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 agentmods add skills/apache/datafusion-python/datafusion_pythonnpx skills add apache/datafusion-python --skill datafusion_pythongit clone --depth 1 https://github.com/apache/datafusion-pythonWhat 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 | $0.00054 | $0.08502 |
| Opus 5 | $0.00027 | $0.04251 |
| Sonnet 5 | $0.00011 | $0.01700 |
| Haiku 4.5 | $0.00005 | $0.00850 |
Grade A, and why
datafusion-python 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 yesterday.
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 — 846 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DataFusion Python DataFrame API Guide
What Is DataFusion?
DataFusion is an in-process query engine built on Apache Arrow. It is not a
database -- there is no server, no connection string, and no external
dependencies. You create a SessionContext, point it at data (Parquet, CSV,
JSON, Arrow IPC, Pandas, Polars, or raw Python dicts/lists), and run queries
using either SQL or the DataFrame API described below.
All data flows through Apache Arrow. The canonical Python implementation is
PyArrow (pyarrow.RecordBatch / pyarrow.Table), but any library that
conforms to the Arrow C Data Interface
can interoperate with DataFusion.
Core Abstractions
| Abstraction | Role | Key import |
|---|---|---|
SessionContext |
Entry point. Loads data, runs SQL, produces DataFrames. | from datafusion import SessionContext |
DataFrame |
Lazy query builder. Each method returns a new DataFrame. | Returned by context methods |
Expr |
Expression tree node (column ref, literal, function call, ...). | from datafusion import col, lit |
functions |
290+ built-in scalar, aggregate, and window functions. | from datafusion import functions as F |
functions.spark |
PySpark-compatible function surface (parameter names match pyspark.sql.functions). |
from datafusion.functions import spark |
Import Conventions
from datafusion import SessionContext, col, lit
from datafusion import functions as F
from datafusion.functions import spark # only when porting pyspark code
Data Loading
ctx = SessionContext()
# From files
df = ctx.read_parquet("path/to/data.parquet")
df = ctx.read_csv("path/to/data.csv")
df = ctx.read_json("path/to/data.json")
# From Python objects
df = ctx.from_pydict({"a": [1, 2, 3], "b": ["x", "y", "z"]})
df = ctx.from_pylist([{"a": 1, "b": "x"}, {"a": 2, "b": "y"}])
df = ctx.from_pandas(pandas_df)
df = ctx.from_polars(polars_df)
df = ctx.from_arrow(arrow_table)
df = ctx.read_batch(record_batch) # one pa.RecordBatch, no named table
df = ctx.read_batches([batch1, batch2]) # several pa.RecordBatch
# From SQL
df = ctx.sql("SELECT a, b FROM my_table WHERE a > 1")
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.
- yesterday First seen · 846 lines · 54 tokens per session scan A 6725b25e6fe5
datafusion-python is a skill published in the GitHub repository apache/datafusion-python (598 stars, last pushed 2d ago), licensed Apache-2.0. It adds 54 tokens to every session and 8,502 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 2026-08-30.
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