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Use when running a dbt Fusion project with Astronomer Cosmos (Cosmos 1.11+, ExecutionMode.LOCAL on Snowflake\u002FDatabricks). Before implementing, verify dbt engine is Fusion (not Core), the warehouse is supported, and local execution is acceptable. Does not cover dbt Core.\n---\n\n# Cosmos + dbt Fusion: Implementation Checklist\n\nExecute steps in order. This skill covers Fusion-specific constraints only.\n\n> **Version note**: dbt Fusion support was introduced in Cosmos 1.11.0. Requires Cosmos ≥1.11.\n>\n> **Reference**: See **[reference\u002Fcosmos-config.md](reference\u002Fcosmos-config.md)** for ProfileConfig, operator_args, and Airflow 3 compatibility details.\n\n> **Before starting**, confirm: (1) dbt engine = Fusion (not Core → use **cosmos-dbt-core**), (2) warehouse = Snowflake, Databricks, Bigquery and Redshift only.\n\n### Fusion-Specific Constraints\n\n| Constraint | Details |\n|------------|---------|\n| No async | `AIRFLOW_ASYNC` not supported |\n| No virtualenv | Fusion is a binary, not a Python package |\n| Warehouse support | Snowflake, Databricks, Bigquery and Redshift support [while in preview](https:\u002F\u002Fgithub.com\u002Fdbt-labs\u002Fdbt-fusion) |\n\n---\n\n## 1. Confirm Cosmos Version\n\n> **CRITICAL**: Cosmos 1.11.0 introduced dbt Fusion compatibility.\n\n```bash\n# Check installed version\npip show astronomer-cosmos\n\n# Install\u002Fupgrade if needed\npip install \"astronomer-cosmos>=1.11.0\"\n```\n\n**Validate**: `pip show astronomer-cosmos` reports version ≥ 1.11.0\n\n---\n\n## 2. Install the dbt Fusion Binary (REQUIRED)\n\ndbt Fusion is NOT bundled with Cosmos or dbt Core. Install it into the Airflow runtime\u002Fimage.\n\nDetermine where to install the Fusion binary (Dockerfile \u002F base image \u002F runtime).\n\n### Example Dockerfile Install\n\n```dockerfile\nUSER root\nRUN apt-get update && apt-get install -y curl\nENV SHELL=\u002Fbin\u002Fbash\nRUN curl -fsSL https:\u002F\u002Fpublic.cdn.getdbt.com\u002Ffs\u002Finstall\u002Finstall.sh | sh -s -- --update\nUSER astro\n```\n\n### Common Install Paths\n\n| Environment | Typical path |\n|-------------|--------------|\n| Astro Runtime | `\u002Fhome\u002Fastro\u002F.local\u002Fbin\u002Fdbt` |\n| System-wide | `\u002Fusr\u002Flocal\u002Fbin\u002Fdbt` |\n\n**Validate**: The `dbt` binary exists at the chosen path and `dbt --version` succeeds.\n\n---\n\n## 3. Choose Parsing Strategy (RenderConfig)\n\nParsing strategy is the same as dbt Core. Pick ONE:\n\n| Load mode | When to use | Required inputs |\n|-----------|-------------|-----------------|\n| `dbt_manifest` | Large projects; fastest parsing | `ProjectConfig.manifest_path` |\n| `dbt_ls` | Complex selectors; need dbt-native selection | Fusion binary accessible to scheduler |\n| `automatic` | Simple setups; let Cosmos pick | (none) |\n\n```python\nfrom cosmos import RenderConfig, LoadMode\n\n_render_config = RenderConfig(\n    load_method=LoadMode.AUTOMATIC,  # or DBT_MANIFEST, DBT_LS\n)\n```\n\n---\n\n## 4. Configure Warehouse Connection (ProfileConfig)\n\n> **Reference**: See **[reference\u002Fcosmos-config.md](reference\u002Fcosmos-config.md#profileconfig-warehouse-connection)** for full ProfileConfig options and examples.\n\n\n```python\nfrom cosmos import ProfileConfig\nfrom cosmos.profiles import SnowflakeUserPasswordProfileMapping\n\n_profile_config = ProfileConfig(\n    profile_name=\"default\",\n    target_name=\"dev\",\n    profile_mapping=SnowflakeUserPasswordProfileMapping(\n        conn_id=\"snowflake_default\",\n    ),\n)\n```\n\n---\n\n## 5. Configure ExecutionConfig (LOCAL Only)\n\n> **CRITICAL**: dbt Fusion with Cosmos requires `ExecutionMode.LOCAL` with `dbt_executable_path` pointing to the Fusion binary.