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-review-semantic-modelWrote 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-review-semantic-model)<a href="https://agentmods.dev/skills/santoshkanthety/powerbi-agent/powerbi-review-semantic-model"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-review-semantic-model/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-review-semantic-model"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-review-semantic-model.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 | $0.00082 | $0.01541 |
| Opus 5 | $0.00041 | $0.00771 |
| Sonnet 5 | $0.00016 | $0.00308 |
| Haiku 4.5 | $0.00008 | $0.00154 |
Grade A, and why
powerbi-review-semantic-model 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 12d 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 — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Warning: This skill is incomplete and still in progress, but may provide value already as-is -- Kurt
Reviewing Semantic Models
Structured evaluation of Power BI semantic models against quality, performance, and best practice standards. Produces actionable findings with prioritized recommendations.
Review Workflow
Step 0: Gather Context
Before analyzing TMDL, collect metadata and understand the business context.
Run the model info script:
python3 scripts/get_model_info.py -w <workspace-id> -m <model-id>
This returns: storage mode, model size, connected reports, deployment pipeline, endorsement status, sensitivity label, data sources, refresh schedule, last refresh, and capacity SKU.
Ask the user:
- What business process does this model represent?
- Who are the primary consumers? (report developers, analysts, executives, AI/Copilot users?)
- Are they the developer of both the model and its reports, or only one?
- Is the model in development, testing, or production?
- Where should findings be documented? (scratchpad, agent-docs, wiki, etc.)
Understanding the business context is critical. A model for 3 analysts has different requirements than one consumed by Copilot across the organization. The audit categories and their severity shift based on this context.
Step 1: Analyze Model Structure
Inspect the model definition to evaluate its structure. The approach depends on available tooling -- use whatever is available to read the model's tables, columns, measures, relationships, and expressions. Do not prescribe a specific tool; common options include Tabular Editor, the te-cli, fab export to TMDL, or programmatic access via APIs.
Step 2: Audit Categories
Evaluate findings across categories, ordered by severity:
Critical
- Bidirectional relationships (ambiguity risk)
- Circular dependencies between tables
- Missing data types on columns
- Tables without relationships (orphaned)
Memory and Size
- High-cardinality columns with large dictionaries (GUIDs, transaction IDs, composite keys)
- IsAvailableInMdx enabled on hidden or high-cardinality columns (wastes memory on attribute hierarchies unused by DAX; disable for columns not consumed via Analyze in Excel / MDX)
- Unsplit DateTime columns (near-unique precision creating massive dictionaries)
- Auto Date/Time tables (hidden LocalDateTable_* bloating memory)
- Inappropriate data types (Double for currency, String for numeric)
- Calculated columns that could be measures
- Unused columns or tables (no references in measures or visuals or other downstream items)
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.
- 12d ago First seen · 137 lines · 82 tokens per session scan A 238ac7e94f6e
powerbi-review-semantic-model is a skill published in the GitHub repository santoshkanthety/powerbi-agent (2 stars, last pushed 13d ago), licensed MIT. It adds 82 tokens to every session and 1,541 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…