Datajunction
DataJunction/dj
Activate this skill whenever working with DataJunction (DJ) semantic layer.
A skill your agent uses to get context about Fabric Lakehouse and its features for software systems and AI-powered functions.
Install Fabric Lakehouse
$ npx skills add github/awesome-copilot --skill fabric-lakehouse -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install github/awesome-copilot fabric-lakehouse --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/fabric-lakehouse .claude/skills/fabric-lakehouse && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "fabric-lakehouse" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouse into .claude/skills/fabric-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fabric-lakehouse", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouseType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add github/awesome-copilot --skill fabric-lakehouse -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install github/awesome-copilot fabric-lakehouse --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/fabric-lakehouse .agents/skills/fabric-lakehouse && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "fabric-lakehouse" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouse into .agents/skills/fabric-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fabric-lakehouse", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill fabric-lakehouse -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install github/awesome-copilot fabric-lakehouse --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/fabric-lakehouse .cursor/skills/fabric-lakehouse && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "fabric-lakehouse" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouse into .cursor/skills/fabric-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fabric-lakehouse", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/github/awesome-copilot.git --path skills/fabric-lakehouse--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add github/awesome-copilot --skill fabric-lakehouse -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install github/awesome-copilot fabric-lakehouse --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/fabric-lakehouse .gemini/skills/fabric-lakehouse && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "fabric-lakehouse" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouse into .gemini/skills/fabric-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fabric-lakehouse", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install github/awesome-copilot fabric-lakehouseInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add github/awesome-copilot --skill fabric-lakehouse -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/fabric-lakehouse .github/skills/fabric-lakehouse && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "fabric-lakehouse" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouse into .github/skills/fabric-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fabric-lakehouse", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add github/awesome-copilot --skill fabric-lakehouse -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install github/awesome-copilot fabric-lakehouse --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/fabric-lakehouse .opencode/skills/fabric-lakehouse && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "fabric-lakehouse" agent skill from https://github.com/github/awesome-copilot/tree/main/skills/fabric-lakehouse into .opencode/skills/fabric-lakehouse/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "fabric-lakehouse", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
fabric-lakehouse- GitHub stars
- 40k
- Used in
- 1 other repo
- Token cost
- ~1.5k tokens
- SKILL.md length
- 817 words
- Files
- 3 (incl. references)
- Skills in repo
- 417
- Repo updated
- First seen
- Licence
- MIT
At a glance
A skill your agent uses to get context about Fabric Lakehouse and its features for software systems and AI-powered functions.
- Get context about Fabric Lakehouse and its features for software systems and AI-powered functions
- SKILL.md covers Core Concepts, Security, Lakehouse Shortcuts and Performance Optimization, plus 3 more sections
- Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
- Tasks that involve Data warehousing
What it does
Fabric Lakehouse is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files, including reference files (for example `references/getdata.md` and `references/pyspark.md`).
It sits in Databases, covering Data warehousing. It works with SQL. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.
When your agent uses it
- Get context about Fabric Lakehouse and its features for software systems and AI-powered functions
- Tasks that involve Data warehousing
Example prompts
- “/fabric-lakehouse”
What it can do on your machine
Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
No scripts in the folder and no shell commands in SKILL.md.
From the folder's file list and the shell code blocks in SKILL.md.
Network
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Fabric Lakehouse loads about 1.5k tokens when it runs, and up to ~2.9k if it reads all its reference files. Until then it costs about 92 tokens; SKILL.md has 817 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
SKILL.md
The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 817 words, ~1,525 tokens.
Download SKILL.mdSave it as
.claude/skills/fabric-lakehouse/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.- name
- fabric-lakehouse
- description
- Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. It offers descriptions of Lakehouse data components, organization with schemas and shortcuts, access control, and code examples. This skill supports users in designing, building, and optimizing Lakehouse solutions using best practices.
