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Use when investigating deployment issues, viewing production logs, analyzing failures, or managing deployment environment variables.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"astronomer","Astronomer","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fastronomer.png",[12,16,19,22,25],{"name":13,"slug":14,"type":15},"Observability","observability","tag",{"name":17,"slug":18,"type":15},"Airflow","airflow",{"name":20,"slug":21,"type":15},"CLI","cli",{"name":23,"slug":24,"type":15},"Deployment","deployment",{"name":26,"slug":27,"type":15},"Debugging","debugging",412,"https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fagents","2026-07-01T08:11:52.396623",null,55,[34,35,36,37,18,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"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":29,"stars":28,"forks":32,"topics":54,"description":55},[34,35,36,37,18,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52],"AI agent tooling for data engineering workflows.","https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fagents\u002Ftree\u002FHEAD\u002Fskills\u002Ftroubleshooting-astro-deployments","---\nname: troubleshooting-astro-deployments\ndescription: Troubleshoot Astronomer production deployments with Astro CLI. Use when investigating deployment issues, viewing production logs, analyzing failures, or managing deployment environment variables.\n---\n\n# Astro Deployment Troubleshooting\n\nThis skill helps you diagnose and troubleshoot production Astronomer deployments using the Astro CLI.\n\n> **For deployment management**, see the **managing-astro-deployments** skill.\n> **For local development**, see the **managing-astro-local-env** skill.\n\n---\n\n## Quick Health Check\n\nStart with these commands to get an overview:\n\n```bash\n# 1. List deployments to find target\nastro deployment list\n\n# 2. Get deployment overview\nastro deployment inspect \u003CDEPLOYMENT_ID>\n\n# 3. Check for errors\nastro deployment logs \u003CDEPLOYMENT_ID> --error -c 50\n```\n\n---\n\n## Viewing Deployment Logs\n\nUse `-c` to control log count (default: 500). Log flags cannot be combined — use one component or level flag per command.\n\n### Component-Specific Logs\n\nView logs from specific Airflow components:\n\n```bash\n# Scheduler logs (DAG processing, task scheduling)\nastro deployment logs \u003CDEPLOYMENT_ID> --scheduler -c 50\n\n# Worker logs (task execution)\nastro deployment logs \u003CDEPLOYMENT_ID> --workers -c 30\n\n# Webserver logs (UI access, health checks)\nastro deployment logs \u003CDEPLOYMENT_ID> --webserver -c 30\n\n# Triggerer logs (deferrable operators)\nastro deployment logs \u003CDEPLOYMENT_ID> --triggerer -c 30\n```\n\n### Log Level Filtering\n\nFilter by severity:\n\n```bash\n# Error logs only (most useful for troubleshooting)\nastro deployment logs \u003CDEPLOYMENT_ID> --error -c 30\n\n# Warning logs\nastro deployment logs \u003CDEPLOYMENT_ID> --warn -c 50\n\n# Info-level logs\nastro deployment logs \u003CDEPLOYMENT_ID> --info -c 50\n```\n\n### Search Logs\n\nSearch for specific keywords:\n\n```bash\n# Search for specific error\nastro deployment logs \u003CDEPLOYMENT_ID> --keyword \"ConnectionError\"\n\n# Search for specific DAG\nastro deployment logs \u003CDEPLOYMENT_ID> --keyword \"my_dag_name\" -c 100\n\n# Find import errors\nastro deployment logs \u003CDEPLOYMENT_ID> --error --keyword \"ImportError\"\n\n# Find task failures\nastro deployment logs \u003CDEPLOYMENT_ID> --error --keyword \"Task failed\"\n```\n\n---\n\n## Complete Investigation Workflow\n\n### Step 1: Identify the Problem\n\n```bash\n# List deployments with status\nastro deployment list\n\n# Get deployment details\nastro deployment inspect \u003CDEPLOYMENT_ID>\n```\n\nLook for:\n- Status: HEALTHY vs UNHEALTHY\n- Runtime