Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/santoshkanthety/powerbi-agentnpx agentmods add skills/santoshkanthety/powerbi-agent/powerbi-lineage-analysisWrote 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/santoshkanthety/powerbi-agent/powerbi-lineage-analysis)<a href="https://agentmods.dev/skills/santoshkanthety/powerbi-agent/powerbi-lineage-analysis"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-lineage-analysis/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/santoshkanthety/powerbi-agent/powerbi-lineage-analysis"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-lineage-analysis.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 | $0.00072 | $0.00776 |
| Opus 5 | $0.00036 | $0.00388 |
| Sonnet 5 | $0.00014 | $0.00155 |
| Haiku 4.5 | $0.00007 | $0.00078 |
Grade A, and why
powerbi-lineage-analysis 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 11d 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.
How it starts
The opening of the file, as written. The whole thing — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
# 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
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.
- 11d ago First seen · 69 lines · 72 tokens per session scan A 4c9054b5f48a
powerbi-lineage-analysis is a skill published in the GitHub repository santoshkanthety/powerbi-agent (2 stars, last pushed 12d ago), licensed MIT. It adds 72 tokens to every session and 776 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
seshat-bi
Route a BI project through Seshat BI's governed seven-stage readiness flow. Use when a user asks to inspect a retail source, initialize a Seshat project, find the truthful next action, validate readiness evidence, or stop at the correct human approval gate.
Data Retention Schedule Review
Use when reviewing a draft or existing data retention schedule to inventory data categories against stated purposes, collect retention-period facts, flag legal-hold interactions, and surface orphaned-data and vendor-coverage gaps for attorney review.
powerbi-workflows
Route guarded Power BI work -- design, native report authoring, semantic-model operations, published queries, QA, bounded formatting, and PBIP adoption -- to the correct Seshat or official Microsoft surface under Seshat BI's gates.
dbt-workflows
Route dbt intent to Seshat's governed shadow workflow or the official dbt Labs competence and execution owner, without bypassing readiness or evidence.
dagster-workflows
Route Dagster intent to Seshat's governed medallion workflow or the official Dagster competence owner without bypassing readiness, approvals, or evidence.
pbi-mcp-doctor
Use when a user asks whether or how to wire Microsoft's official Power BI MCP servers into a Seshat BI workspace: run the read-only environment doctor, map a task to the governed Power BI surface (including the official report-authoring skill), generate a safe read-only config template, or run the mocked read-only…