Agent Skills¶

fabric-dw ships three Claude Code Agent Skills that orchestrate multi-step administration workflows on top of the MCP server.

Skill	Trigger phrases	What it does
`query-optimizer`	"optimize this query", "why is my query slow", "analyze execution plan", "missing statistics", "fix clustering"	Captures the estimated execution plan, inspects query insights history, audits statistics, examines clustering, and proposes or applies optimizations
`warehouse-performance`	"investigate warehouse performance", "why is my warehouse slow", "tune sql pools", "find expensive queries", "find the most frequent queries", "check sql pool pressure", "enable result set caching"	Surfaces long-running and frequent queries plus SQL pool insights, audits statistics health, checks result-set caching, reviews and tunes SQL pool configuration, and produces a prioritized findings report (all mutating actions gated on confirmation)
`dbt-setup`	"set up dbt", "scaffold a dbt project", "create dbt profile", "generate dbt sources"	Generates a complete dbt-fabric scaffold (profiles, project, column-rich sources, requirements) and writes it to disk

All three skills require the fabric-dw MCP server to be configured in your AI client. Installing the plugin also registers the MCP server automatically, so no separate MCP setup is needed for either client below.

Install via Claude Code Plugin¶

The recommended installation path for Claude Code users is the fabric-dw plugin. Skills are installed from GitHub without cloning the repo and are namespaced as /fabric-dw:query-optimizer, /fabric-dw:warehouse-performance, and /fabric-dw:dbt-setup.

/plugin marketplace add sdebruyn/fabric-dw-mcp-cli
/plugin install fabric-dw@fabric-dw

After installing, the skills are available as slash commands:

/fabric-dw:query-optimizer
/fabric-dw:warehouse-performance
/fabric-dw:dbt-setup

Alternatively, add the marketplace and plugin directly to your .claude/settings.json (project-scoped) or ~/.claude/settings.json (personal):

{
  "extraKnownMarketplaces": {
    "fabric-dw": {
      "source": { "source": "github", "repo": "sdebruyn/fabric-dw-mcp-cli" }
    }
  },
  "enabledPlugins": {
    "fabric-dw@fabric-dw": true
  }
}

Version caching

The plugin version is pinned in plugins/fabric-dw/.claude-plugin/plugin.json. If a new release changes skill content, bump the plugin version to force cached copies to update.

Install via GitHub Copilot CLI¶

The same three skills and the bundled MCP server install in one command in GitHub Copilot CLI:

/plugin marketplace add sdebruyn/fabric-dw-mcp-cli
/plugin install fabric-dw@fabric-dw

Install as raw SKILL.md files¶

If you are using a different AI assistant, the Claude API directly, or any tool that supports the SKILL.md format but not the Claude Code plugin mechanism, download or reference the raw skill files:

query-optimizer: https://raw.githubusercontent.com/sdebruyn/fabric-dw-mcp-cli/main/plugins/fabric-dw/skills/query-optimizer/SKILL.md
warehouse-performance: https://raw.githubusercontent.com/sdebruyn/fabric-dw-mcp-cli/main/plugins/fabric-dw/skills/warehouse-performance/SKILL.md
dbt-setup: https://raw.githubusercontent.com/sdebruyn/fabric-dw-mcp-cli/main/plugins/fabric-dw/skills/dbt-setup/SKILL.md

Place each file at one of the Claude Code personal or project skill paths:

Path	Scope
`~/.claude/skills/<name>/SKILL.md`	Personal - applies to all projects
`.claude/skills/<name>/SKILL.md`	Project-scoped - committed to repo

For example, to install query-optimizer at project scope:

mkdir -p .claude/skills/query-optimizer
curl -fsSL https://raw.githubusercontent.com/sdebruyn/fabric-dw-mcp-cli/main/plugins/fabric-dw/skills/query-optimizer/SKILL.md \
  -o .claude/skills/query-optimizer/SKILL.md

Skill reference¶

query-optimizer¶

Performs a structured query performance analysis on a Fabric Data Warehouse:

Captures the estimated execution plan via get_query_plan (no query execution)
Parses the SHOWPLAN_XML for costly operators, residual predicates, and implicit type conversions
Inspects query insights history via list_request_history and list_long_running_queries
Audits statistics per referenced table with list_statistics and show_statistics
Fetches column metadata (types, nullability, identity, computed) via get_table_columns
Reads current data-clustering columns via get_cluster_columns (DW-only; skipped for SQL Analytics Endpoints)
Reports findings and recommendations; optionally applies statistics changes (create_statistics / update_statistics) and re-clustering (set_cluster_columns) after explicit user confirmation

To visualize the execution plan, use the CLI (pass the warehouse name as the first positional argument and the query via -q):

fdw sql plan <warehouse> -q "<query>" --format html -o plan.html   # writes self-contained HTML file; open plan.html in any browser
fdw sql plan <workspace>/<warehouse> -q "<query>" --format svg -o plan.svg   # renders SVG via system dot binary (requires Graphviz)

--format html requires -o/--output and writes a file - it does not open the browser automatically.

warehouse-performance¶

Runs a warehouse-wide performance investigation on a Fabric Data Warehouse (the warehouse-wide counterpart to query-optimizer, which diagnoses a single query):

Finds query hotspots via queries long-running and queries frequent (server-ordered; "top N" is --limit N, no --order-by), plus resource-pressure events via sql-pools insights; drills down with queries history / queries sessions (MCP list_long_running_queries, list_frequent_queries, list_sql_pool_insights, list_request_history, list_session_history). Degrades gracefully when Query Insights is unavailable or permission-denied (needs Contributor+)
Audits statistics health with statistics list and statistics show --histogram, flagging missing or likely-stale statistics as a heuristic (reads work on both surfaces; DDL is DWH-only)
Checks result-set caching via settings show, and recommends settings result-set-caching on when frequent identical queries justify it (toggle is DWH-only and mutating - kept distinct from the local cache clear lookup cache and from cache cooldown)
Reviews and tunes SQL pool configuration via sql-pools status/list/show, with actionable create/update levers (--max-percent, --optimize-for-reads, application-name classifier) against the default 50/50 baseline (workspace-scoped, workspace admin)
Synthesizes a prioritized findings report with each cost driver's query_hash so a specific query can be handed to query-optimizer

Every mutating action (result-set-caching toggle, statistics DDL, sql-pools create/update/delete/enable/disable) is gated behind explicit user confirmation. Two capabilities are documented as observe-only with no API: statement-type routing for SQL pools (#596, use application-name classifiers instead) and result-set cache cooldown (#595).

dbt-setup¶

Bootstraps a dbt-fabric project for a Fabric Data Warehouse. For the human, copy-pasteable CLI walkthrough of the same provision → inspect → scaffold → verify flow, see the Set up a dbt environment guide - this skill is the AI-assistant-driven equivalent.

Resolves the warehouse connection string via get_warehouse
Lists schemas and tables with list_schemas and list_tables to confirm connectivity
Generates all scaffold files with generate_dbt_profile using with_sources=true: the tool performs a bulk column fetch and emits a models/staging/_sources.yml with columns: blocks (name and formatted data_type) for every table, ready for dbt contract enforcement
Writes profiles.yml, dbt_project.yml, models/staging/_sources.yml, requirements.txt, and .gitignore to disk; confirms before overwriting existing files
Provides next steps: pip install -r requirements.txt, dbt debug, dbt run