version compatibility\n- Resource limits (CPU, memory)\n- Recent deployment timestamp\n\n### Step 2: Check Error Logs\n\n```bash\n# Start with errors\nastro deployment logs \u003CDEPLOYMENT_ID> --error -c 50\n```\n\nLook for:\n- Recurring error patterns\n- Specific DAGs failing repeatedly\n- Import errors or syntax errors\n- Connection or credential errors\n\n### Step 3: Review Scheduler Logs\n\n```bash\n# Check DAG processing\nastro deployment logs \u003CDEPLOYMENT_ID> --scheduler -c 30\n```\n\nLook for:\n- DAG parse errors\n- Scheduling delays\n- Task queueing issues\n\n### Step 4: Check Worker Logs\n\n```bash\n# Check task execution\nastro deployment logs \u003CDEPLOYMENT_ID> --workers -c 30\n```\n\nLook for:\n- Task execution failures\n- Resource exhaustion\n- Timeout errors\n\n### Step 5: Verify Configuration\n\n```bash\n# Check environment variables\nastro deployment variable list --deployment-id \u003CDEPLOYMENT_ID>\n\n# Verify deployment settings\nastro deployment inspect \u003CDEPLOYMENT_ID>\n```\n\nLook for:\n- Missing or incorrect environment variables\n- Secrets configuration (AIRFLOW__SECRETS__BACKEND)\n- Connection configuration\n\n---\n\n## Common Investigation Patterns\n\n### Recurring DAG Failures\n\nFollow the complete investigation workflow above, then narrow to the specific DAG:\n\n```bash\nastro deployment logs \u003CDEPLOYMENT_ID> --keyword \"my_dag_name\" -c 100\n```\n\n### Resource Issues\n\n```bash\n# 1. Check deployment resource allocation\nastro deployment inspect \u003CDEPLOYMENT_ID>\n# Look for: resource_quota_cpu, resource_quota_memory\n# Worker queue: max_worker_count, worker_type\n\n# 2. Check for worker scaling issues\nastro deployment logs \u003CDEPLOYMENT_ID> --workers -c 50\n\n# 3. Look for out-of-memory errors\nastro deployment logs \u003CDEPLOYMENT_ID> --error --keyword \"memory\"\n```\n\n### Configuration Problems\n\n```bash\n# 1. Review environment variables\nastro deployment variable list --deployment-id \u003CDEPLOYMENT_ID>\n\n# 2. Check for secrets backend configuration\n# Look for: AIRFLOW__SECRETS__BACKEND, AIRFLOW__SECRETS__BACKEND_KWARGS\n\n# 3. Verify deployment settings\nastro deployment inspect \u003CDEPLOYMENT_ID>\n\n# 4. Check webserver logs for auth issues\nastro deployment logs \u003CDEPLOYMENT_ID> --webserver -c 30\n```\n\n### Import Errors\n\n```bash\n# 1. Find import errors\nastro deployment logs \u003CDEPLOYMENT_ID> --error --keyword \"ImportError\"\n\n# 2. Check scheduler for parse failures\nastro deployment logs \u003CDEPLOYMENT_ID> --scheduler --keyword \"Failed to import\" -c 50\n\n# 3. Verify dependencies were deployed\nastro deployment inspect \u003CDEPLOYMENT_ID>\n# Check: current_tag, last deployment timestamp\n```\n\n---\n\n## Environment Variables Management\n\n### List Variables\n\n```bash\n# List all variables for deployment\nastro deployment variable list --deployment-id \u003CDEPLOYMENT_ID>\n\n# Find specific variable\nastro deployment variable list --deployment-id \u003CDEPLOYMENT_ID> --key AWS_REGION\n\n# Export variables to file\nastro deployment variable list --deployment-id \u003CDEPLOYMENT_ID> --save --env .env.backup\n```\n\n### Create Variables\n\n```bash\n# Create regular variable\nastro deployment variable create --deployment-id \u003CDEPLOYMENT_ID> \\\n  --key API_ENDPOINT \\\n  --value https:\u002F\u002Fapi.example.com\n\n# Create secret (masked in UI and logs)\nastro deployment variable create --deployment-id \u003CDEPLOYMENT_ID> \\\n  --key API_KEY \\\n  --value secret123 \\\n  --secret\n```\n\n### Update Variables\n\n```bash\n# Update existing variable\nastro deployment variable update --deployment-id \u003CDEPLOYMENT_ID> \\\n  --key API_KEY \\\n  --value newsecret\n```\n\n### Delete Variables\n\n```bash\n# Delete variable\nastro deployment variable delete --deployment-id \u003CDEPLOYMENT_ID> --key OLD_KEY\n```\n\n**Note**: Variables are available to DAGs as environment variables. Changes require no redeployment.