check-updates
Check for skills-for-fabric marketplace updates at session start. Compares local version against GitHub releases and shows changelog if updates are available. Use when the user wants to: (1) check for skill updates, (2) see what's new in skills-for-fabric, (3) verify current version. Triggers: "check for updates", "am I up to date", "what version", "update skills", "show changelog".
Best use case
check-updates is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Check for skills-for-fabric marketplace updates at session start. Compares local version against GitHub releases and shows changelog if updates are available. Use when the user wants to: (1) check for skill updates, (2) see what's new in skills-for-fabric, (3) verify current version. Triggers: "check for updates", "am I up to date", "what version", "update skills", "show changelog".
Teams using check-updates should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/check-updates/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How check-updates Compares
| Feature / Agent | check-updates | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Check for skills-for-fabric marketplace updates at session start. Compares local version against GitHub releases and shows changelog if updates are available. Use when the user wants to: (1) check for skill updates, (2) see what's new in skills-for-fabric, (3) verify current version. Triggers: "check for updates", "am I up to date", "what version", "update skills", "show changelog".
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Check for Updates
This skill checks for updates to the skills-for-fabric marketplace at the start of each session.
## When to Run
Run this check **once per week** when any skills-for-fabric skill is first invoked. Skip if already checked within the last 7 days.
## Session State
The update check marker is stored in a **persistent, user-level directory** shared across all sessions and all plugins in the Fabric Skills marketplace:
```text
~/.config/fabric-collection/last-update-check.json
```
This file contains a JSON object mapping plugin names to the **UTC date** (YYYY-MM-DD) of their last update check:
```json
{
"skills-for-fabric": "2026-02-17",
"another-plugin": "2026-02-16"
}
```
Before checking, read `~/.config/fabric-collection/last-update-check.json`:
- If the file exists and the entry for the current plugin is within the last **7 days** (compared to the current **UTC date**), skip the check.
- If the file is missing, the plugin entry is absent, or the date is more than 7 days old (compared to the current **UTC date**), run the update check.
> **IMPORTANT — use UTC consistently**: Always use the current UTC date when saving and comparing the last-update-check timestamp. Do not use the local system timezone, as it varies across environments and can cause the check to run too often or be skipped. In shell, use `date -u +%Y-%m-%d` (Linux/macOS) or `(Get-Date).ToUniversalTime().ToString("yyyy-MM-dd")` (PowerShell).
> **Note**: Create the `~/.config/fabric-collection/` directory if it does not exist. On Windows, use `$env:USERPROFILE\.config\fabric-collection\`.
## Update Check Procedure
### Step 1: Get Local Version
Read the `version` field from the local `package.json` file in the skills-for-fabric installation directory.
### Step 2: Determine Repository Owner and Name
Read the `repository` field from the local `plugin.json` (or `package.json`) to extract the GitHub owner and repo name. Parse the URL to get `owner` and `repo`:
```text
plugin.json → "repository": "https://github.com/<owner>/<repo>"
package.json → "repository.url": "https://github.com/<owner>/<repo>.git"
```
> **CRITICAL**: Use the owner string **exactly as it appears** in the URL. Do NOT alter, normalize, or "correct" the owner name (e.g., do NOT replace underscores with hyphens). The owner `bocrivat_microsoft` uses an **underscore** — this is intentional and correct.
### Step 3: Fetch Latest Release
Use the available tools in your environment to get the latest version. **Try methods in strict order — only fall back to the next method if the previous one fails or is unavailable.**
> **IMPORTANT**: Methods A and B work with both public and private repositories. Method C only works with public repos. Always attempt A or B first.
**Method A — Git CLI (preferred)**
If the skills-for-fabric directory is a Git clone, fetch the remote `package.json` without pulling:
```bash
git fetch origin main --quiet
git show origin/main:package.json
```
Extract the `version` field from the JSON output. This method is the most reliable because it uses the already-configured remote URL and authentication, and avoids any owner/repo name parsing.
