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 cxcscmu/SkillLearnBench --skill python-scala-oopgit clone --depth 1 https://github.com/cxcscmu/SkillLearnBenchWrote 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/cxcscmu/skilllearnbench/python-scala-oop)<a href="https://agentmods.dev/skills/cxcscmu/skilllearnbench/python-scala-oop"><img src="https://agentmods.dev/badge/skills/cxcscmu/skilllearnbench/python-scala-oop.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.00397 |
| Opus 5 | $0.00013 | $0.00198 |
| Sonnet 5 | $0.00005 | $0.00079 |
| Haiku 4.5 | $0.00003 | $0.00040 |
Grade A, and why
python-scala-oop 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 3d 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.
What it actually says
Python to Scala OOP Patterns
Abstract Base Class -> Abstract Class or Trait
// Python: class BaseTokenizer(ABC, Generic[T])
// Scala:
abstract class BaseTokenizer[T] {
def tokenize(value: T): Token
def tokenizeBatch(values: Iterable[T]): Iterator[Token] =
values.iterator.map(tokenize)
}
Generic Classes with Variance
// Covariant container (produces T)
class TokenContainer[+T](items: Seq[T]) {
private val _items: Vector[T] = items.toVector
def getAll: Vector[T] = _items
}
// Contravariant sink (consumes T)
class TokenSink[-T] { ... }
// Invariant handler
class BivariantHandler[T](private var _value: T) { ... }
Builder Pattern (Fluent Interface)
// Use `this.type` or return the class itself for chaining
class TokenizerBuilder[T] {
def withNormalizer(f: String => String): TokenizerBuilder[T] = { ... ; this }
def build(): T => Token = { ... }
}
// Companion object with apply for factory
object TokenizerBuilder {
def apply[T](): TokenizerBuilder[T] = new TokenizerBuilder[T]
}
Mutable State
// Python: @dataclass with mutable fields
// Scala: use var or mutable collections explicitly
import scala.collection.mutable
class MutableTokenBatch {
private val _tokens = mutable.ListBuffer.empty[Token]
private var _processed = false
def add(token: Token): Unit =
if (_processed) throw new RuntimeException("Batch already processed")
else _tokens += token
}
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.
- 3d ago First seen · 63 lines · 26 tokens per session scan A 344813b6d483
python-scala-oop is a skill published in the GitHub repository cxcscmu/SkillLearnBench (83 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 397 once invoked, about $0.0001 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 2026-09-03.
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