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Use when user says \"\u002Fastronomer-data:warehouse-init\" or asks to set up data discovery.",{"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 Engineering","data-engineering",{"name":20,"slug":21,"type":15},"Data Pipeline","data-pipeline",412,"https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fagents","2026-04-06T18:01:51.229323",null,55,[28,29,30,31,14,32,33,34,35,18,36,37,38,39,40,41,42,43,44,45],"agentic-workflow","agents","ai","ai-agents","apache-airflow","claude","cursor","dag","data-pipelines","dbt","llm","mcp","orchestrator","skills","workflow-automation","workflow-management","workflow-orchestration","workflows",{"repoUrl":23,"stars":22,"forks":26,"topics":47,"description":48},[28,29,30,31,14,32,33,34,35,18,36,37,38,39,40,41,42,43,44,45],"AI agent tooling for data engineering workflows.","https:\u002F\u002Fgithub.com\u002Fastronomer\u002Fagents\u002Ftree\u002FHEAD\u002Fskills\u002Fwarehouse-init","---\nname: warehouse-init\ndescription: Initialize warehouse schema discovery. Generates .astro\u002Fwarehouse.md with all table metadata for instant lookups. Run once per project, refresh when schema changes. Use when user says \"\u002Fastronomer-data:warehouse-init\" or asks to set up data discovery.\n---\n\n# Initialize Warehouse Schema\n\nGenerate a comprehensive, user-editable schema reference file for the data warehouse.\n\n**Scripts:** `..\u002Fanalyzing-data\u002Fscripts\u002F` — All CLI commands below are relative to the `analyzing-data` skill's directory. Before running any `scripts\u002Fcli.py` command, `cd` to `..\u002Fanalyzing-data\u002F` relative to this file.\n\n## What This Does\n\n1. Discovers all databases, schemas, tables, and columns from the warehouse\n2. **Enriches with codebase context** (dbt models, gusty SQL, schema docs)\n3. Records row counts and identifies large tables\n4. Generates `.astro\u002Fwarehouse.md` - a version-controllable, team-shareable reference\n5. Enables instant concept→table lookups without warehouse queries\n\n## Process\n\n### Step 1: Read Warehouse Configuration\n\n```bash\ncat ~\u002F.astro\u002Fagents\u002Fwarehouse.yml\n```\n\nGet the list of databases to discover (e.g., `databases: [HQ, ANALYTICS, RAW]`).\n\n### Step 2: Search Codebase for Context (Parallel)\n\n**Launch a subagent to find business context in code:**\n\n```\nTask(\n    subagent_type=\"Explore\",\n    prompt=\"\"\"\n    Search for data model documentation in the codebase:\n\n    1. dbt models: **\u002Fmodels\u002F**\u002F*.yml, **\u002Fschema.yml\n       - Extract table descriptions, column descriptions\n       - Note primary keys and tests\n\n    2. Gusty\u002Fdeclarative SQL: **\u002Fdags\u002F**\u002F*.sql with YAML frontmatter\n       - Parse frontmatter for: description, primary_key, tests\n       - Note schema mappings\n\n    3. AGENTS.md or CLAUDE.md files with data layer documentation\n\n    Return a mapping of:\n      table_name -> {description, primary_key, important_columns, layer}\n    \"\"\"\n)\n```\n\n### Step 3: Parallel Warehouse Discovery\n\n**Launch one subagent per database** using the Task tool:\n\n```\nFor each database in configured_databases:\n    Task(\n        subagent_type=\"general-purpose\",\n        prompt=\"\"\"\n        Discover all metadata for database {DATABASE}.\n\n        Use the CLI to run SQL queries:\n        # Scripts are relative to ..\u002Fanalyzing-data\u002F\n        uv run scripts\u002Fcli.py exec \"df = run_sql('...')