MCP Server Builder
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
Guide for mapping common Python libraries and idioms to Scala equivalents.
$ npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench python-scala-libraries --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .claude/skills && cp -r skills-src/tasks/python-scala-translation/environment/skills/python-scala-libraries .claude/skills/python-scala-libraries && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "python-scala-libraries" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-libraries into .claude/skills/python-scala-libraries/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-scala-libraries", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-librariesType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench python-scala-libraries --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .agents/skills && cp -r skills-src/tasks/python-scala-translation/environment/skills/python-scala-libraries .agents/skills/python-scala-libraries && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "python-scala-libraries" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-libraries into .agents/skills/python-scala-libraries/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-scala-libraries", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench python-scala-libraries --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/tasks/python-scala-translation/environment/skills/python-scala-libraries .cursor/skills/python-scala-libraries && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "python-scala-libraries" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-libraries into .cursor/skills/python-scala-libraries/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-scala-libraries", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/benchflow-ai/skillsbench.git --path tasks/python-scala-translation/environment/skills/python-scala-libraries--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench python-scala-libraries --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/tasks/python-scala-translation/environment/skills/python-scala-libraries .gemini/skills/python-scala-libraries && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "python-scala-libraries" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-libraries into .gemini/skills/python-scala-libraries/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-scala-libraries", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install benchflow-ai/skillsbench python-scala-librariesInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .github/skills && cp -r skills-src/tasks/python-scala-translation/environment/skills/python-scala-libraries .github/skills/python-scala-libraries && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "python-scala-libraries" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-libraries into .github/skills/python-scala-libraries/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-scala-libraries", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install benchflow-ai/skillsbench python-scala-libraries --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/benchflow-ai/skillsbench.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/tasks/python-scala-translation/environment/skills/python-scala-libraries .opencode/skills/python-scala-libraries && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "python-scala-libraries" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/python-scala-translation/environment/skills/python-scala-libraries into .opencode/skills/python-scala-libraries/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "python-scala-libraries", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
python-scala-librariesGuide for mapping common Python libraries and idioms to Scala equivalents.
Python Scala Libraries is an agent skill from benchflow-ai/skillsbench. Guide for mapping common Python libraries and idioms to Scala equivalents. Use when converting Python code that uses standard library modules (json, datetime, os, re, logging) or needs equivalent Scala libraries for HTTP, testing, or async operations.
Its SKILL.md is about 2.2k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It works with Python. The repository describes itself as: SkillsBench evaluates how well skills work and how effective agents are at using them. The licence is Apache-2.0.
Read from SKILL.md and the folder at commit 9a1f4dd. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are python and scala).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Python Scala Libraries loads about 2.2k tokens when it runs. Until then it costs about 69 tokens; SKILL.md has 41 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from benchflow-ai/skillsbench at commit 9a1f4dd, republished under its Apache-2.0 licence (© benchflow-ai). 41 words, ~2,237 tokens.
.claude/skills/python-scala-libraries/SKILL.md (or your agent's skills folder).# Python
import json
data = {"name": "Alice", "age": 30}
json_str = json.dumps(data)
parsed = json.loads(json_str)
# With dataclass
from dataclasses import dataclass, asdict
@dataclass
class Person:
name: str
age: int
person = Person("Alice", 30)
json.dumps(asdict(person))// Scala - circe (most popular)
import io.circe._
import io.circe.generic.auto._
import io.circe.syntax._
import io.circe.parser._
case class Person(name: String, age: Int)
val person = Person("Alice", 30)
val jsonStr: String = person.asJson.noSpaces
val parsed: Either[Error, Person] = decode[Person](jsonStr)
// Scala - play-json
import play.api.libs.json._
case class Person(name: String, age: Int)
implicit val personFormat: Format[Person] = Json.format[Person]