**Method B — GitHub MCP tools (preferred for agentic environments)**
If you have access to GitHub MCP server tools (e.g., `get_file_contents`), use them to read the remote `package.json`. Use the owner and repo extracted in Step 2 **exactly as parsed** (do not modify the strings):
```text
get_file_contents(owner: "<owner>", repo: "<repo>", path: "package.json")
```
For this repository, the correct call is:
```text
get_file_contents(owner: "bocrivat_microsoft", repo: "skills-for-fabric", path: "package.json")
```
Extract the `version` field from the response. This method works with private repositories because MCP tools use authenticated GitHub access.
**Method C — GitHub REST API (fallback only, public repos)**
> ⚠️ **Only use this method if Methods A and B both fail or are unavailable.** This method does not work with private repositories.
If the repository is public, make a GET request using the owner/repo from Step 2:
```text
GET https://api.github.com/repos/<owner>/<repo>/releases/latest
```
Extract the `tag_name` field (e.g., `v0.2.0`) and remove the `v` prefix.
> **Note**: This method returns 404 for private repositories. If you receive a 404 error, do NOT assume the repository doesn't exist — retry with Method A or B.
### Step 4: Compare Versions
Compare the local version with the remote version using semantic versioning:
- If remote > local: Update available
- If remote <= local: Up to date
### Step 5: Display Results
#### If Up to Date
Show a brief confirmation and proceed:
```text
✅ skills-for-fabric v0.1.0 is up to date.
```
#### If Update Available
Show detailed information:
```text
╔══════════════════════════════════════════════════════════════════╗
║ 🔄 skills-for-fabric Update Available ║
║ ║
║ Current: v0.1.0 → Latest: v0.2.0 ║
╚══════════════════════════════════════════════════════════════════╝
## What's New in v0.2.0
[Display relevant CHANGELOG.md entries here]
## Update Commands
Choose the update method based on how you installed skills-for-fabric:
### GitHub Copilot CLI
/plugin update skills-for-fabric@fabric-collection
### Manual (Git clone)
cd /path/to/skills-for-fabric
git pull
./install.ps1 # Windows
./install.sh # macOS/Linux
─────────────────────────────────────────────────────────────────
Would you like to update now? (The current skill will still work)
```
### Step 6: Set Update Marker
After completing the check (regardless of result), update `~/.config/fabric-collection/last-update-check.json` with today's **UTC date** (YYYY-MM-DD) for the current plugin. Create the directory and file if they don't exist. Preserve entries for other plugins already in the file.
## Must
- Check for updates only once per week (based on UTC calendar date, not session lifetime or local timezone)
- Always proceed with the requested skill after the check (non-blocking)
- Handle network errors gracefully (show warning, continue with skill)
- Display the CHANGELOG.md content for versions between current and latest
## Prefer
- Use Git CLI (Method A) or GitHub MCP tools (Method B) for version checking — these work with private repos
- Fall back to the public GitHub REST API (Method C) **only** if Methods A and B both fail
- Show a concise summary rather than overwhelming detail
- Cache the check result in `~/.config/fabric-collection/last-update-check.json`
- Provide copy-pasteable update commands
## Avoid
- Blocking the user from using skills if update check fails
- Checking on every skill invocation (once per week is sufficient)
- Attempting Method C (public API) before trying Methods A or B
- Relying solely on unauthenticated public API calls (will fail for private repos)
- Auto-updating without user consent
## Error Handling
If the update check fails (network error, API rate limit, etc.):
```text
⚠️ Could not check for skills-for-fabric updates (network error).
Continuing with current version (v0.1.0).
Run '/skill check-updates' manually to retry.
