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 skillmds/skillmd --skill pyarrow-pythongit clone --depth 1 https://github.com/skillmds/skillmdWrote 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/skillmds/skillmd/pyarrow-python)<a href="https://agentmods.dev/skills/skillmds/skillmd/pyarrow-python"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/pyarrow-python/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/skillmds/skillmd/pyarrow-python"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/pyarrow-python.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 | $0.00117 | $0.02120 |
| Opus 5.5 | $0.00047 | $0.00848 |
| Sonnet 5 | $0.00023 | $0.00424 |
| Haiku 4.5 | $0.00012 | $0.00212 |
Grade A, and why
pyarrow-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 4d 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 — 181 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PyArrow Python
Produce schema-explicit columnar code whose logical object, batch boundary, null behavior, memory lifetime, and materialization point are deliberate.
Boundary
Use this skill when the implementation directly uses pyarrow or must expose an
Arrow-compatible boundary. Do not introduce PyArrow merely to perform a small
Python-list or pandas transformation. Keep storage-format questions in scope
only when PyArrow reads or writes them. Arrow Flight servers, Acero internals,
and non-Python implementations require their own guidance.
Classify the object first
| Object | Meaning | Use it for |
|---|---|---|
DataType / Field / Schema |
Immutable type and named-field contracts, including nullability and metadata. | Pinning a boundary before constructing or scanning data. |
Scalar |
One typed value, possibly null. | Kernel arguments and scalar results. |
Array |
One contiguous typed column backed by buffers. | A single physical column segment. |
ChunkedArray |
One logical column made of zero or more same-typed arrays. | Table columns and multi-source results; chunk count is not row count. |
RecordBatch |
Equal-length arrays under one schema. | Bounded transport or processing batches. |
Table |
A logical table whose columns may be chunked. | Materialized tabular results that fit the required memory boundary. |
RecordBatchReader |
A schema plus a consumable stream of batches. | Streaming interchange when the consumer can process batches once. |
Dataset |
A logical collection of fragments with a unified schema. | Discovering and querying multi-file or partitioned data. |
Scanner |
A dataset scan with projection, filter, and batching options bound. | Deferred dataset execution and bounded batch iteration. |
Arrays and schemas are immutable. A Table is materialized but may be
physically chunked; combine_chunks() can allocate and is not routine cleanup.
A Dataset describes sources, while a Scanner describes the read. Calling
to_table() materializes all selected rows; to_batches() preserves a batch
boundary. Read the object and memory model when
chunking, buffers, ownership, dictionaries, nested types, or zero-copy claims
matter.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 181 lines · 117 tokens per session scan A 5c93107dbcc4
pyarrow-python is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 117 tokens to every session and 2,120 once invoked, about $0.0005 per session on Opus 5.5. 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 2026-09-19.
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