128,"props":923,"children":924},{},[925,930,935,940,945],{"type":61,"tag":132,"props":926,"children":927},{},[928],{"type":67,"value":929},"Add data quality checks?",{"type":61,"tag":132,"props":931,"children":932},{},[933],{"type":67,"value":934},"Add better error handling?",{"type":61,"tag":132,"props":936,"children":937},{},[938],{"type":67,"value":939},"Add alerting for edge cases?",{"type":61,"tag":132,"props":941,"children":942},{},[943],{"type":67,"value":944},"Update documentation?",{"type":61,"tag":132,"props":946,"children":947},{},[948],{"type":67,"value":949},"Pin dependencies (constraints file, lockfile, or upper-bound specifiers on venv\u002Fexternal\u002Fpod operators) to avoid silent upstream drift?",{"type":61,"tag":390,"props":951,"children":953},{"id":952},"quick-commands",[954],{"type":67,"value":955},"Quick Commands",{"type":61,"tag":70,"props":957,"children":958},{},[959],{"type":67,"value":960},"Provide ready-to-use commands:",{"type":61,"tag":128,"props":962,"children":963},{},[964,975,986],{"type":61,"tag":132,"props":965,"children":966},{},[967,969],{"type":67,"value":968},"To clear and rerun the entire DAG run: ",{"type":61,"tag":88,"props":970,"children":972},{"className":971},[],[973],{"type":67,"value":974},"af runs clear \u003Cdag_id> \u003Crun_id>",{"type":61,"tag":132,"props":976,"children":977},{},[978,980],{"type":67,"value":979},"To clear and rerun specific failed tasks: ",{"type":61,"tag":88,"props":981,"children":983},{"className":982},[],[984],{"type":67,"value":985},"af tasks clear \u003Cdag_id> \u003Crun_id> \u003Ctask_ids> -D",{"type":61,"tag":132,"props":987,"children":988},{},[989,991],{"type":67,"value":990},"To delete a stuck or unwanted run: ",{"type":61,"tag":88,"props":992,"children":994},{"className":993},[],[995],{"type":67,"value":996},"af runs delete \u003Cdag_id> \u003Crun_id>",{"items":998,"total":1093},[999,1011,1023,1039,1053,1070,1082],{"slug":14,"name":14,"fn":1000,"description":1001,"org":1002,"tags":1003,"stars":25,"repoUrl":26,"updatedAt":1010},"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},[1004,1005,1008,1009],{"name":13,"slug":14,"type":15},{"name":1006,"slug":1007,"type":15},"CLI","cli",{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},"2026-04-06T18:01:43.992997",{"slug":1012,"name":1012,"fn":1013,"description":1014,"org":1015,"tags":1016,"stars":25,"repoUrl":26,"updatedAt":1022},"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},[1017,1018,1021],{"name":13,"slug":14,"type":15},{"name":1019,"slug":1020,"type":15},"Approvals","approvals",{"name":20,"slug":21,"type":15},"2026-04-06T18:01:46.758548",{"slug":1024,"name":1024,"fn":1025,"description":1026,"org":1027,"tags":1028,"stars":25,"repoUrl":26,"updatedAt":1038},"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},[1029,1030,1033,1035],{"name":13,"slug":14,"type":15},{"name":1031,"slug":1032,"type":15},"Plugin Development","plugin-development",{"name":1034,"slug":530,"type":15},"Python",{"name":1036,"slug":1037,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":1040,"name":1040,"fn":1041,"description":1042,"org":1043,"tags":1044,"stars":25,"repoUrl":26,"updatedAt":1052},"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},[1045,1046,1048,1049],{"name":13,"slug":14,"type":15},{"name":1047,"slug":39,"type":15},"Data Engineering",{"name":20,"slug":21,"type":15},{"name":1050,"slug":1051,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":1054,"name":1054,"fn":1055,"description":1056,"org":1057,"tags":1058,"stars":25,"repoUrl":26,"updatedAt":1069},"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},[1059,1062,1065,1066],{"name":1060,"slug":1061,"type":15},"Analytics","analytics",{"name":1063,"slug":1064,"type":15},"Data