[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill:01M699FNVP31WZ6NS3SQG0FPVB":3,"skill-same:01M699FNVP31WZ6NS3SQG0FPVB":58},{"skillNo":4,"name":5,"nameZh":6,"description":7,"descriptionZh":6,"repositoryFullName":8,"ownerLogin":9,"ownerAvatarUrl":10,"ownerAccountNo":11,"ownerDisplayName":12,"repositoryNo":13,"starsCount":14,"licenseSpdxId":15,"licenseFileUrl":6,"sourceHtmlUrl":16,"categories":17,"keywords":18,"publishedAt":30,"linkOnly":31,"markdownContent":32,"markdownBody":33,"frontMatter":34,"refName":35,"commitSha":36,"skillDirectory":37,"forksCount":38,"repositoryHtmlUrl":39,"repositoryHomepageUrl":6,"ownerHtmlUrl":40,"ownerBlogUrl":41,"ownerDescription":42,"updatedAt":43,"install":44,"resources":57},"01M699FNVP31WZ6NS3SQG0FPVB","add-function",null,"Guide for adding new functions to the library. Use this when implementing new API wrappers or utility functions.","microsoft\u002Fsemantic-link-labs","microsoft","https:\u002F\u002Favatars.githubusercontent.com\u002Fu\u002F6154722?v=4","01M698VYDXZ6JW5WH3CRV650R8","Microsoft","01M699FKMT63C17BFNH8WHZN2K",599,"MIT","https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-link-labs\u002Fblob\u002Fmain\u002F.claude\u002Fskills\u002Fadd-function\u002FSKILL.md",[],[19,20,21,22,23,24,25,26,27,28,29],"add function","add","library","api","guide","use","functions","utility","implementing","for","when","2026-11-10 10:12:30",false,"---\nname: add-function\ndescription: Guide for adding new functions to the library. Use this when implementing new API wrappers or utility functions.\n---\n\n# Adding New Functions\n\nThis skill covers the workflow for adding new functions to the Semantic Link Labs library.\n\n## When to Use This Skill\n\nUse this skill when you need to:\n- Add a new API wrapper function\n- Create a new utility function\n- Extend existing functionality with new features\n- Add functions to submodules (admin, report, lakehouse, etc.)\n\n---\n\n## Function Categories\n\n| Category | Location | Purpose |\n|----------|----------|---------|\n| **Top-level functions** | `src\u002Fsempy_labs\u002F_*.py` | Main library exports |\n| **Admin functions** | `src\u002Fsempy_labs\u002Fadmin\u002F` | Admin API operations |\n| **Report functions** | `src\u002Fsempy_labs\u002Freport\u002F` | Report operations |\n| **Lakehouse functions** | `src\u002Fsempy_labs\u002Flakehouse\u002F` | Lakehouse operations |\n| **Direct Lake functions** | `src\u002Fsempy_labs\u002Fdirectlake\u002F` | Direct Lake model operations |\n| **TOM methods** | `src\u002Fsempy_labs\u002Ftom\u002F_model.py` | TOMWrapper class methods |\n\n---\n\n## Step 0: Find the API Documentation\n\nBefore implementing an API wrapper, find the relevant API documentation:\n\n```bash\n# Use the API search tool\ncd .claude\u002Fskills\u002Frest-api-patterns\u002Fscripts\npython search_public_api_doc.py \"your search query\"\n\n# Examples:\npython search_public_api_doc.py \"workspace users\" --source fabric\npython search_public_api_doc.py \"dataset refresh\" --source powerbi\n```\n\nSee the [REST API Patterns](..\u002Frest-api-patterns\u002FSKILL.md) skill for more details.\n\n---\n\n## Step 1: Choose the Right Location\n\n### Top-Level Function\n\nFor general-purpose functions exported from `sempy_labs`:\n\n```python\n# src\u002Fsempy_labs\u002F_my_feature.py\n```\n\n### Submodule Function\n\nFor functions belonging to a specific domain:\n\n```python\n# src\u002Fsempy_labs\u002Fadmin\u002F_my_admin_function.py\n# src\u002Fsempy_labs\u002Flakehouse\u002F_my_lakehouse_function.py\n# src\u002Fsempy_labs\u002Freport\u002F_my_report_function.py\n```\n\n---\n\n## Step 2: Create the Function\n\n### Required Imports\n\n```python\nimport pandas as pd\nfrom typing import Optional, List\nfrom uuid import UUID\n\n# Logging decorator from sempy\nfrom sempy._utils._log import log\n\n# Helper functions\nfrom sempy_labs._helper_functions import (\n    resolve_workspace_name_and_id,\n    resolve_workspace_id,\n    _base_api,\n    _create_dataframe,\n)\n\n# Icons for user messages\nimport sempy_labs._icons as icons\n```\n\n### Function Template\n\n```python\n@log\ndef my_new_function(\n    item: str | UUID,\n    workspace: Optional[str | UUID] = None,\n    option: str = \"default\",\n) -> pd.DataFrame:\n    \"\"\"\n    Short description of what the function does.