\n\n---\n\n## Key Metrics from `deployment inspect`\n\nFocus on these fields when troubleshooting:\n\n- **status**: HEALTHY vs UNHEALTHY\n- **runtime_version**: Airflow version compatibility\n- **scheduler_size\u002Fscheduler_count**: Scheduler capacity\n- **executor**: CELERY, KUBERNETES, or LOCAL\n- **worker_queues**: Worker scaling limits and types\n  - `min_worker_count`, `max_worker_count`\n  - `worker_concurrency`\n  - `worker_type` (resource class)\n- **resource_quota_cpu\u002Fmemory**: Overall resource limits\n- **dag_deploy_enabled**: Whether DAG-only deploys work\n- **current_tag**: Last deployment version\n- **is_high_availability**: Redundancy enabled\n\n---\n\n## Investigation Best Practices\n\n1. **Always start with error logs** - Most obvious failures appear here\n2. **Check error logs for patterns** - Same DAG failing repeatedly? Timing patterns?\n3. **Component-specific troubleshooting**:\n   - Worker logs → task execution details\n   - Scheduler logs → DAG processing and scheduling\n   - Webserver logs → UI issues and health checks\n   - Triggerer logs → deferrable operator issues\n4. **Use `--keyword` for targeted searches** - More efficient than reading all logs\n5. **The `inspect` command is your health dashboard** - Check it first\n6. **Environment variables in `inspect` output** - May reveal configuration issues\n7. **Log count default is 500** - Adjust with `-c` based on needs\n8. **Don't forget to check deployment time** - Recent deploy might have introduced issue\n\n---\n\n## Troubleshooting Quick Reference\n\n| Symptom | Command |\n|---------|---------|\n| Deployment shows UNHEALTHY | `astro deployment inspect \u003CID>` + `--error` logs |\n| DAG not appearing | `--error` logs for import errors, check `--scheduler` logs |\n| Tasks failing | `--workers` logs + search for DAG with `--keyword` |\n| Slow scheduling | `--scheduler` logs + check `inspect` for scheduler resources |\n| UI not responding | `--webserver` logs |\n| Connection issues | Check variables, search logs for connection name |\n| Import errors | `--error --keyword \"ImportError\"` + `--scheduler` logs |\n| Out of memory | `inspect` for resources + `--workers --keyword \"memory\"` |\n\n---\n\n## Related Skills\n\n- **managing-astro-deployments**: Create, update, delete deployments, deploy code\n- **managing-astro-local-env**: Manage local Airflow development environment\n- **setting-up-astro-project**: Initialize and configure Astro projects\n",{"data":59,"body":60},{"name":4,"description":6},{"type":61,"children":62},"root",[63,72,78,113,117,124,129,299,302,308,321,328,333,573,579,584,760,766,771,1044,1047,1053,1059,1135,1140,1165,1171,1229,1233,1256,1262,1320,1324,1342,1348,1406,1410,1428,1434,1537,1541,1559,1562,1568,1574,1579,1641,1647,1834,1840,2021,2027,2222,2225,2231,2237,2424,2430,2618,2624,2710,2716,2780,2790,2793,2805,2810,2943,2946,2952,3088,3091,3097,3304,3307,3313,3344],{"type":64,"tag":65,"props":66,"children":68},"element","h1",{"id":67},"astro-deployment-troubleshooting",[69],{"type":70,"value":71},"text","Astro Deployment Troubleshooting",{"type":64,"tag":73,"props":74,"children":75},"p",{},[76],{"type":70,"value":77},"This skill helps you diagnose and troubleshoot production Astronomer deployments using the Astro CLI.",{"type":64,"tag":79,"props":80,"children":81},"blockquote",{},[82],{"type":64,"tag":73,"props":83,"children":84},{},[85,91,93,98,100,105,106,111],{"type":64,"tag":86,"props":87,"children":88},"strong",{},[89],{"type":70,"value":90},"For deployment management",{"type":70,"value":92},", see the ",{"type":64,"tag":86,"props":94,"children":95},{},[96],{"type":70,"value":97},"managing-astro-deployments",{"type":70,"value":99}," skill.