\"\n        uv run scripts\u002Fcli.py exec \"print(df)\"\n\n        1. Query schemas:\n           SELECT SCHEMA_NAME FROM {DATABASE}.INFORMATION_SCHEMA.SCHEMATA\n\n        2. Query tables with row counts:\n           SELECT TABLE_SCHEMA, TABLE_NAME, ROW_COUNT, COMMENT\n           FROM {DATABASE}.INFORMATION_SCHEMA.TABLES\n           ORDER BY TABLE_SCHEMA, TABLE_NAME\n\n        3. For important schemas (MODEL_*, METRICS_*, MART_*), query columns:\n           SELECT TABLE_NAME, COLUMN_NAME, DATA_TYPE, COMMENT\n           FROM {DATABASE}.INFORMATION_SCHEMA.COLUMNS\n           WHERE TABLE_SCHEMA = 'X'\n\n        Return a structured summary:\n        - Database name\n        - List of schemas with table counts\n        - For each table: name, row_count, key columns\n        - Flag any tables with >100M rows as \"large\"\n        \"\"\"\n    )\n```\n\n**Run all subagents in parallel** (single message with multiple Task calls).\n\n### Step 4: Discover Categorical Value Families\n\nFor key categorical columns (like OPERATOR, STATUS, TYPE, FEATURE), discover value families:\n\n```bash\nuv run cli.py exec \"df = run_sql('''\nSELECT DISTINCT column_name, COUNT(*) as occurrences\nFROM table\nWHERE column_name IS NOT NULL\nGROUP BY column_name\nORDER BY occurrences DESC\nLIMIT 50\n''')\"\nuv run cli.py exec \"print(df)\"\n```\n\nGroup related values into families by common prefix\u002Fsuffix (e.g., `Export*` for ExportCSV, ExportJSON, ExportParquet).\n\n### Step 5: Merge Results\n\nCombine warehouse metadata + codebase context:\n\n1. **Quick Reference table** - concept → table mappings (pre-populated from code if found)\n2. **Categorical Columns** - value families for key filter columns\n3. **Database sections** - one per database\n4. **Schema subsections** - tables grouped by schema\n5. **Table details** - columns, row counts, **descriptions from code**, warnings\n\n### Step 6: Generate warehouse.md\n\nWrite the file to:\n- `.astro\u002Fwarehouse.md` (default - project-specific, version-controllable)\n- `~\u002F.astro\u002Fagents\u002Fwarehouse.md` (if `--global` flag)\n\n## Output Format\n\n```markdown\n# Warehouse Schema\n\n> Generated by `\u002Fastronomer-data:warehouse-init` on {DATE}. Edit freely to add business context.\n\n## Quick Reference\n\n| Concept | Table | Key Column | Date Column |\n|---------|-------|------------|-------------|\n| customers | HQ.MODEL_ASTRO.ORGANIZATIONS | ORG_ID | CREATED_AT |\n\u003C!-- Add your concept mappings here -->\n\n## Categorical Columns\n\nWhen filtering on these columns, explore value families first (values often have variants):\n\n| Table | Column | Value Families |\n|-------|--------|----------------|\n| {TABLE} | {COLUMN} | `{PREFIX}*` ({VALUE1}, {VALUE2}, ...) |\n\u003C!-- Populated by \u002Fastronomer-data:warehouse-init from actual warehouse data -->\n\n## Data Layer Hierarchy\n\nQuery downstream first: `reporting` > `mart_*` > `metric_*` > `model_*` > `IN_*`\n\n| Layer | Prefix | Purpose |\n|-------|--------|---------|\n| Reporting | `reporting.*` | Dashboard-optimized |\n| Mart | `mart_*` | Combined analytics |\n| Metric | `metric_*` | KPIs at various grains |\n| Model | `model_*` | Cleansed sources of truth |\n| Raw | `IN_*` | Source data - avoid |\n\n## {DATABASE} Database\n\n### {SCHEMA} Schema\n\n#### {TABLE_NAME}\n{DESCRIPTION from code if found}\n\n| Column | Type | Description |\n|--------|------|-------------|\n| COL1 | VARCHAR | {from code or inferred} |\n\n- **Rows:** {ROW_COUNT}\n- **Key column:** {PRIMARY_KEY from code or inferred}\n{IF ROW_COUNT > 100M: - **⚠️ WARNING:** Large table - always add date filters}\n\n## Relationships\n\n```\n{Inferred relationships based on column names like *_ID}\n```\n```\n\n## Command Options\n\n| Option | Effect |\n|--------|--------|\n| `\u002Fastronomer-data:warehouse-init` | Generate .astro\u002Fwarehouse.md |\n| `\u002Fastronomer-data:warehouse-init --refresh` | Regenerate, preserving user edits |\n| `\u002Fastronomer-data:warehouse-init --database HQ` | Only discover specific database |\n| `\u002Fastronomer-data:warehouse-init --global` | Write to ~\u002F.astro\u002Fagents\u002F instead |\n\n### Step 7: Pre-populate Cache\n\nAfter generating warehouse.md, populate the concept cache:\n\n```bash\n# Scripts are relative to ..