\n\n    Extended description with more details about the function's behavior,\n    use cases, and any important notes.\n\n    This is a wrapper function for the following API: `API Name \u003Chttps:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002F...>`_.\n\n    Service Principal Authentication is supported (see `here \u003Chttps:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-link-labs\u002Fblob\u002Fmain\u002Fnotebooks\u002FService%20Principal.ipynb>`_ for examples).\n\n    Parameters\n    ----------\n    item : str | uuid.UUID\n        The name or ID of the item.\n    workspace : str | uuid.UUID, default=None\n        The Fabric workspace name or ID.\n        Defaults to None which resolves to the workspace of the attached lakehouse\n        or if no lakehouse attached, resolves to the workspace of the notebook.\n    option : str, default=\"default\"\n        An option that controls function behavior.\n\n    Returns\n    -------\n    pandas.DataFrame\n        A pandas dataframe showing the results.\n        Columns include: 'Column1', 'Column2', 'Column3'.\n\n    Raises\n    ------\n    ValueError\n        If the item does not exist.\n    FabricHTTPException\n        If the API request fails.\n    \"\"\"\n\n    # Resolve workspace\n    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)\n\n    # Define result DataFrame structure\n    columns = {\n        \"Column1\": \"string\",\n        \"Column2\": \"string\",\n        \"Column3\": \"int\",\n    }\n    df = _create_dataframe(columns=columns)\n\n    # Make API call\n    responses = _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\",\n        uses_pagination=True,\n        client=\"fabric_sp\",\n    )\n\n    # Process responses\n    rows = []\n    for r in responses:\n        for item in r.get(\"value\", []):\n            rows.append({\n                \"Column1\": item.get(\"id\"),\n                \"Column2\": item.get(\"name\"),\n                \"Column3\": item.get(\"count\", 0),\n            })\n\n    if rows:\n        df = pd.DataFrame(rows)\n\n    return df\n```\n\n---\n\n## Step 3: Export the Function\n\n### From Module File\n\nAdd to the module's `__init__.py`:\n\n```python\n# src\u002Fsempy_labs\u002Fadmin\u002F__init__.py (example for admin submodule)\n\nfrom ._my_admin_function import my_new_function\n\n__all__ = [\n    ...,\n    \"my_new_function\",\n]\n```\n\n### From Main Package\n\nFor top-level functions, add to `src\u002Fsempy_labs\u002F__init__.py`:\n\n```python\nfrom ._my_feature import my_new_function\n\n__all__ = [\n    ...,\n    \"my_new_function\",\n]\n```\n\n---\n\n## Common Patterns\n\n### Functions That Modify Resources\n\n```python\n@log\ndef create_item(\n    name: str,\n    workspace: Optional[str | UUID] = None,\n) -> None:\n    \"\"\"\n    Creates a new item.\n    ...\n    \"\"\"\n    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)\n\n    payload = {\n        \"displayName\": name,\n    }\n\n    _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\",\n        method=\"post\",\n        payload=payload,\n        status_codes=[201, 202],\n        client=\"fabric_sp\",\n    )\n\n    print(\n        f\"{icons.green_dot} The '{name}' item has been successfully created \"\n        f\"in the '{workspace_name}' workspace.