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 pandera-polarsgit 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/pandera-polars)<a href="https://agentmods.dev/skills/skillmds/skillmd/pandera-polars"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/pandera-polars/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/pandera-polars"><img src="https://agentmods.dev/badge/skills/skillmds/skillmd/pandera-polars.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 | $0.00112 | $0.01923 |
| Opus 5.5 | $0.00045 | $0.00769 |
| Sonnet 5 | $0.00022 | $0.00385 |
| Haiku 4.5 | $0.00011 | $0.00192 |
Grade A, and why
pandera-polars 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 — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pandera for Polars
Create executable Polars dataframe contracts whose backend, validation depth, coercion, failure aggregation, and pipeline boundary are explicit.
Boundary
Use this skill only for Pandera's Polars backend. Import it as
pandera.polars; pandas, Ibis, PySpark, and Narwhals-backed behavior differs.
Use the Polars skill for transformation semantics and this skill for runtime
dataframe contracts. Do not replace ordinary Python object validation with a
one-row dataframe schema.
Know the objects and overloaded words
| Object | Runtime meaning | Use it for |
|---|---|---|
DataFrameSchema |
An executable schema object containing Polars column and dataframe checks. | Dynamic/programmatic schemas and schema composition. |
Column |
A named column contract: dtype, nullability, requirement, uniqueness, coercion, and checks. | Per-column structural and value rules. |
Check |
A predicate contract evaluated by the backend. | Domain constraints not captured by dtype/nullability. |
DataFrameModel |
A class-declared schema compiled from annotations, Fields, checks, and config. |
Reusable named contracts with type-checker-friendly declarations. |
Field |
Declarative column constraints inside a DataFrameModel. |
Built-in comparisons, membership, aliases, nullable/unique behavior. |
PolarsData |
Custom-check input holding a LazyFrame and optional column key. |
Native vectorized Polars checks. |
SchemaError / SchemaErrors |
One validation failure or an aggregate of failures. | Machine-readable failure handling and diagnostics. |
Two kinds of “lazy” must remain separate:
pl.LazyFrameis a deferred Polars query. Pandera's native Polars validation checks schema-level properties by default and does not automatically execute all data-level checks on an uncollected plan.schema.validate(..., lazy=True)requests accumulation of multiple validation failures before raising; it does not make eager validation computationally lazy.
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 · 187 lines · 112 tokens per session scan A f2aaeadb9f36
pandera-polars is a skill published in the GitHub repository skillmds/skillmd (1 stars, last pushed yesterday), licensed MIT. It adds 112 tokens to every session and 1,923 once invoked, about $0.0004 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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