菜单
Fabric CLI OneLake file operations — upload, download, copy, move, list, create, and delete files and folders in OneLake storage. Use when working with files inside Lakehouse Files/ or Tables/ paths.
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
Practitioner-level dbt patterns for Microsoft Fabric. Covers materialization decisions, incremental model gotchas, snapshot strategies, selector/tag patterns, ref() chain rules, and Fabric-specific T-SQL quirks. Use when writing, reviewing, or debugging dbt models on Fabric. Also use when setting up dbt_project.yml configuration or CI/CD pipelines for dbt-fabric projects.
Define semantic models and MetricFlow metrics in dbt on Microsoft Fabric. Use when creating semantic model YAML, defining entities, dimensions, and measures, or configuring metric types. Also use when choosing between denormalized marts and semantic layer queries.
Implement SCD Type 2 snapshots in dbt on Microsoft Fabric. Use when building snapshot models, choosing between timestamp and check strategies, or handling Fabric-specific datetime2 quirks. Also use when tracking slowly changing dimensions or debugging snapshot drift.
Configure Elementary anomaly detection for dbt on Microsoft Fabric. Use when placing data quality tests by medallion layer, setting up volume or column anomalies, or configuring timestamp_column. Also use when debugging silent test failures or choosing severity levels.
Build dlt REST API pipelines to ADLS Gen2 and OneLake. Use when configuring RESTAPIConfig, handling pagination, or setting schema contracts for REST sources. Also use when landing REST API data into Fabric lakehouses or debugging schema evolution.
Use when modeling ASC 606 revenue recognition in dbt on Microsoft Fabric, including performance obligations, transaction price allocation, deferred revenue, contract modifications, or silver/gold revenue reporting layers.
| name | fabric-cli-onelake |
| description | Fabric CLI OneLake file operations — upload, download, copy, move, list, create, and delete files and folders in OneLake storage. Use when working with files inside Lakehouse Files/ or Tables/ paths. |
| allowed-tools | Bash(uv run *), Bash(fab *), Bash(cat *), Bash(grep *), Read, Write, Edit, Glob |
| user-invocable | false |
| metadata | {"author":"accelerate-data"} |
Complete reference for reading and writing files in OneLake storage via fab CLI.
uv run --env-file .env fab <command>
OneLake storage lives inside items (Lakehouse, Warehouse, etc.) with two root sections:
ws1.Workspace/lh1.Lakehouse/Files/ ← Unstructured file storage
ws1.Workspace/lh1.Lakehouse/Tables/ ← Delta tables (managed)
ws1.Workspace/wh1.Warehouse/Tables/dbo/ ← Warehouse tables (schema-qualified)
Path examples:
ws1.Workspace/lh1.Lakehouse/Files/data.csv
ws1.Workspace/lh1.Lakehouse/Files/raw-data/2024/january/sales.parquet
ws1.Workspace/lh1.Lakehouse/Tables/dbo/customer_profiles
ws1.Workspace/wh1.Warehouse/Tables/dbo/orders
ws1.Workspace/sem1.SemanticModel/Tables
# Change directory into OneLake
uv run --env-file .env fab cd ws1.Workspace/lh1.Lakehouse/Files
uv run --env-file .env fab cd ws1.Workspace/lh1.Lakehouse/Tables
uv run --env-file .env fab cd ws1.Workspace/lh1.Lakehouse/Files/raw-data/2024
# List files in a lakehouse
uv run --env-file .env fab ls ws1.Workspace/lh1.Lakehouse/Files
# List tables
uv run --env-file .env fab ls ws1.Workspace/lh1.Lakehouse/Tables
# List warehouse tables (schema-qualified)
uv run --env-file .env fab ls ws1.Workspace/wh1.Warehouse/Tables/dbo
# List semantic model tables
uv run --env-file .env fab ls ws1.Workspace/sem1.SemanticModel/Tables
# Check if a folder exists
uv run --env-file .env fab exists ws1.Workspace/lh1.Lakehouse/Files/data-processing
# Check if a file exists
uv run --env-file .env fab exists ws1.Workspace/lh1.Lakehouse/Files/datasets/sales.parquet
# Full metadata
uv run --env-file .env fab get ws1.Workspace/lh1.Lakehouse/Files/datasets
# Query specific field
uv run --env-file .env fab get ws1.Workspace/lh1.Lakehouse/Files/datasets -q paths[0]
# Full JSON
uv run --env-file .env fab get ws1.Workspace/lh1.Lakehouse/Files/datasets -q .