\"\n    )\n```\n\n### Functions That Delete Resources\n\n```python\n@log\ndef delete_item(\n    item: str | UUID,\n    workspace: Optional[str | UUID] = None,\n) -> None:\n    \"\"\"\n    Deletes an item.\n    ...\n    \"\"\"\n    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)\n    item_id = resolve_item_id(item=item, type=\"ItemType\", workspace=workspace_id)\n\n    _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\u002F{item_id}\",\n        method=\"delete\",\n        client=\"fabric_sp\",\n    )\n\n    print(\n        f\"{icons.green_dot} The item has been successfully deleted \"\n        f\"from the '{workspace_name}' workspace.\"\n    )\n```\n\n### Functions With Long-Running Operations\n\n```python\n@log\ndef long_running_operation(\n    item: str | UUID,\n    workspace: Optional[str | UUID] = None,\n) -> dict:\n    \"\"\"\n    Performs a long-running operation.\n    ...\n    \"\"\"\n    workspace_id = resolve_workspace_id(workspace)\n    item_id = resolve_item_id(item=item, type=\"ItemType\", workspace=workspace_id)\n\n    # lro_return_json handles polling for completion\n    result = _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\u002F{item_id}\u002Foperation\",\n        method=\"post\",\n        lro_return_json=True,\n        client=\"fabric_sp\",\n    )\n\n    return result\n```\n\n---\n\n## Step 4: Add Tests\n\nCreate tests for the new function:\n\n```python\n# tests\u002Ftest_my_feature.py\n\nimport pytest\nimport pandas as pd\n\n\ndef test_my_new_function_returns_dataframe():\n    \"\"\"Test that my_new_function returns a DataFrame.\"\"\"\n    from sempy_labs import my_new_function\n\n    # This might require mocking for unit tests\n    result = my_new_function()\n\n    assert isinstance(result, pd.DataFrame)\n\n\ndef test_my_new_function_with_workspace():\n    \"\"\"Test my_new_function with specific workspace.\"\"\"\n    from sempy_labs import my_new_function\n\n    result = my_new_function(workspace=\"Test Workspace\")\n\n    assert isinstance(result, pd.DataFrame)\n```\n\n---\n\n## Step 5: Document the Function\n\nEnsure the docstring follows numpydoc style:\n\n1. ✅ Short description (one line)\n2. ✅ Extended description (if needed)\n3. ✅ API reference link (for wrapper functions)\n4. ✅ Service Principal note (if supported)\n5. ✅ All parameters documented with types\n6. ✅ Return value documented\n7. ✅ Exceptions documented (if applicable)\n\n---\n\n## Checklist Before Committing\n\n- [ ] Function follows naming conventions (`list_`, `get_`, `create_`, etc.)\n- [ ] `@log` decorator is applied\n- [ ] Complete docstring with numpydoc style\n- [ ] Type hints for all parameters and return value\n- [ ] Uses standard helper functions (`_base_api`, `resolve_*`, etc.)\n- [ ] Function exported in `__init__.py`\n- [ ] Tests written for the new function\n- [ ] Code formatted with black\n- [ ] No linting errors\n- [ ] Documentation builds without warnings\n\n---\n\n## Example: Complete New Function\n\nSee [_workspaces.py](..\u002F..\u002Fsrc\u002Fsempy_labs\u002F_workspaces.py) for well-implemented examples:\n\n- `list_workspace_users` — List function returning DataFrame\n- `update_workspace_user` — Update function with parameters\n- `delete_user_from_workspace` — Delete function with confirmation message\n\n---\n\n## API Documentation Resources\n\nWhen wrapping REST APIs, reference the official documentation:\n\n| API | Documentation |\n|-----|---------------|\n| Fabric Core API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fcore\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fcore\u002F) |\n| Fabric Admin API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fadmin\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fadmin\u002F) |\n| Power BI REST API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fpower-bi\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fpower-bi\u002F) |\n| Azure Management API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fresources\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fresources\u002F) |\n","# Adding New Functions\n\nThis skill covers the workflow for adding new functions to the Semantic Link Labs library.