python-scala-functional

python-scala-functional is a skill for Claude Code, Codex from benchflow-ai/skillsbench. It costs 51 tokens per session (2,369 once invoked), scanned A, original, Apache-2.0.

A guide for translating Python functions, decorators, closures, and generators into functional Scala using higher-order functions, composition, pattern matching, and Option.

In plain words
What is it for?
Use it when rewriting callbacks, reusable functions, decorators, and missing-value handling in Scala.
Why use it?
It helps you carry over Python's function-based techniques without forcing them into Scala's different functional programming style.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it when rewriting callbacks, reusable functions, decorators, and missing-value handling in Scala.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/benchflow-ai/skillsbench/python-scala-functional
About the project

SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.

benchflow-ai/skillsbench · 1,764 stars · on GitHub · skillsbench.ai

Install

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.

Any agent
npx skills add benchflow-ai/skillsbench --skill python-scala-functional
Clone the repo
git clone --depth 1 https://github.com/benchflow-ai/skillsbench

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for python-scala-functional

README.md
[![agentmods](https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/python-scala-functional/github.svg)](https://agentmods.dev/skills/benchflow-ai/skillsbench/python-scala-functional)
Your own site
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/python-scala-functional"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/python-scala-functional/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.

agentmods 80×15 button for python-scala-functional

Your own site · 80×15
<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/python-scala-functional"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/python-scala-functional.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 51 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,369 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00051 $0.02369
Opus 5 $0.00026 $0.01184
Sonnet 5 $0.00010 $0.00474
Haiku 4.5 $0.00005 $0.00237

Measured 9d ago against content hash cf0986337f4b, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

python-scala-functional 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 9d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

tasks/python-scala-translation/environment/skills/python-scala-functional/SKILL.md · 371 lines

How it starts

The opening of the file, as written. The whole thing — 371 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Python to Scala Functional Programming Translation

Higher-Order Functions

# Python
def apply_twice(f, x):
    return f(f(x))

def make_multiplier(n):
    return lambda x: x * n

double = make_multiplier(2)
result = apply_twice(double, 5)  # 20
// Scala
def applyTwice[A](f: A => A, x: A): A = f(f(x))

def makeMultiplier(n: Int): Int => Int = x => x * n

val double = makeMultiplier(2)
val result = applyTwice(double, 5)  // 20

Decorators → Function Composition

# Python
def log_calls(func):
    def wrapper(*args, **kwargs):
        print(f"Calling {func.__name__}")
        result = func(*args, **kwargs)
        print(f"Finished {func.__name__}")
        return result
    return wrapper

@log_calls
def add(a, b):
    return a + b
// Scala - function composition
def logCalls[A, B](f: A => B, name: String): A => B = { a =>
  println(s"Calling $name")
  val result = f(a)
  println(s"Finished $name")
  result
}

val add = (a: Int, b: Int) => a + b
val loggedAdd = logCalls(add.tupled, "add")

// Alternative: using by-name parameters
def withLogging[A](name: String)(block: => A): A = {
  println(s"Calling $name")
  val result = block
  println(s"Finished $name")
  result
}

Pattern Matching

# Python (3.10+)
def describe(value):
    match value:
        case 0:
            return "zero"
        case int(x) if x > 0:
            return "positive int"
        case int(x):
            return "negative int"
        case [x, y]:
            return f"pair: {x}, {y}"
        case {"name": name, "age": age}:
            return f"{name} is {age}"
        case _:
            return "unknown"
// Scala - pattern matching is more powerful
def describe(value: Any): String = value match {
  case 0 => "zero"
  case x: Int if x > 0 => "positive int"
  case _: Int => "negative int"
  case (x, y) => s"pair: $x, $y"
  case List(x, y) => s"list of two: $x, $y"
  case m: Map[_, _] if m.contains("name") =>
    s"${m("name")} is ${m("age")}"
  case _ => "unknown"
}

// Case class pattern matching (preferred)
sealed trait Result
case class Success(value: Int) extends Result
case class Error(message: String) extends Result

def handle(result: Result): String = result match {
  case Success(v) if v > 100 => s"Big success: $v"
  case Success(v) => s"Success: $v"
  case Error(msg) => s"Failed: $msg"
}

Read the full file on GitHub · 371 lines

Changes

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.

  1. 9d ago First seen · 371 lines · 51 tokens per session scan A cf0986337f4b

Subscribe to this mod's changes

python-scala-functional is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 51 tokens to every session and 2,369 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 2026-09-03.