\n\n```python\nfrom cosmos import ExecutionConfig\nfrom cosmos.constants import InvocationMode\n\n_execution_config = ExecutionConfig(\n    invocation_mode=InvocationMode.SUBPROCESS,\n    dbt_executable_path=\"\u002Fhome\u002Fastro\u002F.local\u002Fbin\u002Fdbt\",  # REQUIRED: path to Fusion binary\n    # execution_mode is LOCAL by default - do not change\n)\n```\n\n---\n\n## 6. Configure Project (ProjectConfig)\n\n```python\nfrom cosmos import ProjectConfig\n\n_project_config = ProjectConfig(\n    dbt_project_path=\"\u002Fpath\u002Fto\u002Fdbt\u002Fproject\",\n    # manifest_path=\"\u002Fpath\u002Fto\u002Fmanifest.json\",  # for dbt_manifest load mode\n    # install_dbt_deps=False,  # if deps precomputed in CI\n)\n```\n\n---\n\n## 7. Assemble DAG \u002F TaskGroup\n\n### Option A: DbtDag (Standalone)\n\n```python\nfrom cosmos import DbtDag, ProjectConfig, ProfileConfig, ExecutionConfig, RenderConfig\nfrom cosmos.profiles import SnowflakeUserPasswordProfileMapping\nfrom pendulum import datetime\n\n_project_config = ProjectConfig(\n    dbt_project_path=\"\u002Fusr\u002Flocal\u002Fairflow\u002Fdbt\u002Fmy_project\",\n)\n\n_profile_config = ProfileConfig(\n    profile_name=\"default\",\n    target_name=\"dev\",\n    profile_mapping=SnowflakeUserPasswordProfileMapping(\n        conn_id=\"snowflake_default\",\n    ),\n)\n\n_execution_config = ExecutionConfig(\n    dbt_executable_path=\"\u002Fhome\u002Fastro\u002F.local\u002Fbin\u002Fdbt\",  # Fusion binary\n)\n\n_render_config = RenderConfig()\n\nmy_fusion_dag = DbtDag(\n    dag_id=\"my_fusion_cosmos_dag\",\n    project_config=_project_config,\n    profile_config=_profile_config,\n    execution_config=_execution_config,\n    render_config=_render_config,\n    start_date=datetime(2025, 1, 1),\n    schedule=\"@daily\",\n)\n```\n\n### Option B: DbtTaskGroup (Inside Existing DAG)\n\n```python\nfrom airflow.sdk import dag, task  # Airflow 3.x\n# from airflow.decorators import dag, task  # Airflow 2.x\nfrom airflow.models.baseoperator import chain\nfrom cosmos import DbtTaskGroup, ProjectConfig, ProfileConfig, ExecutionConfig\nfrom pendulum import datetime\n\n_project_config = ProjectConfig(dbt_project_path=\"\u002Fusr\u002Flocal\u002Fairflow\u002Fdbt\u002Fmy_project\")\n_profile_config = ProfileConfig(profile_name=\"default\", target_name=\"dev\")\n_execution_config = ExecutionConfig(dbt_executable_path=\"\u002Fhome\u002Fastro\u002F.local\u002Fbin\u002Fdbt\")\n\n@dag(start_date=datetime(2025, 1, 1), schedule=\"@daily\")\ndef my_dag():\n    @task\n    def pre_dbt():\n        return \"some_value\"\n\n    dbt = DbtTaskGroup(\n        group_id=\"dbt_fusion_project\",\n        project_config=_project_config,\n        profile_config=_profile_config,\n        execution_config=_execution_config,\n    )\n\n    @task\n    def post_dbt():\n        pass\n\n    chain(pre_dbt(), dbt, post_dbt())\n\nmy_dag()\n```\n\n---\n\n## 8. Final Validation\n\nBefore finalizing, verify:\n\n- [ ] **Cosmos version**: ≥1.11.0\n- [ ] **Fusion binary installed**: Path exists and is executable\n- [ ] **Warehouse supported**: Snowflake, Databricks, Bigquery or Redshift only\n- [ ] **Secrets handling**: Airflow connections or env vars, NOT plaintext\n\n### Troubleshooting\n\nIf user reports dbt Core regressions after enabling Fusion:\n\n```bash\nAIRFLOW__COSMOS__PRE_DBT_FUSION=1\n```\n\n### User Must Test\n\n- [ ] The DAG parses in the Airflow UI (no import\u002Fparse-time errors)\n- [ ] A manual run succeeds against the target warehouse (at least one model)\n\n---\n\n## Reference\n\n- Cosmos dbt Fusion docs: https:\u002F\u002Fastronomer.github.io\u002Fastronomer-cosmos\u002Fconfiguration\u002Fdbt-fusion.html\n- dbt Fusion install: https:\u002F\u002Fdocs.getdbt.com\u002Fdocs\u002Fcore\u002Fpip-install#dbt-fusion\n\n---\n\n## Related Skills\n\n- **cosmos-dbt-core**: For dbt Core projects (not Fusion)\n- **authoring-dags**: General DAG authoring patterns\n- **testing-dags**: Testing DAGs after creation\n",{"data":56,"body":57},{"name":4,"description":6},{"type":58,"children":59},"root",[60,69,75,111,131,138,220,224,231,244,336,354,357,363,368,373,379,428,434,490,514,517,523,528,625,673,676,682,703,793,796,802,830,899,902,908,969,972,978,984,1242,1248,1487,1490,1496,1501,1572,1578,1583,1609,1615,1637,1640,1645,1670,1673,1679,1711],{"type":61,"tag":62,"props":63,"children":65},"element","h1",{"id":64},"cosmos-dbt-fusion-implementation-checklist",[66],{"type":67,"value":68},"text","Cosmos + dbt Fusion: Implementation Checklist",{"type":61,"tag":70,"props":71,"children":72},"p",{},[73],{"type":67,"value":74},"Execute steps in order. 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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},[1724,1725,1728,1729],{"name":13,"slug":14,"type":15},{"name":1726,"slug":1727,"type":15},"CLI","cli",{"name":23,"slug":24,"type":15},{"name":1730,"slug":1731,"type":15},"Debugging","debugging","2026-04-06T18:01:43.992997",{"slug":1734,"name":1734,"fn":1735,"description":1736,"org":1737,"tags":1738,"stars":27,"repoUrl":28,"updatedAt":1744},"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},[1739,1740,1743],{"name":13,"slug":14,"type":15},{"name":1741,"slug":1742,"type":15},"Approvals","approvals",{"name":23,"slug":24,"type":15},"2026-04-06T18:01:46.758548",{"slug":1746,"name":1746,"fn":1747,"description":1748,"org":1749,"tags":1750,"stars":27,"repoUrl":28,"updatedAt":1760},"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},[1751,1752,1755,1757],{"name":13,"slug":14,"type":15},{"name":1753,"slug":1754,"type":15},"Plugin Development","plugin-development",{"name":1756,"slug":629,"type":15},"Python",{"name":1758,"slug":1759,"type":15},"UI Components","ui-components","2026-04-06T18:01:56.827891",{"slug":1762,"name":1762,"fn":1763,"description":1764,"org":1765,"tags":1766,"stars":27,"repoUrl":28,"updatedAt":1773},"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},[1767,1768,1769,1770],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1771,"slug":1772,"type":15},"Operations","operations","2026-07-07T06:43:11.160671",{"slug":1775,"name":1775,"fn":1776,"description":1777,"org":1778,"tags":1779,"stars":27,"repoUrl":28,"updatedAt":1790},"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},[1780,1783,1786,1787],{"name":1781,"slug":1782,"type":15},"Analytics","analytics",{"name":1784,"slug":1785,"type":15},"Data Analysis","data-analysis",{"name":17,"slug":18,"type":15},{"name":1788,"slug":1789,"type":15},"SQL","sql","2026-04-06T18:01:49.599775",{"slug":1792,"name":1792,"fn":1793,"description":1794,"org":1795,"tags":1796,"stars":27,"repoUrl":28,"updatedAt":1803},"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},[1797,1798,1799,1800],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1801,"slug":1802,"type":15},"Observability","observability","2026-04-06T18:02:03.487365",{"slug":1698,"name":1698,"fn":1805,"description":1806,"org":1807,"tags":1808,"stars":27,"repoUrl":28,"updatedAt":1813},"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},[1809,1810,1811,1812],{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},{"name":1756,"slug":629,"type":15},"2026-04-06T18:01:52.679888",{"slug":1815,"name":1815,"fn":1816,"description":1817,"org":1818,"tags":1819,"stars":27,"repoUrl":28,"updatedAt":1828},"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},[1820,1821,1824,1825],{"name":13,"slug":14,"type":15},{"name":1822,"slug":1823,"type":15},"API