\n\n## When to Use This Skill\n\nUse this skill when you need to:\n- Add a new API wrapper function\n- Create a new utility function\n- Extend existing functionality with new features\n- Add functions to submodules (admin, report, lakehouse, etc.)\n\n---\n\n## Function Categories\n\n| Category | Location | Purpose |\n|----------|----------|---------|\n| **Top-level functions** | `src\u002Fsempy_labs\u002F_*.py` | Main library exports |\n| **Admin functions** | `src\u002Fsempy_labs\u002Fadmin\u002F` | Admin API operations |\n| **Report functions** | `src\u002Fsempy_labs\u002Freport\u002F` | Report operations |\n| **Lakehouse functions** | `src\u002Fsempy_labs\u002Flakehouse\u002F` | Lakehouse operations |\n| **Direct Lake functions** | `src\u002Fsempy_labs\u002Fdirectlake\u002F` | Direct Lake model operations |\n| **TOM methods** | `src\u002Fsempy_labs\u002Ftom\u002F_model.py` | TOMWrapper class methods |\n\n---\n\n## Step 0: Find the API Documentation\n\nBefore implementing an API wrapper, find the relevant API documentation:\n\n```bash\n# Use the API search tool\ncd .claude\u002Fskills\u002Frest-api-patterns\u002Fscripts\npython search_public_api_doc.py \"your search query\"\n\n# Examples:\npython search_public_api_doc.py \"workspace users\" --source fabric\npython search_public_api_doc.py \"dataset refresh\" --source powerbi\n```\n\nSee the [REST API Patterns](..\u002Frest-api-patterns\u002FSKILL.md) skill for more details.\n\n---\n\n## Step 1: Choose the Right Location\n\n### Top-Level Function\n\nFor general-purpose functions exported from `sempy_labs`:\n\n```python\n# src\u002Fsempy_labs\u002F_my_feature.py\n```\n\n### Submodule Function\n\nFor functions belonging to a specific domain:\n\n```python\n# src\u002Fsempy_labs\u002Fadmin\u002F_my_admin_function.py\n# src\u002Fsempy_labs\u002Flakehouse\u002F_my_lakehouse_function.py\n# src\u002Fsempy_labs\u002Freport\u002F_my_report_function.py\n```\n\n---\n\n## Step 2: Create the Function\n\n### Required Imports\n\n```python\nimport pandas as pd\nfrom typing import Optional, List\nfrom uuid import UUID\n\n# Logging decorator from sempy\nfrom sempy._utils._log import log\n\n# Helper functions\nfrom sempy_labs._helper_functions import (\n    resolve_workspace_name_and_id,\n    resolve_workspace_id,\n    _base_api,\n    _create_dataframe,\n)\n\n# Icons for user messages\nimport sempy_labs._icons as icons\n```\n\n### Function Template\n\n```python\n@log\ndef my_new_function(\n    item: str | UUID,\n    workspace: Optional[str | UUID] = None,\n    option: str = \"default\",\n) -> pd.DataFrame:\n    \"\"\"\n    Short description of what the function does.\n\n    Extended description with more details about the function's behavior,\n    use cases, and any important notes.\n\n    This is a wrapper function for the following API: `API Name \u003Chttps:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002F...>`_.\n\n    Service Principal Authentication is supported (see `here \u003Chttps:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-link-labs\u002Fblob\u002Fmain\u002Fnotebooks\u002FService%20Principal.ipynb>`_ for examples).\n\n    Parameters\n    ----------\n    item : str | uuid.UUID\n        The name or ID of the item.\n    workspace : str | uuid.UUID, default=None\n        The Fabric workspace name or ID.