val json = Json.toJson(person)
val parsed = json.as[Person]# Python
from datetime import datetime, date, timedelta
import pytz
now = datetime.now()
today = date.today()
specific = datetime(2024, 1, 15, 10, 30)
formatted = now.strftime("%Y-%m-%d %H:%M:%S")
parsed = datetime.strptime("2024-01-15", "%Y-%m-%d")
tomorrow = now + timedelta(days=1)
# Timezone
utc_now = datetime.now(pytz.UTC)// Scala - java.time (recommended)
import java.time._
import java.time.format.DateTimeFormatter
val now = LocalDateTime.now()
val today = LocalDate.now()
val specific = LocalDateTime.of(2024, 1, 15, 10, 30)
val formatted = now.format(DateTimeFormatter.ofPattern("yyyy-MM-dd HH:mm:ss"))
val parsed = LocalDate.parse("2024-01-15")
val tomorrow = now.plusDays(1)
// Timezone
val utcNow = ZonedDateTime.now(ZoneOffset.UTC)
val instant = Instant.now()# Python
from pathlib import Path
import os
# Reading
with open("file.txt", "r") as f:
content = f.read()
lines = f.readlines()
# Writing
with open("file.txt", "w") as f:
f.write("Hello")
# Path operations
path = Path("dir/file.txt")
path.exists()
path.parent
path.name
path.suffix
list(Path(".").glob("*.txt"))
os.makedirs("new/dir", exist_ok=True)// Scala - java.nio.file (standard) + scala.io
import java.nio.file.{Files, Path, Paths}
import scala.io.Source
import scala.util.Using
// Reading
val content = Source.fromFile("file.txt").mkString
Using(Source.fromFile("file.txt")) { source =>
source.getLines().toList
}
// Writing
Files.writeString(Paths.get("file.txt"), "Hello")
// Path operations
val path = Paths.get("dir/file.txt")
Files.exists(path)
path.getParent
path.getFileName
// Extension: path.toString.split('.').lastOption
Files.createDirectories(Paths.get("new/dir"))
// os-lib (better alternative)
// import os._
// os.read(os.pwd / "file.txt")
// os.write(os.pwd / "file.txt", "Hello")# Python
import re
pattern = r"\d+"
text = "abc123def456"
# Search
match = re.search(pattern, text)
if match:
print(match.group())
# Find all
matches = re.findall(pattern, text)
# Replace
result = re.sub(pattern, "X", text)
# Split
parts = re.split(r"\s+", "a b c")// Scala
val pattern = """\d+""".r
val text = "abc123def456"
// Search
pattern.findFirstIn(text) match {
case Some(m) => println(m)
case None => ()
}
// Find all
val matches = pattern.findAllIn(text).toList
// Replace
val result = pattern.replaceAllIn(text, "X")
// Split
val parts = """\s+""".r.split("a b c").toList
// Pattern matching with regex
val datePattern = """(\d{4})-(\d{2})-(\d{2})""".r
"2024-01-15" match {
case datePattern(year, month, day) => s"$year/$month/$day"
case _ => "no match"
}# Python
import requests
response = requests.get("https://api.example.com/data")
data = response.json()
response = requests.post(
"https://api.example.com/data",
json={"key": "value"},
headers={"Authorization": "Bearer token"}
)// Scala - sttp (recommended)
import sttp.client3._
import sttp.client3.circe._
import io.circe.generic.auto._
val backend = HttpURLConnectionBackend()
val response = basicRequest
.get(uri"https://api.example.com/data")
.send(backend)
val postResponse = basicRequest
.post(uri"https://api.example.com/data")
.body("""{"key": "value"}""")
.header("Authorization", "Bearer token")
.send(backend)# Python
import logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
logger.debug("Debug message")
logger.info("Info message")
logger.warning("Warning message")
logger.error("Error message", exc_info=True)// Scala - scala-logging with logback
import com.typesafe.scalalogging.LazyLogging
class MyClass extends LazyLogging {
logger.debug("Debug message")
logger.info("Info message")
logger.warn("Warning message")
logger.error("Error message", exception)
}
// Alternative: slf4j directly
import org.slf4j.LoggerFactory
val logger = LoggerFactory.getLogger(getClass)# Python - pytest
import pytest
def test_addition():
assert 1 + 1 == 2
@pytest.fixture
def sample_data():
return {"key": "value"}
def test_with_fixture(sample_data):
assert sample_data["key"] == "value"
@pytest.mark.parametrize("input,expected", [
(1, 2),
(2, 4),
])
def test_double(input, expected):
assert input * 2 == expected// Scala - ScalaTest
import org.scalatest.flatspec.AnyFlatSpec
import org.scalatest.matchers.should.Matchers
class MySpec extends AnyFlatSpec with Matchers {
"Addition" should "work" in {
1 + 1 shouldEqual 2
}
it should "handle negative numbers" in {
-1 + 1 shouldEqual 0
}
}
// Table-driven tests
import org.scalatest.prop.TableDrivenPropertyChecks._
val examples = Table(
("input", "expected"),
(1, 2),
(2, 4)
)
forAll(examples) { (input, expected) =>
input * 2 shouldEqual expected
}
// Scala - munit (simpler alternative)
class MySuite extends munit.FunSuite {
test("addition") {
assertEquals(1 + 1, 2)
}
}# Python
import asyncio
async def fetch_data(url: str) -> str:
# async HTTP call
await asyncio.sleep(1)
return "data"
async def main():
results = await asyncio.gather(
fetch_data("url1"),
fetch_data("url2"),
)// Scala - Future (standard)
import scala.concurrent.{Future, ExecutionContext}
import scala.concurrent.ExecutionContext.Implicits.global
def fetchData(url: String): Future[String] = Future {
Thread.sleep(1000)
"data"
}
val results: Future[List[String]] = Future.sequence(List(
fetchData("url1"),
fetchData("url2")
))
// Scala - cats-effect IO (preferred for FP)
import cats.effect.IO
import cats.syntax.parallel._
def fetchData(url: String): IO[String] = IO.sleep(1.second) *> IO.pure("data")
val results: IO[List[String]] = List(
fetchData("url1"),
fetchData("url2")
).parSequence# Python
import os
from dataclasses import dataclass
@dataclass
class Config:
db_host: str = os.getenv("DB_HOST", "localhost")
db_port: int = int(os.getenv("DB_PORT", "5432"))// Scala - pureconfig