\u002Fanalyzing-data\u002F\nuv run cli.py concept import -p .astro\u002Fwarehouse.md\nuv run cli.py concept learn customers HQ.MART_CUST.CURRENT_ASTRO_CUSTS -k ACCT_ID\n```\n\n### Step 8: Offer CLAUDE.md Integration (Ask User)\n\n**Ask the user:**\n\n> Would you like to add the Quick Reference table to your CLAUDE.md file?\n>\n> This ensures the schema mappings are always in context for data queries, improving accuracy from ~25% to ~100% for complex queries.\n>\n> Options:\n> 1. **Yes, add to CLAUDE.md** (Recommended) - Append Quick Reference section\n> 2. **No, skip** - Use warehouse.md and cache only\n\n**If user chooses Yes:**\n\n1. Check if `.claude\u002FCLAUDE.md` or `CLAUDE.md` exists\n2. If exists, append the Quick Reference section (avoid duplicates)\n3. If not exists, create `.claude\u002FCLAUDE.md` with just the Quick Reference\n\n**Quick Reference section to add:**\n\n```markdown\n## Data Warehouse Quick Reference\n\nWhen querying the warehouse, use these table mappings:\n\n| Concept | Table | Key Column | Date Column |\n|---------|-------|------------|-------------|\n{rows from warehouse.md Quick Reference}\n\n**Large tables (always filter by date):** {list tables with >100M rows}\n\n> Auto-generated by `\u002Fastronomer-data:warehouse-init`. Run `\u002Fastronomer-data:warehouse-init --refresh` to update.\n```\n**If yes:** Append the Quick Reference section to `.claude\u002FCLAUDE.md` or `CLAUDE.md`.\n\n## After Generation\n\nTell the user:\n\n```\nGenerated .astro\u002Fwarehouse.md\n\nSummary:\n  - {N} databases, {N} schemas, {N} tables\n  - {N} tables enriched with code descriptions\n  - {N} concepts cached for instant lookup\n\nNext steps:\n  1. Edit .astro\u002Fwarehouse.md to add business context\n  2. Commit to version control\n  3. Run \u002Fastronomer-data:warehouse-init --refresh when schema changes\n```\n\n## Refresh Behavior\n\nWhen `--refresh` is specified:\n\n1. Read existing warehouse.md\n2. Preserve all HTML comments (`\u003C!-- ... -->`)\n3. Preserve Quick Reference table entries (user-added)\n4. Preserve user-added descriptions\n5. Update row counts and add new tables\n6. Mark removed tables with `\u003C!-- REMOVED -->` comment\n\n## Cache Staleness & Schema Drift\n\nThe runtime cache has a **7-day TTL** by default. After 7 days, cached entries expire and will be re-discovered on next use.\n\n### When to Refresh\n\nRun `\u002Fastronomer-data:warehouse-init --refresh` when:\n- **Schema changes**: Tables added, renamed, or removed\n- **Column changes**: New columns added or types changed\n- **After deployments**: If your data pipeline deploys schema migrations\n- **Weekly**: As a good practice, even if no known changes\n\n### Signs of Stale Cache\n\nWatch for these indicators:\n- Queries fail with \"table not found\" errors\n- Results seem wrong or outdated\n- New tables aren't being discovered\n\n### Manual Cache Reset\n\nIf you suspect cache issues:\n\n```bash\n# Scripts are relative to ..\u002Fanalyzing-data\u002F\nuv run scripts\u002Fcli.py cache status\nuv run scripts\u002Fcli.py cache clear --stale-only\nuv run scripts\u002Fcli.py cache clear\n```\n\n## Codebase Patterns Recognized\n\n| Pattern | Source | What We Extract |\n|---------|--------|-----------------|\n| `**\u002Fmodels\u002F**\u002F*.yml` | dbt | table\u002Fcolumn descriptions, tests |\n| `**\u002Fdags\u002F**\u002F*.sql` | gusty | YAML frontmatter (description, primary_key) |\n| `AGENTS.md`, `CLAUDE.md` | docs | data layer hierarchy, conventions |\n| `**\u002Fdocs\u002F**\u002F*.md` | docs | business context |\n\n## Example Session\n\n```\nUser: \u002Fastronomer-data:warehouse-init\n\nAgent:\n→ Reading warehouse configuration...