powerbi-data-transformation

powerbi-data-transformation is a skill for Claude Code, Codex from santoshkanthety/powerbi-agent. It costs 79 tokens per session (1,719 once invoked), scanned A, original, MIT.

A set of data-cleaning patterns for combining tables, changing value types, creating keys, and joining related data. ETL and ELT are ways of preparing data before or while loading it into another system.

In plain words
What is it for?
Use it to append or union tables, align their schemas, convert types, create surrogate or hash keys, and perform small joins in tools such as Power Query or Spark.
Why use it?
It addresses mismatched column names, date formats, data types, missing values, and key formats that can prevent datasets from working together.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to append or union tables, align their schemas, convert types, create surrogate or hash keys, and perform small joins in tools such as Power Query or Spark.

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Install with agentmods
npx agentmods add skills/santoshkanthety/powerbi-agent/powerbi-data-transformation
Install

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.

Any agent
npx skills add santoshkanthety/powerbi-agent --skill powerbi-data-transformation
Clone the repo
git clone --depth 1 https://github.com/santoshkanthety/powerbi-agent

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for powerbi-data-transformation

README.md
[![agentmods](https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-data-transformation/github.svg)](https://agentmods.dev/skills/santoshkanthety/powerbi-agent/powerbi-data-transformation)
Your own site
<a href="https://agentmods.dev/skills/santoshkanthety/powerbi-agent/powerbi-data-transformation"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-data-transformation/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.

agentmods 80×15 button for powerbi-data-transformation

Your own site · 80×15
<a href="https://agentmods.dev/skills/santoshkanthety/powerbi-agent/powerbi-data-transformation"><img src="https://agentmods.dev/badge/skills/santoshkanthety/powerbi-agent/powerbi-data-transformation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 79 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,719 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00079 $0.01719
Opus 5 $0.00039 $0.00860
Sonnet 5 $0.00016 $0.00344
Haiku 4.5 $0.00008 $0.00172

Measured 11d ago against content hash a6e515203ee6, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

powerbi-data-transformation 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.

skills/powerbi-data-transformation/SKILL.md · 196 lines

How it starts

The opening of the file, as written. The whole thing — 196 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Skill: Data Transformation — Union, Append, Type Conversion & Light Joins

Trigger

Activate when the user mentions: union, append, stack tables, combine datasets, convert data types, cast, type mismatch, key generation, surrogate key, hash key, join key, light join, merge, schema alignment, homogenise, data harmonisation, Power Query union, Spark union, M append, combine queries

What You Know

You have harmonised data from dozens of source systems — different date formats, inconsistent key strategies, null handling, schema drift. You know every trick for making mismatched data play nicely, and you know when a "light join" is the right move vs a full dimensional join.

Unioning / Appending Data

Spark — Union Multiple Sources (with schema alignment)

from pyspark.sql.functions import col, lit, current_timestamp
from functools import reduce

def union_with_schema_alignment(dfs: list) -> "DataFrame":
    """Union DataFrames with different schemas — fills missing columns with null."""
    # Find superset of all columns
    all_cols = list({col for df in dfs for col in df.columns})

    aligned = []
    for df in dfs:
        missing = [c for c in all_cols if c not in df.columns]
        for c in missing:
            df = df.withColumn(c, lit(None))
        aligned.append(df.select(all_cols))

    return reduce(lambda a, b: a.union(b), aligned)


# Usage: combine CRM + ERP + CSV contacts into one Bronze table
crm_df   = spark.read.format("delta").load(".../raw_crm_contact")
erp_df   = spark.read.format("delta").load(".../raw_erp_contact")
csv_df   = spark.read.option("header", True).csv(".../files/contacts.csv")

# Tag each source before unioning
crm_df  = crm_df.withColumn("_source_system", lit("CRM"))
erp_df  = erp_df.withColumn("_source_system", lit("ERP"))
csv_df  = csv_df.withColumn("_source_system", lit("CSV"))

combined = union_with_schema_alignment([crm_df, erp_df, csv_df])

Power Query / M — Append Queries

// Append CRM and ERP contact tables
let
    CRM_Contacts  = Excel.Workbook(...){[Name="CRM"]}[Data],
    ERP_Contacts  = Csv.Document(...),
    // Align schemas before appending
    CRM_Aligned   = Table.SelectColumns(CRM_Contacts, {"Name","Email","Phone","Source"}),
    ERP_Aligned   = Table.SelectColumns(ERP_Contacts, {"Name","Email","Phone","Source"}),
    Combined      = Table.Combine({CRM_Aligned, ERP_Aligned})
in
    Combined

Read the full file on GitHub · 196 lines

Changes

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.

  1. 11d ago First seen · 196 lines · 0 tokens per session scan A a6e515203ee6

Subscribe to this mod's changes

powerbi-data-transformation is a skill published in the GitHub repository santoshkanthety/powerbi-agent (2 stars, last pushed 12d ago), licensed MIT. It adds 79 tokens to every session and 1,719 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.

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