\n        Defaults to None which resolves to the workspace of the attached lakehouse\n        or if no lakehouse attached, resolves to the workspace of the notebook.\n    option : str, default=\"default\"\n        An option that controls function behavior.\n\n    Returns\n    -------\n    pandas.DataFrame\n        A pandas dataframe showing the results.\n        Columns include: 'Column1', 'Column2', 'Column3'.\n\n    Raises\n    ------\n    ValueError\n        If the item does not exist.\n    FabricHTTPException\n        If the API request fails.\n    \"\"\"\n\n    # Resolve workspace\n    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)\n\n    # Define result DataFrame structure\n    columns = {\n        \"Column1\": \"string\",\n        \"Column2\": \"string\",\n        \"Column3\": \"int\",\n    }\n    df = _create_dataframe(columns=columns)\n\n    # Make API call\n    responses = _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\",\n        uses_pagination=True,\n        client=\"fabric_sp\",\n    )\n\n    # Process responses\n    rows = []\n    for r in responses:\n        for item in r.get(\"value\", []):\n            rows.append({\n                \"Column1\": item.get(\"id\"),\n                \"Column2\": item.get(\"name\"),\n                \"Column3\": item.get(\"count\", 0),\n            })\n\n    if rows:\n        df = pd.DataFrame(rows)\n\n    return df\n```\n\n---\n\n## Step 3: Export the Function\n\n### From Module File\n\nAdd to the module's `__init__.py`:\n\n```python\n# src\u002Fsempy_labs\u002Fadmin\u002F__init__.py (example for admin submodule)\n\nfrom ._my_admin_function import my_new_function\n\n__all__ = [\n    ...,\n    \"my_new_function\",\n]\n```\n\n### From Main Package\n\nFor top-level functions, add to `src\u002Fsempy_labs\u002F__init__.py`:\n\n```python\nfrom ._my_feature import my_new_function\n\n__all__ = [\n    ...,\n    \"my_new_function\",\n]\n```\n\n---\n\n## Common Patterns\n\n### Functions That Modify Resources\n\n```python\n@log\ndef create_item(\n    name: str,\n    workspace: Optional[str | UUID] = None,\n) -> None:\n    \"\"\"\n    Creates a new item.\n    ...\n    \"\"\"\n    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)\n\n    payload = {\n        \"displayName\": name,\n    }\n\n    _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\",\n        method=\"post\",\n        payload=payload,\n        status_codes=[201, 202],\n        client=\"fabric_sp\",\n    )\n\n    print(\n        f\"{icons.green_dot} The '{name}' item has been successfully created \"\n        f\"in the '{workspace_name}' workspace.\"\n    )\n```\n\n### Functions That Delete Resources\n\n```python\n@log\ndef delete_item(\n    item: str | UUID,\n    workspace: Optional[str | UUID] = None,\n) -> None:\n    \"\"\"\n    Deletes an item.\n    ...\n    \"\"\"\n    (workspace_name, workspace_id) = resolve_workspace_name_and_id(workspace)\n    item_id = resolve_item_id(item=item, type=\"ItemType\", workspace=workspace_id)\n\n    _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\u002F{item_id}\",\n        method=\"delete\",\n        client=\"fabric_sp\",\n    )\n\n    print(\n        f\"{icons.green_dot} The item has been successfully deleted \"\n        f\"from the '{workspace_name}' workspace.\"\n    )\n```\n\n### Functions With Long-Running Operations\n\n```python\n@log\ndef long_running_operation(\n    item: str | UUID,\n    workspace: Optional[str | UUID] = None,\n) -> dict:\n    \"\"\"\n    Performs a long-running operation.\n    ...