القائمة
Microsoft Fabric workspace management, governance, REST API patterns, and medallion architecture implementation
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/fabioc-aloha/Alex_Plug_In --skill microsoft-fabricيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
عرض SKILL.md
استنادا إلى تصنيف SOC المهني
| name | microsoft-fabric |
| description | Microsoft Fabric workspace management, governance, REST API patterns, and medallion architecture implementation |
| tier | standard |
| applyTo | **/*fabric*,**/*lakehouse*,**/*medallion* |
| user-invokable | false |
⚠️ Staleness Watch (Last validated: Feb 2026 — REST API v1): Microsoft Fabric ships major features monthly. Monitor the Fabric release notes for new item types, API surface changes, and Git integration improvements. The REST API base URL (
api.fabric.microsoft.com/v1) is stable but new endpoints are added regularly.
Expert knowledge for Microsoft Fabric workspace management, governance, and documentation. Covers REST API patterns, medallion architecture implementation, permission compliance pipelines, and automated workspace inspection.
data-project/
├── .github/
│ ├── copilot-instructions.md # Data project context
│ └── prompts/
│ └── pipeline-review.prompt.md
├── docs/
│ ├── DATA-PLAN.md # Project scope, objectives
│ ├── DATA-DICTIONARY.md # Field definitions
│ ├── LINEAGE.md # Data flow documentation
│ └── architecture/
│ └── medallion-design.md
├── pipelines/
│ ├── bronze/ # Raw ingestion
│ │ └── [source]-ingest.py
│ ├── silver/ # Cleansing, standardization
│ │ └── [entity]-transform.py
│ └── gold/ # Business logic, aggregations
│ └── [domain]-model.py
├── notebooks/
│ ├── exploration/ # EDA notebooks
│ └── prototypes/ # Pipeline prototypes
├── schemas/
│ ├── bronze/
│ ├── silver/
│ └── gold/
├── tests/
│ ├── unit/
│ └── data-quality/
├── config/
│ ├── connections.yaml
│ └── environments/
│ ├── dev.yaml
│ └── prod.yaml
└── README.md
# Data Plan: [Project Name]
## Objective
[What business problem does this data pipeline solve?]
## Data Sources
| Source | Type | Frequency | Volume |
|--------|------|-----------|--------|
| [System A] | [API/Database/File] | [Daily/Hourly/Real-time] | [~X records/day] |
## Medallion Architecture
| Layer | Description | Key Transformations |
|-------|-------------|---------------------|
| Bronze | Raw ingestion from [sources] | Minimal: schema enforcement, timestamps |
| Silver | Cleansed, standardized | Deduplication, type casting, validation |
| Gold | Business-ready | Joins, aggregations, business logic |
## Data Quality Rules
| Rule | Layer | Implementation |
|------|-------|----------------|
| No nulls in [field] | Silver | Validation check |
| Referential integrity | Gold | Foreign key check |
## Success Criteria
- [ ] All sources ingesting to Bronze
- [ ] Silver quality checks passing
- [ ] Gold tables serving [consumers]
- [ ] Documentation complete
# Data Dictionary: [Dataset Name]
## Tables
### [table_name]
**Layer**: Gold
**Description**: [What this table represents]
**Update Frequency**: [Daily/Hourly]
**Primary Key**: [field]
| Field | Type | Description | Nullable | Example |
|-------|------|-------------|----------|---------|
| id | STRING | Unique identifier | No | "abc123" |
| created_at | TIMESTAMP | Record creation time | No | 2026-02-07T10:00:00Z |
| [field] | [TYPE] | [Description] | [Yes/No] | [Example] |
**Business Rules**:
- [Rule 1]
- [Rule 2]
**Lineage**: Bronze.[source_table] → Silver.[cleaned_table] → Gold.[table_name]
# [Project Name] — Data Engineering Context
## Overview
[What data problem this project solves]
## Current Phase
- [ ] Source analysis
- [ ] Bronze layer
- [ ] Silver layer
- [ ] Gold layer
- [ ] Documentation
## Key Files
- Data plan: docs/DATA-PLAN.md
- Data dictionary: docs/DATA-DICTIONARY.md
- Lineage: docs/LINEAGE.md
## Alex Guidance
- **Platform**: [Fabric/Databricks/Snowflake/etc.]
