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-data-transformationgit 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-data-transformation)<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.
<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>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 | $0.00079 | $0.01719 |
| Opus 5 | $0.00039 | $0.00860 |
| Sonnet 5 | $0.00016 | $0.00344 |
| Haiku 4.5 | $0.00008 | $0.00172 |
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.
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
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 · 196 lines · 0 tokens per session scan A a6e515203ee6
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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