\n    \"\"\"\n    workspace_id = resolve_workspace_id(workspace)\n    item_id = resolve_item_id(item=item, type=\"ItemType\", workspace=workspace_id)\n\n    # lro_return_json handles polling for completion\n    result = _base_api(\n        request=f\"\u002Fv1\u002Fworkspaces\u002F{workspace_id}\u002Fitems\u002F{item_id}\u002Foperation\",\n        method=\"post\",\n        lro_return_json=True,\n        client=\"fabric_sp\",\n    )\n\n    return result\n```\n\n---\n\n## Step 4: Add Tests\n\nCreate tests for the new function:\n\n```python\n# tests\u002Ftest_my_feature.py\n\nimport pytest\nimport pandas as pd\n\n\ndef test_my_new_function_returns_dataframe():\n    \"\"\"Test that my_new_function returns a DataFrame.\"\"\"\n    from sempy_labs import my_new_function\n\n    # This might require mocking for unit tests\n    result = my_new_function()\n\n    assert isinstance(result, pd.DataFrame)\n\n\ndef test_my_new_function_with_workspace():\n    \"\"\"Test my_new_function with specific workspace.\"\"\"\n    from sempy_labs import my_new_function\n\n    result = my_new_function(workspace=\"Test Workspace\")\n\n    assert isinstance(result, pd.DataFrame)\n```\n\n---\n\n## Step 5: Document the Function\n\nEnsure the docstring follows numpydoc style:\n\n1. ✅ Short description (one line)\n2. ✅ Extended description (if needed)\n3. ✅ API reference link (for wrapper functions)\n4. ✅ Service Principal note (if supported)\n5. ✅ All parameters documented with types\n6. ✅ Return value documented\n7. ✅ Exceptions documented (if applicable)\n\n---\n\n## Checklist Before Committing\n\n- [ ] Function follows naming conventions (`list_`, `get_`, `create_`, etc.)\n- [ ] `@log` decorator is applied\n- [ ] Complete docstring with numpydoc style\n- [ ] Type hints for all parameters and return value\n- [ ] Uses standard helper functions (`_base_api`, `resolve_*`, etc.)\n- [ ] Function exported in `__init__.py`\n- [ ] Tests written for the new function\n- [ ] Code formatted with black\n- [ ] No linting errors\n- [ ] Documentation builds without warnings\n\n---\n\n## Example: Complete New Function\n\nSee [_workspaces.py](..\u002F..\u002Fsrc\u002Fsempy_labs\u002F_workspaces.py) for well-implemented examples:\n\n- `list_workspace_users` — List function returning DataFrame\n- `update_workspace_user` — Update function with parameters\n- `delete_user_from_workspace` — Delete function with confirmation message\n\n---\n\n## API Documentation Resources\n\nWhen wrapping REST APIs, reference the official documentation:\n\n| API | Documentation |\n|-----|---------------|\n| Fabric Core API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fcore\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fcore\u002F) |\n| Fabric Admin API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fadmin\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Ffabric\u002Fadmin\u002F) |\n| Power BI REST API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fpower-bi\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fpower-bi\u002F) |\n| Azure Management API | [https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fresources\u002F](https:\u002F\u002Flearn.microsoft.com\u002Frest\u002Fapi\u002Fresources\u002F) |\n",{"name":5,"description":7},"main","2ddf46109c1209ceffa88149f4631e7b2275f9a1",".claude\u002Fskills\u002Fadd-function",204,"https:\u002F\u002Fgithub.com\u002Fmicrosoft\u002Fsemantic-link-labs","https:\u002F\u002Fgithub.com\u002Fmicrosoft","https:\u002F\u002Fopensource.microsoft.com","Open source projects and samples from Microsoft","2026-11-13 04:54:42",{"source":8,"skillName":5,"commands":45,"prompt":55,"zipAvailable":56},[46,49,52],{"tool":47,"command":48},"npx","npx skills add microsoft\u002Fsemantic-link-labs --skill add-function",{"tool":50,"command":51},"bunx","bunx skills add microsoft\u002Fsemantic-link-labs --skill add-function",{"tool":53,"command":54},"pnpm","pnpm dlx skills add microsoft\u002Fsemantic-link-labs --skill 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