\n→ Found 1 warehouse with databases: HQ, PRODUCT\n\n→ Searching codebase for data documentation...\n  Found: AGENTS.md with data layer hierarchy\n  Found: 45 SQL files with YAML frontmatter in dags\u002Fdeclarative\u002F\n\n→ Launching parallel warehouse discovery...\n  [Database: HQ] Discovering schemas...\n  [Database: PRODUCT] Discovering schemas...\n\n→ HQ: Found 29 schemas, 401 tables\n→ PRODUCT: Found 1 schema, 0 tables\n\n→ Merging warehouse metadata with code context...\n  Enriched 45 tables with descriptions from code\n\n→ Generated .astro\u002Fwarehouse.md\n\nSummary:\n  - 2 databases\n  - 30 schemas\n  - 401 tables\n  - 45 tables enriched with code descriptions\n  - 8 large tables flagged (>100M rows)\n\nNext steps:\n  1. Review .astro\u002Fwarehouse.md\n  2. Add concept mappings to Quick Reference\n  3. Commit to version control\n  4. 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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},[2564,2565,2568,2569],{"name":13,"slug":14,"type":15},{"name":2566,"slug":2567,"type":15},"CLI","cli",{"name":20,"slug":21,"type":15},{"name":2570,"slug":2571,"type":15},"Debugging","debugging","2026-04-06T18:01:43.992997",{"slug":2574,"name":2574,"fn":2575,"description":2576,"org":2577,"tags":2578,"stars":22,"repoUrl":23,"updatedAt":2584},"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},[2579,2580,2583],{"name":13,"slug":14,"type":15},{"name":2581,"slug":2582,"type":15},"Approvals","approvals",{"name":20,"slug":21,"type":15},"2026-04-06T18:01:46.758548",{"slug":2586,"name":2586,"fn":2587,"description":2588,"org":2589,"tags":2590,"stars":22,"repoUrl":23,"updatedAt":2601},"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},[2591,2592,2595,2598],{"name":13,"slug":14,"type":15},{"name":2593,"slug":2594,"type":15},"Plugin Development","plugin-development",{"name":2596,"slug":2597,"type":15},"Python","python",{"name":2599,"slug":2600,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":2603,"name":2603,"fn":2604,"description":2605,"org":2606,"tags":2607,"stars":22,"repoUrl":23,"updatedAt":2614},"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},[2608,2609,2610,2611],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":2612,"slug":2613,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":96,"name":96,"fn":2616,"description":2617,"org":2618,"tags":2619,"stars":22,"repoUrl":23,"updatedAt":2630},"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},[2620,2623,2626,2627],{"name":2621,"slug":2622,"type":15},"Analytics","analytics",{"name":2624,"slug":2625,"type":15},"Data Analysis","data-analysis",{"name":17,"slug":18,"type":15},{"name":2628,"slug":2629,"type":15},"SQL","sql","2026-04-06T18:01:49.599775",{"slug":2632,"name":2632,"fn":2633,"description":2634,"org":2635,"tags":2636,"stars":22,"repoUrl":23,"updatedAt":2643},"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},[2637,2638,2639,2640],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":2641,"slug":2642,"type":15},"Observability","observability","2026-04-06T18:02:03.487365",{"slug":2645,"name":2645,"fn":2646,"description":2647,"org":2648,"tags":2649,"stars":22,"repoUrl":23,"updatedAt":2656},"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},[2650,2651,2652,2655],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":2653,"slug":2654,"type":15},"ETL","etl",{"name":2596,"slug":2597,"type":15},"2026-04-06T18:01:52.679888",{"slug":2658,"name":2658,"fn":2659,"description":2660,"org":2661,"tags":2662,"stars":22,"repoUrl":23,"updatedAt":2671},"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},[2663,2664,2667,2668],{"name":13,"slug":14,"type":15},{"name":2665,"slug":2666,"type":15},"API