```
## Manual Invocation
Users can manually check for updates at any time:
- GitHub Copilot CLI: `/skill check-updates`
- Other tools: Invoke the check-updates skill directly
## Reference
- **GitHub Repository**: https://github.com/microsoft/skills-for-fabric
- **Releases**: https://github.com/microsoft/skills-for-fabric/releases
- **CHANGELOG**: See `CHANGELOG.md` in repository rootRelated Skills
quality-check
Run local quality checks on skills-for-fabric before committing. Validates all skills in the skills/ folder for structural compliance, semantic disambiguation, broken references, and content quality. Use before submitting a PR to catch issues early. Triggers: "check my skills", "run quality check", "validate skills", "pre-commit check", "lint skills".
best-practices-check
Verify skills-for-fabric against Microsoft Fabric best practices from the internet. Searches for current best practices, compares them against skill content, and identifies gaps or improvements. Use when the user wants to: (1) validate a skill covers industry best practices, (2) find missing guidance, (3) improve skill quality with current recommendations. Triggers: "check best practices", "validate best practices", "best practices for", "compare against best practices", "skill coverage".
sqldw-consumption-cli
Execute read-only T-SQL queries against Fabric Data Warehouse, Lakehouse SQL Endpoints, and Mirrored Databases via CLI. Default skill for any lakehouse data query (row counts, SELECT, filtering, aggregation) unless the user explicitly requests PySpark or Spark DataFrames. Use when the user wants to: (1) query warehouse/lakehouse data, (2) count rows or explore lakehouse tables, (3) discover schemas/columns, (4) generate T-SQL scripts, (5) monitor SQL performance, (6) export results to CSV/JSON. Triggers: "warehouse", "SQL query", "T-SQL", "query warehouse", "show warehouse tables", "show lakehouse tables", "query lakehouse", "lakehouse table", "how many rows", "count rows", "SQL endpoint", "describe warehouse schema", "generate T-SQL script", "warehouse performance", "export SQL data", "connect to warehouse", "lakehouse data", "explore lakehouse".
sqldw-authoring-cli
Execute authoring T-SQL (DDL, DML, data ingestion, transactions, schema changes) against Microsoft Fabric Data Warehouse and SQL endpoints from agentic CLI environments. Use when the user wants to: (1) create/alter/drop tables from terminal, (2) insert/update/delete/merge data via CLI, (3) run COPY INTO or OPENROWSET ingestion, (4) manage transactions or stored procedures, (5) perform schema evolution, (6) use time travel or snapshots, (7) generate ETL/ELT shell scripts, (8) create views/functions/procedures on Lakehouse SQLEP. Triggers: "create table in warehouse", "insert data via T-SQL", "load from ADLS", "COPY INTO", "run ETL with T-SQL", "alter warehouse table", "upsert with T-SQL", "merge into warehouse", "create T-SQL procedure", "warehouse time travel", "recover deleted warehouse data", "create warehouse schema", "deploy warehouse", "transaction conflict", "snapshot isolation error".
spark-consumption-cli
Analyze lakehouse data interactively using Fabric Livy sessions and PySpark/Spark SQL for advanced analytics, DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data. Use when the user explicitly asks for PySpark, Spark DataFrames, Livy sessions, or Python-based analysis — NOT for simple SQL queries. Triggers: "PySpark", "Spark SQL", "analyze with PySpark", "Spark DataFrame", "Livy session", "lakehouse with Python", "PySpark analysis", "PySpark data quality", "Delta time-travel with Spark".
spark-authoring-cli
Develop Microsoft Fabric Spark/data engineering workflows with intelligent routing to specialized resources. Provides core workspace/lakehouse management and routes to: data engineering patterns, development workflow, or infrastructure orchestration. Use when the user wants to: (1) manage Fabric workspaces and resources, (2) develop notebooks and PySpark applications, (3) design data pipelines and orchestration, (4) provision infrastructure as code. Triggers: "develop notebook", "data engineering", "workspace setup", "pipeline design", "infrastructure provisioning", "Delta Lake patterns", "Spark development", "lakehouse configuration", "organize lakehouse tables", "create Livy session", "notebook deployment".
