Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add boshi-xixixi/TraeSkill --skill fabric-lakehousegit clone --depth 1 https://github.com/boshi-xixixi/TraeSkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/boshi-xixixi/traeskill/fabric-lakehouse)<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/fabric-lakehouse"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/fabric-lakehouse/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/boshi-xixixi/traeskill/fabric-lakehouse"><img src="https://agentmods.dev/badge/skills/boshi-xixixi/traeskill/fabric-lakehouse.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00066 | $0.01253 |
| Opus 5 | $0.00033 | $0.00626 |
| Sonnet 5 | $0.00013 | $0.00251 |
| Haiku 4.5 | $0.00007 | $0.00125 |
Grade A, and why
fabric-lakehouse scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- fabric-lakehouse — 97% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 107 lines · 66 tokens per session scan A 987b1c1e29a6
fabric-lakehouse is a skill published in the GitHub repository boshi-xixixi/TraeSkill (263 stars, last pushed 4mo ago), licensed MIT. It adds 66 tokens to every session and 1,253 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
azure-machine-learning
Expert knowledge for Azure Machine Learning development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when running AutoML jobs, Prompt Flow/RAG, online endpoints, feature stores…
azure-data-factory
Expert knowledge for Azure Data Factory development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when building ADF pipelines, mapping data flows, SSIS IR/SHIR, CI/CD deployments…
azure-databricks
Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakeflow pipelines, Genie/AI Runtime, Delta…
azure-hdinsight
Expert knowledge for Azure HDInsight development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when working with HDInsight Spark/Hive/Kafka/HBase clusters, Ambari/Oozie pipelines…
azure-synapse-analytics
Expert knowledge for Azure Synapse Analytics development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Synapse SQL pools, Spark pools, Synapse Link, PolyBase ELT, or…
azure-cognitive-search
Expert knowledge for Azure AI Search development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when designing indexes, skillsets, indexers, vector/semantic search, or secure data…