Development","api-development",{"name":20,"slug":21,"type":15},{"name":2669,"slug":2670,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":2673,"name":2673,"fn":2674,"description":2675,"org":2676,"tags":2677,"stars":22,"repoUrl":23,"updatedAt":2685},"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},[2678,2679,2682],{"name":20,"slug":21,"type":15},{"name":2680,"slug":2681,"type":15},"Engineering","engineering",{"name":2683,"slug":2684,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":2687,"name":2687,"fn":2688,"description":2689,"org":2690,"tags":2691,"stars":22,"repoUrl":23,"updatedAt":2695},"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},[2692,2693,2694],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":2680,"slug":2681,"type":15},"2026-07-18T05:11:54.496539",{"slug":2697,"name":2697,"fn":2698,"description":2699,"org":2700,"tags":2701,"stars":22,"repoUrl":23,"updatedAt":2708},"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},[2702,2703,2704,2705],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":2653,"slug":2654,"type":15},{"name":2706,"slug":2707,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":2710,"name":2710,"fn":2711,"description":2712,"org":2713,"tags":2714,"stars":22,"repoUrl":23,"updatedAt":2721},"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},[2715,2716,2717,2720],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":2718,"slug":2719,"type":15},"Data Quality","data-quality",{"name":2653,"slug":2654,"type":15},"2026-04-06T18:02:02.138565",{"items":2723,"total":1329},[2724,2731,2737,2744,2751,2758,2765],{"slug":14,"name":14,"fn":2560,"description":2561,"org":2725,"tags":2726,"stars":22,"repoUrl":23,"updatedAt":2572},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2727,2728,2729,2730],{"name":13,"slug":14,"type":15},{"name":2566,"slug":2567,"type":15},{"name":20,"slug":21,"type":15},{"name":2570,"slug":2571,"type":15},{"slug":2574,"name":2574,"fn":2575,"description":2576,"org":2732,"tags":2733,"stars":22,"repoUrl":23,"updatedAt":2584},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2734,2735,2736],{"name":13,"slug":14,"type":15},{"name":2581,"slug":2582,"type":15},{"name":20,"slug":21,"type":15},{"slug":2586,"name":2586,"fn":2587,"description":2588,"org":2738,"tags":2739,"stars":22,"repoUrl":23,"updatedAt":2601},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2740,2741,2742,2743],{"name":13,"slug":14,"type":15},{"name":2593,"slug":2594,"type":15},{"name":2596,"slug":2597,"type":15},{"name":2599,"slug":2600,"type":15},{"slug":2603,"name":2603,"fn":2604,"description":2605,"org":2745,"tags":2746,"stars":22,"repoUrl":23,"updatedAt":2614},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2747,2748,2749,2750],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":2612,"slug":2613,"type":15},{"slug":96,"name":96,"fn":2616,"description":2617,"org":2752,"tags":2753,"stars":22,"repoUrl":23,"updatedAt":2630},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2754,2755,2756,2757],{"name":2621,"slug":2622,"type":15},{"name":2624,"slug":2625,"type":15},{"name":17,"slug":18,"type":15},{"name":2628,"slug":2629,"type":15},{"slug":2632,"name":2632,"fn":2633,"description":2634,"org":2759,"tags":2760,"stars":22,"repoUrl":23,"updatedAt":2643},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2761,2762,2763,2764],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":20,"slug":21,"type":15},{"name":2641,"slug":2642,"type":15},{"slug":2645,"name":2645,"fn":2646,"description":2647,"org":2766,"tags":2767,"stars":22,"repoUrl":23,"updatedAt":2656},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[2768,2769,2770,2771],{"name":13,"slug":14,"type":15},{"name":20,"slug":21,"type":15},{"name":2653,"slug":2654,"type":15},{"name":2596,"slug":2597,"type":15}]