\n",{"type":64,"tag":86,"props":101,"children":102},{},[103],{"type":70,"value":104},"For local development",{"type":70,"value":92},{"type":64,"tag":86,"props":107,"children":108},{},[109],{"type":70,"value":110},"managing-astro-local-env",{"type":70,"value":112}," skill.",{"type":64,"tag":114,"props":115,"children":116},"hr",{},[],{"type":64,"tag":118,"props":119,"children":121},"h2",{"id":120},"quick-health-check",[122],{"type":70,"value":123},"Quick Health Check",{"type":64,"tag":73,"props":125,"children":126},{},[127],{"type":70,"value":128},"Start with these commands to get an overview:",{"type":64,"tag":130,"props":131,"children":136},"pre",{"className":132,"code":133,"language":134,"meta":135,"style":135},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","# 1. 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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},[3357,3358,3359,3362],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":3360,"slug":3361,"type":15},"Data Pipeline","data-pipeline",{"name":26,"slug":27,"type":15},"2026-04-06T18:01:43.992997",{"slug":3365,"name":3365,"fn":3366,"description":3367,"org":3368,"tags":3369,"stars":28,"repoUrl":29,"updatedAt":3375},"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},[3370,3371,3374],{"name":17,"slug":18,"type":15},{"name":3372,"slug":3373,"type":15},"Approvals","approvals",{"name":3360,"slug":3361,"type":15},"2026-04-06T18:01:46.758548",{"slug":3377,"name":3377,"fn":3378,"description":3379,"org":3380,"tags":3381,"stars":28,"repoUrl":29,"updatedAt":3392},"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},[3382,3383,3386,3389],{"name":17,"slug":18,"type":15},{"name":3384,"slug":3385,"type":15},"Plugin Development","plugin-development",{"name":3387,"slug":3388,"type":15},"Python","python",{"name":3390,"slug":3391,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":3394,"name":3394,"fn":3395,"description":3396,"org":3397,"tags":3398,"stars":28,"repoUrl":29,"updatedAt":3406},"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},[3399,3400,3402,3403],{"name":17,"slug":18,"type":15},{"name":3401,"slug":42,"type":15},"Data Engineering",{"name":3360,"slug":3361,"type":15},{"name":3404,"slug":3405,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":3408,"name":3408,"fn":3409,"description":3410,"org":3411,"tags":3412,"stars":28,"repoUrl":29,"updatedAt":3423},"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},[3413,3416,3419,3420],{"name":3414,"slug":3415,"type":15},"Analytics","analytics",{"name":3417,"slug":3418,"type":15},"Data Analysis","data-analysis",{"name":3401,"slug":42,"type":15},{"name":3421,"slug":3422,"type":15},"SQL","sql","2026-04-06T18:01:49.599775",{"slug":3425,"name":3425,"fn":3426,"description":3427,"org":3428,"tags":3429,"stars":28,"repoUrl":29,"updatedAt":3434},"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},[3430,3431,3432,3433],{"name":17,"slug":18,"type":15},{"name":3401,"slug":42,"type":15},{"name":3360,"slug":3361,"type":15},{"name":13,"slug":14,"type":15},"2026-04-06T18:02:03.487365",{"slug":3436,"name":3436,"fn":3437,"description":3438,"org":3439,"tags":3440,"stars":28,"repoUrl":29,"updatedAt":3447},"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},[3441,3442,3443,3446],{"name":17,"slug":18,"type":15},{"name":3360,"slug":3361,"type":15},{"name":3444,"slug":3445,"type":15},"ETL","etl",{"name":3387,"slug":3388,"type":15},"2026-04-06T18:01:52.679888",34,{"items":3450,"total":3448},[3451,3458,3464,3471,3478,3485,3492,3499,3514,3528,3538,3551],{"slug":18,"name":18,"fn":3353,"description":3354,"org":3452,"tags":3453,"stars":28,"repoUrl":29,"updatedAt":3363},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3454,3455,3456,3457],{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":3360,"slug":3361,"type":15},{"name":26,"slug":27,"type":15},{"slug":3365,"name":3365,"fn":3366,"description":3367,"org":3459,"tags":3460,"stars":28,"repoUrl":29,"updatedAt":3375},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3461,3462,3463],{"name":17,"slug":18,"type":15},{"name":3372,"slug":3373,"type":15},{"name":3360,"slug":3361,"type":15},{"slug":3377,"name":3377,"fn":3378,"description":3379,"org":3465,"tags":3466,"stars":28,"repoUrl":29,"updatedAt":3392},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3467,3468,3469,3470],{"name":17,"slug":18,"type":15},{"name":3384,"slug":3385,"type":15},{"name":3387,"slug":3388,"type":15},{"name":3390,"slug":3391,"type":15},{"slug":3394,"name":3394,"fn":3395,"description":3396,"org":3472,"tags":3473,"stars":28,"repoUrl":29,"updatedAt":3406},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3474,3475,3476,3477],{"name":17,"slug":18,"type":15},{"name":3401,"slug":42,"type":15},{"name":3360,"slug":3361,"type":15},{"name":3404,"slug":3405,"type":15},{"slug":3408,"name":3408,"fn":3409,"description":3410,"org":3479,"tags":3480,"stars":28,"repoUrl":29,"updatedAt":3423},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3481,3482,3483,3484],{"name":3414,"slug":3415,"type":15},{"name":3417,"slug":3418,"type":15},{"name":3401,"slug":42,"type":15},{"name":3421,"slug":3422,"type":15},{"slug":3425,"name":3425,"fn":3426,"description":3427,"org":3486,"tags":3487,"stars":28,"repoUrl":29,"updatedAt":3434},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3488,3489,3490,3491],{"name":17,"slug":18,"type":15},{"name":3401,"slug":42,"type":15},{"name":3360,"slug":3361,"type":15},{"name":13,"slug":14,"type":15},{"slug":3436,"name":3436,"fn":3437,"description":3438,"org":3493,"tags":3494,"stars":28,"repoUrl":29,"updatedAt":3447},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[3495,3496,3497,3498],{"name":17,"slug":18,"type":15},{"name":3360,"slug":3361,"type":15},{"name":3444,"slug":3445,"type":15},{"name":3387,"slug":3388,"type":15},{"slug":3500,"name":3500,"fn":3501,"description":3502,"org":3503,"tags":3504,"stars":28,"repoUrl":29,"updatedAt":3513},"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},[3505,3506,3509,3510],{"name":17,"slug":18,"type":15},{"name":3507,"slug":3508,"type":15},"API Development","api-development",{"name":3360,"slug":3361,"type":15},{"name":3511,"slug":3512,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":3515,"name":3515,"fn":3516,"description":3517,"org":3518,"tags":3519,"stars":28,"repoUrl":29,"updatedAt":3527},"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},[3520,3521,3524],{"name":3360,"slug":3361,"type":15},{"name":3522,"slug":3523,"type":15},"Engineering","engineering",{"name":3525,"slug":3526,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":3529,"name":3529,"fn":3530,"description":3531,"org":3532,"tags":3533,"stars":28,"repoUrl":29,"updatedAt":3537},"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},[3534,3535,3536],{"name":17,"slug":18,"type":15},{"name":3360,"slug":3361,"type":15},{"name":3522,"slug":3523,"type":15},"2026-07-18T05:11:54.496539",{"slug":3539,"name":3539,"fn":3540,"description":3541,"org":3542,"tags":3543,"stars":28,"repoUrl":29,"updatedAt":3550},"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},[3544,3545,3546,3547],{"name":17,"slug":18,"type":15},{"name":3360,"slug":3361,"type":15},{"name":3444,"slug":3445,"type":15},{"name":3548,"slug":3549,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":3552,"name":3552,"fn":3553,"description":3554,"org":3555,"tags":3556,"stars":28,"repoUrl":29,"updatedAt":3563},"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},[3557,3558,3559,3562],{"name":17,"slug":18,"type":15},{"name":3401,"slug":42,"type":15},{"name":3560,"slug":3561,"type":15},"Data Quality","data-quality",{"name":3444,"slug":3445,"type":15},"2026-04-06T18:02:02.138565"]