Development","api-development",{"name":23,"slug":24,"type":15},{"name":1826,"slug":1827,"type":15},"Go","go","2026-07-11T05:39:13.552213",{"slug":1830,"name":1830,"fn":1831,"description":1832,"org":1833,"tags":1834,"stars":27,"repoUrl":28,"updatedAt":1842},"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},[1835,1836,1839],{"name":23,"slug":24,"type":15},{"name":1837,"slug":1838,"type":15},"Engineering","engineering",{"name":1840,"slug":1841,"type":15},"Java","java","2026-07-18T05:48:13.374003",{"slug":1844,"name":1844,"fn":1845,"description":1846,"org":1847,"tags":1848,"stars":27,"repoUrl":28,"updatedAt":1852},"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},[1849,1850,1851],{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},{"name":1837,"slug":1838,"type":15},"2026-07-18T05:11:54.496539",{"slug":1854,"name":1854,"fn":1855,"description":1856,"org":1857,"tags":1858,"stars":27,"repoUrl":28,"updatedAt":1865},"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},[1859,1860,1861,1862],{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},{"name":1863,"slug":1864,"type":15},"Templates","templates","2026-04-06T18:01:45.361425",{"slug":1867,"name":1867,"fn":1868,"description":1869,"org":1870,"tags":1871,"stars":27,"repoUrl":28,"updatedAt":1878},"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},[1872,1873,1874,1877],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":1875,"slug":1876,"type":15},"Data Quality","data-quality",{"name":20,"slug":21,"type":15},"2026-04-06T18:02:02.138565",34,{"items":1881,"total":1879},[1882,1889,1895,1902,1909,1916,1923],{"slug":14,"name":14,"fn":1720,"description":1721,"org":1883,"tags":1884,"stars":27,"repoUrl":28,"updatedAt":1732},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1885,1886,1887,1888],{"name":13,"slug":14,"type":15},{"name":1726,"slug":1727,"type":15},{"name":23,"slug":24,"type":15},{"name":1730,"slug":1731,"type":15},{"slug":1734,"name":1734,"fn":1735,"description":1736,"org":1890,"tags":1891,"stars":27,"repoUrl":28,"updatedAt":1744},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1892,1893,1894],{"name":13,"slug":14,"type":15},{"name":1741,"slug":1742,"type":15},{"name":23,"slug":24,"type":15},{"slug":1746,"name":1746,"fn":1747,"description":1748,"org":1896,"tags":1897,"stars":27,"repoUrl":28,"updatedAt":1760},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1898,1899,1900,1901],{"name":13,"slug":14,"type":15},{"name":1753,"slug":1754,"type":15},{"name":1756,"slug":629,"type":15},{"name":1758,"slug":1759,"type":15},{"slug":1762,"name":1762,"fn":1763,"description":1764,"org":1903,"tags":1904,"stars":27,"repoUrl":28,"updatedAt":1773},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1905,1906,1907,1908],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1771,"slug":1772,"type":15},{"slug":1775,"name":1775,"fn":1776,"description":1777,"org":1910,"tags":1911,"stars":27,"repoUrl":28,"updatedAt":1790},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1912,1913,1914,1915],{"name":1781,"slug":1782,"type":15},{"name":1784,"slug":1785,"type":15},{"name":17,"slug":18,"type":15},{"name":1788,"slug":1789,"type":15},{"slug":1792,"name":1792,"fn":1793,"description":1794,"org":1917,"tags":1918,"stars":27,"repoUrl":28,"updatedAt":1803},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1919,1920,1921,1922],{"name":13,"slug":14,"type":15},{"name":17,"slug":18,"type":15},{"name":23,"slug":24,"type":15},{"name":1801,"slug":1802,"type":15},{"slug":1698,"name":1698,"fn":1805,"description":1806,"org":1924,"tags":1925,"stars":27,"repoUrl":28,"updatedAt":1813},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[1926,1927,1928,1929],{"name":13,"slug":14,"type":15},{"name":23,"slug":24,"type":15},{"name":20,"slug":21,"type":15},{"name":1756,"slug":629,"type":15}]