# Save metadata to file
uv run --env-file .env fab get ws1.Workspace/lh1.Lakehouse/Files/datasets -q paths[0] -o /tmp/metadata
# Upload a single file
uv run --env-file .env fab cp /tmp/local-data/dataset.csv ws1.Workspace/lh1.Lakehouse/Files/upload/
# Upload to a specific path
uv run --env-file .env fab cp ./local/data.csv ws1.Workspace/lh1.Lakehouse/Files/data.csv
# Download a file
uv run --env-file .env fab cp ws1.Workspace/lh1.Lakehouse/Files/csv/fab.csv /tmp/mydir
# Download to specific path
uv run --env-file .env fab cp ws1.Workspace/lh1.Lakehouse/Files/data.csv ./local/
# Copy between paths in same lakehouse
uv run --env-file .env fab cp ws1.Workspace/lh1.Lakehouse/Files/csv/data.csv ws1.Workspace/lh1.Lakehouse/Files/dest/
# Copy between workspaces
uv run --env-file .env fab cp ws1.Workspace/lh1.Lakehouse/Files/data.csv ws2.Workspace/lh2.Lakehouse/Files/data.csv
# Force overwrite
uv run --env-file .env fab cp ./data.csv ws1.Workspace/lh1.Lakehouse/Files/data.csv -f
# Recursive copy (all files in a folder)
uv run --env-file .env fab cp ws1.Workspace/lh1.Lakehouse/Files/source/ ws1.Workspace/lh1.Lakehouse/Files/dest/ -r
Copy flags: -f force (overwrite), -r recursive
# Create a folder
uv run --env-file .env fab create ws1.Workspace/lh1.Lakehouse/Files/data-processing
# Create nested folders
uv run --env-file .env fab create ws1.Workspace/lh1.Lakehouse/Files/raw-data/2024/january
# Delete a folder (with confirmation)
uv run --env-file .env fab rm ws1.Workspace/lh1.Lakehouse/Files/old-data
# Force delete a file (no confirmation)
uv run --env-file .env fab rm ws1.Workspace/lh1.Lakehouse/Files/temp/temporary-file.csv -f
Shortcuts provide virtual access to external data without copying:
# Create internal OneLake shortcut
uv run --env-file .env fab ln ws1.Workspace/lh1.Lakehouse/Files/shared_data.Shortcut --type oneLake --target ../../shared.Workspace/source.Lakehouse/Files/datasets
# Create external Azure Data Lake shortcut
uv run --env-file .env fab ln ws1.Workspace/lh1.Lakehouse/Tables/external_sales.Shortcut --type adlsGen2 -i '{"location": "https://storageaccount.dfs.core.windows.net/container", "subpath": "/data/sales", "connectionId": "<guid>"}'
# Check shortcut exists
uv run --env-file .env fab exists ws1.Workspace/lh1.Lakehouse/Files/external_data.Shortcut
# Get shortcut target
uv run --env-file .env fab get ws1.Workspace/lh1.Lakehouse/Files/external_data.Shortcut -q target
# Rename shortcut
uv run --env-file .env fab set ws1.Workspace/lh1.Lakehouse/Files/updated_name.Shortcut -q name -i external_data
# Delete shortcut
uv run --env-file .env fab rm ws1.Workspace/lh1.Lakehouse/Files/old_data.Shortcut -f
Shortcuts work in both /Files and /Tables sections.
# Upload each file in a local directory
for f in /tmp/data/*.csv; do
uv run --env-file .env fab cp "$f" ws1.Workspace/lh1.Lakehouse/Files/uploads/
done
# Download to temp
uv run --env-file .env fab cp ws1.Workspace/lh1.Lakehouse/Files/reports/summary.csv /tmp/
# Inspect locally
cat /tmp/summary.csv | head -20
# Upload
uv run --env-file .env fab cp ./data.parquet ws1.Workspace/lh1.Lakehouse/Files/data.parquet
# Verify it exists
uv run --env-file .env fab exists ws1.Workspace/lh1.Lakehouse/Files/data.parquet
fab cp to download files + Read tool to inspect content locally. Never use MCP download + base64 decode — it wastes context tokens. Never paste base64 strings in echo commands."ai_pro_wp.Workspace/ai_fork_lake.Lakehouse/Files/my folder/data.csv"| Issue | Fix |
|---|---|
ERROR_WORKSPACE_NOT_FOUND | Check workspace name and .Workspace extension |
| File not found after upload | Verify full path including /Files/ prefix |
| Cannot list Tables | Tables are managed by Spark/SQL — use fab table schema for details |
| Copy fails between workspaces | Ensure both workspaces exist and auth tokens have access |
| Shortcut creation fails | Check --type (oneLake, adlsGen2, s3, etc.) and connection ID |
| Permission denied | Verify FAB_TOKEN_ONELAKE is set in .env |