- metadata.author
- tedvilutis
- metadata.version
- 1.0
When to Use This Skill
Use this skill when you need to:
- Generate a document or explanation that includes definition and context about Fabric Lakehouse and its capabilities.
- Design, build, and optimize Lakehouse solutions using best practices.
- Understand the core concepts and components of a Lakehouse in Microsoft Fabric.
- Learn how to manage tabular and non-tabular data within a Lakehouse.
Fabric Lakehouse
Core Concepts
What is a Lakehouse?
Lakehouse in Microsoft Fabric is an item that gives users a place to store their tabular data (like tables) and non-tabular data (like files). It combines the flexibility of a data lake with the management capabilities of a data warehouse. It provides:
- Unified storage in OneLake for structured and unstructured data
- Delta Lake format for ACID transactions, versioning, and time travel
- SQL analytics endpoint for T-SQL queries
- Semantic model for Power BI integration
- Support for other table formats like CSV, Parquet
- Support for any file formats
- Tools for table optimization and data management
Key Components
- Delta Tables: Managed tables with ACID compliance and schema enforcement
- Files: Unstructured/semi-structured data in the Files section
- SQL Endpoint: Auto-generated read-only SQL interface for querying
- Shortcuts: Virtual links to external/internal data without copying
- Fabric Materialized Views: Pre-computed tables for fast query performance
Tabular data in a Lakehouse
Tabular data in a form of tables are stored under "Tables" folder. Main format for tables in Lakehouse is Delta. Lakehouse can store tabular data in other formats like CSV or Parquet, these formats are only available for Spark querying. Tables can be internal, when data is stored under "Tables" folder, or external, when only reference to a table is stored under "Tables" folder but the data itself is stored in a referenced location. Tables are referenced through Shortcuts, which can be internal (pointing to another location in Fabric) or external (pointing to data stored outside of Fabric).
Schemas for tables in a Lakehouse
When creating a lakehouse, users can choose to enable schemas. Schemas are used to organize Lakehouse tables. Schemas are implemented as folders under the "Tables" folder and store tables inside of those folders. The default schema is "dbo" and it can't be deleted or renamed. All other schemas are optional and can be created, renamed, or deleted. Users can reference a schema located in another lakehouse using a Schema Shortcut, thereby referencing all tables in the destination schema with a single shortcut.
Files in a Lakehouse
Files are stored under "Files" folder. Users can create folders and subfolders to organize their files. Any file format can be stored in Lakehouse.
Fabric Materialized Views
Set of pre-computed tables that are automatically updated based on a schedule. They provide fast query performance for complex aggregations and joins. Materialized views are defined using PySpark or Spark SQL and stored in an associated Notebook.
Spark Views
Logical tables defined by a SQL query. They do not store data but provide a virtual layer for querying. Views are defined using Spark SQL and stored in Lakehouse next to Tables.
Show full SKILL.md (319 more words)Show less
Security
Item access or control plane security
Users can have workspace roles (Admin, Member, Contributor, Viewer) that provide different levels of access to Lakehouse and its contents. Users can also get access permission using sharing capabilities of Lakehouse.
Data access or OneLake Security
For data access use OneLake security model, which is based on Microsoft Entra ID (formerly Azure Active Directory) and role-based access control (RBAC). Lakehouse data is stored in OneLake, so access to data is controlled through OneLake permissions. In addition to object-level permissions, Lakehouse also supports column-level and row-level security for tables, allowing fine-grained control over who can see specific columns or rows in a table.
Lakehouse Shortcuts
Shortcuts create virtual links to data without copying:
Types of Shortcuts
- Internal: Link to other Fabric Lakehouses/tables, cross-workspace data sharing
- ADLS Gen2: Link to ADLS Gen2 containers in Azure
- Amazon S3: AWS S3 buckets, cross-cloud data access
- Dataverse: Microsoft Dataverse, business application data
- Google Cloud Storage: GCS buckets, cross-cloud data access
Performance Optimization
V-Order Optimization
For faster data read with semantic model enable V-Order optimization on Delta tables. This presorts data in a way that improves query performance for common access patterns.