powerbi-consumption-cli
The ONLY supported path for read-only Microsoft Fabric Power BI semantic model (formerly "Power BI dataset") query interactions. Execute DAX queries via the MCP server ExecuteQuery tool to: (1) discover semantic model metadata (tables, columns, measures, relationships, hierarchies, etc.) and their properties, (2) retrieve data from a semantic model. Triggers: "DAX query", "semantic model metadata", "list semantic model tables", "run EVALUATE", "get measure expression".
powerbi-authoring-cli
Create, manage, and deploy Power BI semantic models inside Microsoft Fabric workspaces via `az rest` CLI against Fabric and Power BI REST APIs. Use when the user wants to: (1) create a semantic model from TMDL definition files, (2) retrieve or download semantic model definitions, (3) update a semantic model definition with modified TMDL, (4) trigger or manage dataset refresh operations, (5) configure data sources, parameters, or permissions, (6) deploy semantic models between pipeline stages. Covers Fabric Items API (CRUD) and Power BI Datasets API (refresh, data sources, permissions). For read-only DAX queries, use `powerbi-consumption-cli`. For fine-grained modeling changes, route to `powerbi-modeling-mcp`. Triggers: "create semantic model", "upload TMDL", "download semantic model TMDL", "refresh dataset", "semantic model deployment pipeline", "dataset permissions", "list dataset users", "semantic model authoring".
eventhouse-consumption-cli
Run KQL queries against Fabric Eventhouse for real-time intelligence and time-series analytics using `az rest` against the Kusto REST API. Covers KQL operators (where, summarize, join, render), Eventhouse schema discovery (.show tables), time-series patterns with bin(), and ingestion monitoring. Use when the user wants to: 1. Run read-only KQL queries against an Eventhouse or KQL Database 2. Discover Eventhouse table schema and metadata 3. Analyse real-time or time-series data with KQL operators 4. Monitor ingestion health and active KQL queries 5. Export KQL results to JSON Triggers: "kql query", "kusto query", "eventhouse query", "kql database", "real-time intelligence", "time-series kql", "query eventhouse", "explore eventhouse", "show tables kql"
eventhouse-authoring-cli
Execute KQL management commands (table management, ingestion, policies, functions, materialized views) against Fabric Eventhouse and KQL Databases via CLI. Use when the user wants to: 1. Create or alter KQL tables, columns, or functions 2. Ingest data into an Eventhouse (inline, from storage, streaming) 3. Configure retention, caching, or partitioning policies 4. Create or manage materialized views and update policies 5. Manage data mappings for ingestion pipelines 6. Deploy KQL schema via scripts Triggers: "create kql table", "kql ingestion", "ingest into eventhouse", "kql function", "materialized view", "kql retention policy", "eventhouse schema", "kql authoring", "create eventhouse table", "kql mapping"
e2e-medallion-architecture
Implement end-to-end Medallion Architecture (Bronze/Silver/Gold) lakehouse patterns in Microsoft Fabric using PySpark, Delta Lake, and Fabric Pipelines. Use when the user wants to: (1) design a Bronze/Silver/Gold data lakehouse, (2) set up multi-layer workspace with lakehouses for each tier, (3) build ingestion-to-analytics pipelines with data quality enforcement, (4) optimize Spark configurations per medallion layer, (5) orchestrate Bronze-to-Silver-to-Gold flows via notebooks. Triggers: "medallion architecture", "bronze silver gold", "lakehouse layers", "e2e data pipeline", "end-to-end lakehouse", "data lakehouse pattern", "multi-layer lakehouse", "build medallion", "setup medallion".
skill-test
Manage the skills-for-fabric evaluation framework: add eval plans for new or existing skills, list available tests and their results, generate eval datasets, review metrics, and check test coverage. Directs test execution to the tests/ folder. Triggers: "add tests", "add evals", "list tests", "show eval results", "run tests", "generate eval data", "eval metrics", "test coverage", "missing tests". "show tests"