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Lineage Analysis

CommunityPopular
data-goblin
lineage-analysis

Trace relationships between semantic models and downstream reports across Fabric workspaces. Automatically invoke when the user asks to "find downstream reports", "show report lineage", "impact analysis", "what depends on this dataset", "cross-workspace lineage", "which reports are connected", "get model dependencies", or mentions model-to-report dependency tracing.

Overview

Publisherdata-goblin
Repositorypower-bi-agentic-development
Skill namelineage-analysis
Stars
1K
Forks
149
Bundled files
1
LicenseGPL-3.0
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.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by data-goblin on GitHub. Read the source before you install it.

Installation

Install the Lineage Analysis 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/data-goblin/power-bi-agentic-development/tree/main/plugins/semantic-models/skills/lineage-analysis
  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/data-goblin/power-bi-agentic-development.git /tmp/power-bi-agentic-development
mkdir -p .claude/skills
cp -r /tmp/power-bi-agentic-development/plugins/semantic-models/skills/lineage-analysis .claude/skills/lineage-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lineage Analysis 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 Lineage Analysis 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 Lineage Analysis 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.

Lineage Analysis

Trace downstream dependencies from a semantic model to all connected reports across the tenant. No admin permissions required -- workspace contributor access is sufficient.

When to Use

  • Before modifying or deleting a semantic model, to understand impact
  • Auditing which reports are connected to a model and where they live
  • Identifying orphaned or test reports connected to production models
  • Cross-workspace dependency mapping

Downstream Reports

Run scripts/get-downstream-reports.py to find all reports bound to a semantic model.

bash
# By workspace and model name
python3 scripts/get-downstream-reports.py "Workspace Name" "Model Name"

# By dataset GUID directly
python3 scripts/get-downstream-reports.py --dataset-id <guid>

# JSON output for further processing
python3 scripts/get-downstream-reports.py "Workspace" "Model" --json

Requirements: azure-identity, requests (pip install azure-identity requests). Authenticated via DefaultAzureCredential (works with az login, managed identity, or environment variables).

How it works: Lists all workspaces the user can access, then queries each workspace's reports in parallel (8 workers) checking datasetId. Groups results by workspace. Typically completes in under 10 seconds for ~100 workspaces.

Permissions: Workspace contributor or higher on any workspace to be scanned. Reports in workspaces without access will not appear. For full tenant coverage, use the --dataset-id flag with a tenant admin token and the admin/reports API instead.

Limitations

Reports are not the only consumers. A semantic model can also be consumed by:

  • Analyze in Excel workbooks (.xlsx live connections)
  • Composite models (other semantic models chaining via DirectQuery)
  • Explorations (ad-hoc visual explorations in the Power BI service)
  • Fabric notebooks (connecting via Spark or sempy)
  • Fabric data agents
  • Paginated reports (.rdl)
  • Dataflows referencing the model
  • Third-party tools connecting via XMLA

The script only discovers Power BI reports. For full dependency mapping including these other item types, use the Fabric lineage APIs (fab api "admin/groups/{id}/lineage") or the lineage view in the Power BI service UI.

Not appropriate for many models at once. The script scans all accessible workspaces per invocation. Running it in a loop over dozens of semantic models will generate excessive API calls and risk throttling. For bulk inventory across many models, use the admin scan API (fab api "admin/workspaces/getInfo") when tenant-admin access is available, or the cross-workspace catalog via fab find ... -P type=SemanticModel for a quick non-admin list. Neither alternative resolves report-to-model dependency edges; fab find and the OneLake catalog do not expose lineage. For that you still need either a per-workspace scan (this script's approach) or the official Power BI lineage admin API at fab api "admin/groups/{ws-id}/lineage".

Interpreting Results

FieldMeaning
Report format PBIRModern format, editable as JSON
Report format PBIRLegacyLegacy format, needs conversion to PBIR for direct editing
Reports in unexpected workspacesMay indicate copies, forks, or thin reports pointing at a shared model
Many downstream reportsHigh-impact model -- changes require coordination

Related Skills

  • semantic-model -- Design, build, refresh, and review models (quality, memory, DAX, design)
  • refreshing-semantic-model -- Trigger and monitor model refreshes
  • fabric-cli (fabric-cli plugin) -- Workspace and item management via fab CLI

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Lineage Analysis AI skill do?

Trace relationships between semantic models and downstream reports across Fabric workspaces. Automatically invoke when the user asks to "find downstream reports", "show report lineage", "impact analysis", "what depends on this dataset", "cross-workspace lineage", "which reports are connected", "get model dependencies", or mentions model-to-report dependency tracing.

Why use Lineage Analysis on TypingMind?

Because you install it once and use it with any model. Lineage Analysis 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 Lineage Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/data-goblin/power-bi-agentic-development/tree/main/plugins/semantic-models/skills/lineage-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Lineage Analysis?

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 Lineage Analysis?

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

Is the Lineage Analysis AI skill free?

Yes. It is published on GitHub by data-goblin under the GPL-3.0 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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