Table Optimization
Tables can also be optimized using the OPTIMIZE command, which compacts small files into larger ones and can also apply Z-ordering to improve query performance on specific columns. Regular optimization helps maintain performance as data is ingested and updated over time. The Vacuum command can be used to clean up old files and free up storage space, especially after updates and deletes.
Lineage
The Lakehouse item supports lineage, which allows users to track the origin and transformations of data. Lineage information is automatically captured for tables and files in Lakehouse, showing how data flows from source to destination. This helps with debugging, auditing, and understanding data dependencies.
PySpark Code Examples
See PySpark code for details.
Getting data into Lakehouse
See Get data for details.
© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 2 other files (references) in skills/fabric-lakehouse of github/awesome-copilot.
- SKILL.md
- references/getdata.md
- references/pyspark.md
Open the folder on GitHubat commit 727ff2e
Used in 1 other repository
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Fabric Lakehouse next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Fabric Lakehouse this skillgithub/awesome-copilot | 40k | 1 repos | ~1.5k | Automated safety check: Pass | MIT | |
| DatajunctionDataJunction/dj | 161 | — | ~2k | Automated safety check: Pass | MIT | |
| SQL Prodavila7/claude-code-templates | 32k | 8 repos | ~1.9k | Automated safety check: Pass | MIT | |
| Sap Dataspheresecondsky/sap-skills | 460 | — | ~6k | Automated safety check: Pass | GPL-3.0 | |
| Query Validationnimrodfisher/data-analytics-skills | 465 | — | ~551 | Automated safety check: Pass | MIT | |
| Model Explorationhashgraph-online/awesome-codex-plugins | 1.2k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 |
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Questions about Fabric Lakehouse
What does Fabric Lakehouse do?
A skill your agent uses to get context about Fabric Lakehouse and its features for software systems and AI-powered functions. Fabric Lakehouse is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. Use this skill to get context about Fabric Lakehouse and its features for software systems and AI-powered functions.
When should I use Fabric Lakehouse?
Fabric Lakehouse fits situations like: get context about Fabric Lakehouse and its features for software systems and AI-powered functions; tasks that involve Data warehousing.
How do I install Fabric Lakehouse in Claude Code?
Run `npx skills add github/awesome-copilot --skill fabric-lakehouse -a claude-code`. Or copy the skill folder (skills/fabric-lakehouse in github/awesome-copilot) into .claude/skills/fabric-lakehouse in your project. Claude Code loads it when a task matches its description.
How do I install Fabric Lakehouse in Codex?
Run `npx skills add github/awesome-copilot --skill fabric-lakehouse -a codex`. Or copy the skill folder (skills/fabric-lakehouse in github/awesome-copilot) into .agents/skills/fabric-lakehouse in your project. Codex loads it when a task matches its description.
Can I use Fabric Lakehouse in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill fabric-lakehouse -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/fabric-lakehouse, .gemini/skills/fabric-lakehouse, .github/skills/fabric-lakehouse and .opencode/skills/fabric-lakehouse in your project.
What does Fabric Lakehouse need to run?
SKILL.md names no scripts, command-line tools or credentials: Fabric Lakehouse is instructions for the agent only.
Does Fabric Lakehouse access the network?
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Is Fabric Lakehouse safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
What licence does Fabric Lakehouse use?
Fabric Lakehouse is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Fabric Lakehouse use?
About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 1.3k tokens, read only when the agent opens those files.
What are the alternatives to Fabric Lakehouse?
Skills that share tags, products or a category with Fabric Lakehouse: Datajunction (DataJunction/dj, 161 stars), SQL Pro (davila7/claude-code-templates, 32k stars), Sap Datasphere (secondsky/sap-skills, 460 stars) and Query Validation (nimrodfisher/data-analytics-skills, 465 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Fabric Lakehouse?
github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.
Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.