Analysis","data-analysis",{"name":1047,"slug":39,"type":15},{"name":1067,"slug":1068,"type":15},"SQL","sql","2026-04-06T18:01:49.599775",{"slug":1071,"name":1071,"fn":1072,"description":1073,"org":1074,"tags":1075,"stars":25,"repoUrl":26,"updatedAt":1081},"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},[1076,1077,1078,1079],{"name":13,"slug":14,"type":15},{"name":1047,"slug":39,"type":15},{"name":20,"slug":21,"type":15},{"name":804,"slug":1080,"type":15},"observability","2026-04-06T18:02:03.487365",{"slug":1083,"name":1083,"fn":1084,"description":1085,"org":1086,"tags":1087,"stars":25,"repoUrl":26,"updatedAt":1092},"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},[1088,1089,1090,1091],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":1034,"slug":530,"type":15},"2026-04-06T18:01:52.679888",34,{"items":1095,"total":1093},[1096,1103,1109,1116,1123,1130,1137,1144,1159,1173,1183,1196],{"slug":14,"name":14,"fn":1000,"description":1001,"org":1097,"tags":1098,"stars":25,"repoUrl":26,"updatedAt":1010},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1099,1100,1101,1102],{"name":13,"slug":14,"type":15},{"name":1006,"slug":1007,"type":15},{"name":20,"slug":21,"type":15},{"name":23,"slug":24,"type":15},{"slug":1012,"name":1012,"fn":1013,"description":1014,"org":1104,"tags":1105,"stars":25,"repoUrl":26,"updatedAt":1022},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1106,1107,1108],{"name":13,"slug":14,"type":15},{"name":1019,"slug":1020,"type":15},{"name":20,"slug":21,"type":15},{"slug":1024,"name":1024,"fn":1025,"description":1026,"org":1110,"tags":1111,"stars":25,"repoUrl":26,"updatedAt":1038},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1112,1113,1114,1115],{"name":13,"slug":14,"type":15},{"name":1031,"slug":1032,"type":15},{"name":1034,"slug":530,"type":15},{"name":1036,"slug":1037,"type":15},{"slug":1040,"name":1040,"fn":1041,"description":1042,"org":1117,"tags":1118,"stars":25,"repoUrl":26,"updatedAt":1052},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1119,1120,1121,1122],{"name":13,"slug":14,"type":15},{"name":1047,"slug":39,"type":15},{"name":20,"slug":21,"type":15},{"name":1050,"slug":1051,"type":15},{"slug":1054,"name":1054,"fn":1055,"description":1056,"org":1124,"tags":1125,"stars":25,"repoUrl":26,"updatedAt":1069},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1126,1127,1128,1129],{"name":1060,"slug":1061,"type":15},{"name":1063,"slug":1064,"type":15},{"name":1047,"slug":39,"type":15},{"name":1067,"slug":1068,"type":15},{"slug":1071,"name":1071,"fn":1072,"description":1073,"org":1131,"tags":1132,"stars":25,"repoUrl":26,"updatedAt":1081},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1133,1134,1135,1136],{"name":13,"slug":14,"type":15},{"name":1047,"slug":39,"type":15},{"name":20,"slug":21,"type":15},{"name":804,"slug":1080,"type":15},{"slug":1083,"name":1083,"fn":1084,"description":1085,"org":1138,"tags":1139,"stars":25,"repoUrl":26,"updatedAt":1092},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1140,1141,1142,1143],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":1034,"slug":530,"type":15},{"slug":1145,"name":1145,"fn":1146,"description":1147,"org":1148,"tags":1149,"stars":25,"repoUrl":26,"updatedAt":1158},"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},[1150,1151,1154,1155],{"name":13,"slug":14,"type":15},{"name":1152,"slug":1153,"type":15},"API Development","api-development",{"name":20,"slug":21,"type":15},{"name":1156,"slug":1157,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":1160,"name":1160,"fn":1161,"description":1162,"org":1163,"tags":1164,"stars":25,"repoUrl":26,"updatedAt":1172},"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},[1165,1166,1169],{"name":20,"slug":21,"type":15},{"name":1167,"slug":1168,"type":15},"Engineering","engineering",{"name":1170,"slug":1171,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":1174,"name":1174,"fn":1175,"description":1176,"org":1177,"tags":1178,"stars":25,"repoUrl":26,"updatedAt":1182},"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},[1179,1180,1181],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":1167,"slug":1168,"type":15},"2026-07-18T05:11:54.496539",{"slug":1184,"name":1184,"fn":1185,"description":1186,"org":1187,"tags":1188,"stars":25,"repoUrl":26,"updatedAt":1195},"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},[1189,1190,1191,1192],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":1193,"slug":1194,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":1197,"name":1197,"fn":1198,"description":1199,"org":1200,"tags":1201,"stars":25,"repoUrl":26,"updatedAt":1208},"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},[1202,1203,1204,1207],{"name":13,"slug":14,"type":15},{"name":1047,"slug":39,"type":15},{"name":1205,"slug":1206,"type":15},"Data Quality","data-quality",{"name":17,"slug":18,"type":15},"2026-04-06T18:02:02.138565"]