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Search Consumption Cli

OrganizationPopular
microsoft
search-consumption-cli

Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.

Overview

Publishermicrosoft
Repositoryskills-for-fabric
Skill namesearch-consumption-cli
Stars
1.2K
Forks
354
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by microsoft on GitHub. Read the source before you install it.

Installation

Install the Search Consumption Cli AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/microsoft/skills-for-fabric/tree/main/plugins/fabric-skills/skills/search-consumption-cli
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/microsoft/skills-for-fabric.git /tmp/skills-for-fabric
mkdir -p .claude/skills
cp -r /tmp/skills-for-fabric/plugins/fabric-skills/skills/search-consumption-cli .claude/skills/search-consumption-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Search Consumption Cli in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Search Consumption Cli on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Search Consumption Cli is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Telemetry — MANDATORY. Every api.fabric.microsoft.com call must carry x-ms-fabric-skill: search-consumption-cli (az rest: --headers "x-ms-fabric-skill=search-consumption-cli"), including every LRO poll, fabric_lro and retry. Snippets omit it — add it anyway.

CRITICAL NOTES

  1. The Catalog Search API finds items, not workspaces. To find a workspace by name, use GET /v1/workspaces (see COMMON-CLI.md § Resolve Workspace Properties by Name).
  2. The search text matches against item display name, description, and workspace name.
  3. Dataflow (Gen1) and Dataflow (Gen2) are not supported.

Catalog Search — CLI Skill

Prerequisite Knowledge

Table of Contents

TaskReferenceNotes
Search for an ItemSKILL.md § Search for an ItemBy name, description, or workspace name
List All Items of a TypeSKILL.md § List All Items of a TypeEmpty search + type filter
PaginationSKILL.md § PaginationContinuation token pattern
Agentic WorkflowSKILL.md § Agentic Workflow
ExamplesSKILL.md § Examples
Gotchas and TroubleshootingSKILL.md § Gotchas and Troubleshooting

Must/Prefer/Avoid

MUST DO

PREFER

  • Catalog Search over list-and-filter — single cross-workspace call, no need to resolve workspace first.
  • Type filters — narrow results with "filter": "Type eq 'Lakehouse'" to reduce noise.
  • Empty search with type filter — to list all items of a type across workspaces.
  • jq for extracting IDs from the response — cleaner than JMESPath for nested hierarchy.workspace.

AVOID

  • Searching for workspaces — the Catalog Search API returns items, not workspaces. Use GET /v1/workspaces instead (see COMMON-CLI.md § Resolve Workspace Properties by Name).
  • Querying source data after the workspace/item is known — route to the workload-specific consumption skill (sqldw-cli, spark-cli, eventhouse-cli, or fabriciq) instead of Catalog Search.
  • Inventing filter syntax — only eq, ne, or, and parentheses are supported.
  • Assuming all item types are supported — Dataflow (Gen1) and Dataflow (Gen2) are not returned yet.

Search for an Item

bash
cat > /tmp/body.json << 'EOF'
{"search": "SalesLakehouse", "filter": "Type eq 'Lakehouse'", "pageSize": 10}
EOF
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json

The search text matches against item display name, description and workspace name. Type filtering is optional. The response includes id, type, displayName, description, and hierarchy.workspace (with id and displayName) for each match.

Extract item and workspace IDs

bash
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json \
  --query "value[0].{itemId:id, workspaceId:hierarchy.workspace.id, name:displayName}" \
  --output json

Filter Examples

GoalFilter
Only lakehousesType eq 'Lakehouse'
Reports or semantic modelsType eq 'Report' or Type eq 'SemanticModel'
Exclude notebooksType ne 'Notebook'

For the full list of supported item types, see the Catalog Search API reference.


List All Items of a Type

Use an empty search string with a type filter (pageSize max is 1000):

bash
cat > /tmp/body.json << 'EOF'
{"search": "", "filter": "Type eq 'Lakehouse'", "pageSize": 100}
EOF
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json

Pagination

If the response includes a non-null continuationToken, pass it in the next request:

bash
cat > /tmp/body.json << 'EOF'
{"search": "", "filter": "Type eq 'Lakehouse'", "pageSize": 100, "continuationToken": "<token>"}
EOF
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json

Continue until continuationToken is null.


Agentic Workflow

  1. Ask — user provides an item name, type, or description keywords.
  2. Search — call Catalog Search with the user's input and optional type filter.
  3. Disambiguate — if multiple matches, present results (name, type, workspace) and ask the user to pick.
  4. Return — provide the search results, include the item id and hierarchy.workspace.id for downstream use.

Examples

Find a specific report

bash
cat > /tmp/body.json << 'EOF'
{"search": "Monthly Sales Revenue", "filter": "Type eq 'Report'", "pageSize": 10}
EOF
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json \
  --query "value[].{name:displayName, type:type, workspace:hierarchy.workspace.displayName}" \
  --output table

List all semantic models across workspaces

bash
cat > /tmp/body.json << 'EOF'
{"search": "", "filter": "Type eq 'SemanticModel'", "pageSize": 1000}
EOF
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json

Save search results to file

bash
cat > /tmp/body.json << 'EOF'
{"search": "", "filter": "Type eq 'Lakehouse'", "pageSize": 1000}
EOF
az rest --method post \
  --resource "https://api.fabric.microsoft.com" \
  --url "https://api.fabric.microsoft.com/v1/catalog/search" \
  --body @/tmp/body.json \
  --query "value[].{name:displayName, type:type, workspace:hierarchy.workspace.displayName, id:id}" \
  --output json > /tmp/search_results.json

Gotchas and Troubleshooting

SymptomCauseFix
401 UnauthorizedWrong token audience or expired sessionVerify --resource "https://api.fabric.microsoft.com". Run az login.
InvalidPageSizepageSize outside 1–1000Use a value between 1 and 1000.
InvalidFilterBad filter syntaxOnly eq, ne, or, and parentheses. Don't mix eq with and, or ne with or. Don't mix eq and ne in the same filter.
TypeNotFoundUnrecognized item type in filterCheck spelling (case-sensitive). See API reference for valid types.
FilterTooManyValuesFilter has more than 500 valuesReduce the number of type values in the filter.
InvalidRequestMissing request bodyEnsure --body points to a valid JSON file.
Empty results for known itemItem type not supportedDataflow Gen1/Gen2 are excluded. Use GET /v1/workspaces/{id}/items instead.
New item not foundCatalog index propagation delayIndexing lag is variable and not yet near-real-time — usually minutes, but not guaranteed. A just-created item may not appear in search results yet; verify it exists via GET /v1/workspaces/{id}/items instead.
Too many resultsSearch text too broadAdd a type filter or use more specific search text.

Frequently asked questions

What does the Search Consumption Cli AI skill do?

Finds Fabric items across every workspace with the Catalog Search API when the workspace is unknown, locating an item by display name, inventorying one item type tenant-wide, and returning the workspace and item IDs later calls need. Dataflow Gen1 and Gen2 items are not supported. Use only to locate items; once found, query them with the owning item skill such as sqldw-cli, spark-cli, eventhouse-cli or fabriciq.

Why use Search Consumption Cli on TypingMind?

Because you install it once and use it with any model. Search Consumption Cli is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Search Consumption Cli in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/skills-for-fabric/tree/main/plugins/fabric-skills/skills/search-consumption-cli. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Search Consumption Cli?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Search Consumption Cli?

As many as you like. As long as a model supports skills, you can use Search Consumption Cli with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Search Consumption Cli AI skill free?

Yes. It is published on GitHub by microsoft under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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