Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add santoshkanthety/powerbi-agent --skill powerbi-medallion-architecturegit clone --depth 1 https://github.com/santoshkanthety/powerbi-agentWrote 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-medallion-architecture)<a href="https://agentmods.dev/skills/santoshkanthety/powerbi-agent/powerbi-medallion-architecture"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-medallion-architecture/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-medallion-architecture"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-medallion-architecture.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 | $0.00058 | $0.00940 |
| Opus 5 | $0.00029 | $0.00470 |
| Sonnet 5 | $0.00012 | $0.00188 |
| Haiku 4.5 | $0.00006 | $0.00094 |
Grade A, and why
powerbi-medallion-architecture 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Medallion Architecture Design & Setup
Trigger
Activate when the user mentions: medallion, bronze, silver, gold, lakehouse, data lake, data architecture, layer design, raw layer, curated layer, semantic layer, OneLake
What You Know
You embody the experience of a 20+ year data architect who has designed and delivered enterprise medallion architectures on Microsoft Fabric and Azure Data Lake. You know every trade-off, every failure mode, and every shortcut worth taking.
The Three-Layer Medallion Pattern
OneLake
├── Bronze/ (Raw Ingestion)
│ ├── crm/
│ ├── erp/
│ └── files/
├── Silver/ (Cleaned & Conformed)
│ ├── dim_customer/
│ ├── dim_product/
│ └── fact_sales/
└── Gold/ (Business-Ready)
├── semantic_model/
├── finance_reporting/
└── marketing_analytics/
Bronze Layer — Raw Zone
- Never transform, never filter — store exactly as received
- Partition by ingestion date:
year=YYYY/month=MM/day=DD/ - Store as Parquet or Delta — never CSV (no schema enforcement)
- Preserve source system identifiers and timestamps
- Add metadata columns:
_ingested_at,_source_system,_file_name - Retain for minimum 7 years (audit trail, replays)
Silver Layer — Conformed Zone
- Apply data quality rules: nulls, duplicates, referential integrity
- Standardize data types (e.g., all dates as UTC timestamps)
- Resolve naming conflicts across source systems
- Implement slowly changing dimensions (SCD Type 1, 2, 6)
- Add surrogate keys — never expose natural keys to Gold
- Validate with Great Expectations or Fabric Data Quality
Gold Layer — Business Zone
- Optimized for analytical consumption
- Star Schema preferred for Power BI (see star-schema skill)
- Pre-aggregated rollup tables for large fact tables (>100M rows)
- Semantic layer aligned to business vocabulary
- RLS policies applied at this layer
- Delta tables with V-Order optimization for Power BI DirectLake
Fabric-Specific Guidance
DirectLake Performance — Gold Layer Rules
- Enable V-Order on all Delta tables:
OPTIMIZE table VORDER - Target file size: 128MB–1GB per Parquet file
- Partition only when cardinality > 10M rows and filter patterns are clear
- Avoid over-partitioning — it kills DirectLake scan performance
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 · 100 lines · 0 tokens per session scan A fef5eae34710
powerbi-medallion-architecture is a skill published in the GitHub repository santoshkanthety/powerbi-agent (2 stars, last pushed 12d ago), licensed MIT. It adds 58 tokens to every session and 940 once invoked, about $0.0003 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
genomic-intelligence
Predict regulatory features, gene structure, and expression directly from DNA sequence using Genomic Intelligence's hosted transformer DNA language models — no local GPU or model weights. Six tasks over a REST API and a hosted MCP server (keyless public demo): promoter regions, splice donor/acceptor sites, enhancer…
bulk-rnaseq
End-to-end bulk RNA-seq orchestrator — takes raw FASTQ reads through QC and trimming (FastQC, fastp/Trim Galore), alignment and quantification (STAR, Salmon, featureCounts), assembles a gene-level counts matrix, then hands off to differential expression (pydeseq2), pathway/GSEA enrichment (pathway-enrichment), and…
umap-learn
Use UMAP-learn for nonlinear dimensionality reduction, 2D/3D embeddings, clustering preprocessing, supervised or semi-supervised UMAP, DensMAP, AlignedUMAP, and Parametric UMAP workflows.
optimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS…
shap
Explain and audit machine-learning predictions with SHAP. Use for selecting SHAP explainers and maskers, computing and validating feature attributions, handling multi-output explanations, and producing local or global SHAP visualizations.
torchdrug
Build and troubleshoot TorchDrug 0.2.1 workflows for molecular graphs, property prediction, self-supervised pretraining, molecule generation, retrosynthesis, protein representation learning, and knowledge graph reasoning. Use when code imports torchdrug or needs its datasets, models, tasks, or Engine.