- **Language**: [Python/SQL/Spark]
- Follow medallion architecture patterns
- Document data quality rules
- Include idempotency in pipeline designs
## Conventions
- Table naming: [layer]_[domain]_[entity]
- Pipeline naming: [source]_to_[target]_[frequency]
- Use incremental loads where possible
## Don't
- Don't modify Bronze layer (append-only)
- Don't hardcode connection strings
- Don't skip data quality checks
Endpoint Structure:
Base URL: https://api.fabric.microsoft.com/v1
Workspaces: /workspaces/{workspaceId}
Items: /workspaces/{workspaceId}/items?type={type}
Definitions: /workspaces/{workspaceId}/{type}/{itemId}/getDefinition
Async Operation Pattern:
Location headerLocation URL until status = Succeeded/result endpoint for payloadAuthentication:
# Fabric API token
$fabricToken = az account get-access-token --resource https://api.fabric.microsoft.com --query accessToken -o tsv
# Unity Catalog token (for schema-enabled lakehouses)
$storageToken = az account get-access-token --resource https://storage.azure.com --query accessToken -o tsv
Layer Design:
| Layer | Purpose | Lakehouse Type | Schema |
|---|---|---|---|
| Bronze | Raw ingestion | Standard | Default |
| Silver | Cleansed data | Standard | Default |
| Gold | Domain models | Schema-enabled | dbo, CxPulse |
Table Distribution Pattern (Fishbowl Example):
Key Insight: Gold layer benefits from schema-enabled lakehouses for domain organization while Bronze/Silver remain standard for ingestion flexibility.
7-Step CPM Pattern:
graph LR
A["📥 Copy<br/>Contacts"] --> B["🔐 MSWide<br/>Perm API"]
B --> C["🔐 TXN<br/>Perm API"]
C --> D["✅ CPERM<br/>Validation"]
D --> E["🌍 Country<br/>Enrichment"]
E --> F["💧 Data<br/>Hydration"]
F --> G["📤 Load<br/>Lakehouse"]
style A fill:#ddf4ff,color:#0550ae,stroke:#80ccff
style B fill:#d8b9ff,color:#6639ba,stroke:#bf8aff
style C fill:#d8b9ff,color:#6639ba,stroke:#bf8aff
style D fill:#d3f5db,color:#1a7f37,stroke:#6fdd8b
style E fill:#fff8c5,color:#9a6700,stroke:#d4a72c
style F fill:#ddf4ff,color:#0550ae,stroke:#80ccff
style G fill:#d3f5db,color:#1a7f37,stroke:#6fdd8b
linkStyle default stroke:#57606a,stroke-width:1.5px
Permission Tables:
CPERM_OptOut - Marketing opt-outsUS_FAR_List - US government exclusionsUS_CAPSL_List - CAPSL complianceSpamHaus_List - Known bad actorsSchema-Enabled Lakehouse Access:
# Catalog URL pattern
catalog_url = f"https://onelake.table.fabric.microsoft.com/delta/{workspace_id}/{lakehouse_id}"
# List schemas
GET /schemas
# List tables in schema
GET /schemas/{schema_name}/tables
Domain Organization:
Workspace Scanner Pattern:
# Use Invoke-WebRequest for header access (not Invoke-RestMethod)
$response = Invoke-WebRequest -Uri $uri -Headers $headers -Method Post
# Handle 202 Accepted
if ($response.StatusCode -eq 202) {
$operationUrl = $response.Headers["Location"][0]
# Poll for completion
do {
Start-Sleep -Seconds 2
$status = Invoke-RestMethod -Uri $operationUrl -Headers $headers
} while ($status.status -ne "Succeeded")
# Get result
$result = Invoke-RestMethod -Uri "$operationUrl/result" -Headers $headers
}
Skill created from Fishbowl workspace inspection session, February 2026
Create and maintain ASCII visual dashboards for project tracking with parallel lane progress bars
Store and manage voice samples for TTS cloning — portable, version-controlled audio references
Clear documentation through visual excellence