import pureconfig._
import pureconfig.generic.auto._
case class Config(dbHost: String, dbPort: Int)
val config = ConfigSource.default.loadOrThrow[Config]
// application.conf (HOCON format)
// db-host = "localhost"
// db-host = ${?DB_HOST}
// db-port = 5432# Python
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--name", required=True)
parser.add_argument("--count", type=int, default=1)
args = parser.parse_args()// Scala - scopt
import scopt.OParser
case class Config(name: String = "", count: Int = 1)
val builder = OParser.builder[Config]
val parser = {
import builder._
OParser.sequence(
opt[String]("name").required().action((x, c) => c.copy(name = x)),
opt[Int]("count").action((x, c) => c.copy(count = x))
)
}
OParser.parse(parser, args, Config()) match {
case Some(config) => // use config
case None => // arguments are bad
}
// Scala - decline (FP style)
import com.monovore.decline._
val nameOpt = Opts.option[String]("name", "Name")
val countOpt = Opts.option[Int]("count", "Count").withDefault(1)
val command = Command("app", "Description") {
(nameOpt, countOpt).tupled
}| Python | Scala |
|---|---|
| pip/poetry | sbt/mill |
| requirements.txt | build.sbt |
| pyproject.toml | build.sbt |
| setup.py | build.sbt |
| virtualenv | Project-local dependencies (automatic) |
© benchflow-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Just SKILL.md in tasks/python-scala-translation/environment/skills/python-scala-libraries of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Python Scala Libraries next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Python Scala Libraries this skillbenchflow-ai/skillsbench | 1.8k | — | ~2.2k | Automated safety check: Pass | Apache-2.0 | |
| MCP Server Builderanthropics/skills | 180k | 64 repos | ~2.3k | Automated safety check: Pass | Apache-2.0 | |
| PDF Processinganthropics/skills | 180k | 48 repos | ~2k | Automated safety check: Pass | Proprietary | |
| NotebookLM Research AssistantPleasePrompto/notebooklm-skill | 7.8k | 14 repos | ~2.4k | Automated safety check: Notes | MIT | |
| Manim Video Productionbrowser-use/video-use | 28k | 6 repos | ~3k | Automated safety check: Pass | MIT | |
| Code Review ChecklistshareAI-lab/learn-claude-code | 78k | 5 repos | ~1.1k | Automated safety check: Pass | MIT |
anthropics/skills
Guides the design and implementation of Model Context Protocol servers in TypeScript or Python, from tool naming and error messages to evaluation.
anthropics/skills
Handles everyday PDF jobs in Python and on the command line: extract text and tables, merge, split, rotate, watermark, fill forms, encrypt and OCR.
PleasePrompto/notebooklm-skill
Lets Claude Code ask questions of your Google NotebookLM notebooks through browser automation and return answers grounded in your uploaded sources.
browser-use/video-use
Produces math and technical explainer videos with Manim Community Edition: concept animations, equation derivations, algorithm walkthroughs and data stories.
shareAI-lab/learn-claude-code
Reviews code against a five-part checklist covering security, correctness, performance, maintainability and testing, and reports findings in a fixed format.
hugohe3/ppt-master
Generates editable PowerPoint decks, rebuilds slides from images, fills .pptx templates and polishes existing presentations through routed workflows.
benchflow-ai/skillsbench
This skill should be used when working on Lean 4 formalization projects to maintain persistent memory of successful proof patterns, failed approaches, project conventions, and user preferences…
benchflow-ai/skillsbench
World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure.
benchflow-ai/skillsbench
AC branch pi-model power flow equations (P/Q and |S|) with transformer tap ratio and phase shift, matching acopf-math-model.md and MATPOWER branch fields.
benchflow-ai/skillsbench
Civilization 6 district mechanics library. An agent skill from benchflow-ai/skillsbench.
benchflow-ai/skillsbench
Build deterministic, verifiable data visualizations with D3.js (v6).
benchflow-ai/skillsbench
DC power flow analysis for power systems. An agent skill from benchflow-ai/skillsbench.
Works with
Guide for mapping common Python libraries and idioms to Scala equivalents. Python Scala Libraries is an agent skill from benchflow-ai/skillsbench. Guide for mapping common Python libraries and idioms to Scala equivalents.
Python Scala Libraries fits situations like: converting Python code that uses standard library modules (json; needs equivalent Scala libraries for HTTP; async operations.
Run `npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a claude-code`. Or copy the skill folder (tasks/python-scala-translation/environment/skills/python-scala-libraries in benchflow-ai/skillsbench) into .claude/skills/python-scala-libraries in your project. Claude Code loads it when a task matches its description.
Run `npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a codex`. Or copy the skill folder (tasks/python-scala-translation/environment/skills/python-scala-libraries in benchflow-ai/skillsbench) into .agents/skills/python-scala-libraries in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add benchflow-ai/skillsbench --skill python-scala-libraries -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/python-scala-libraries, .gemini/skills/python-scala-libraries, .github/skills/python-scala-libraries and .opencode/skills/python-scala-libraries in your project.
SKILL.md names no scripts, command-line tools or credentials: Python Scala Libraries is instructions for the agent only. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Python Scala Libraries is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 2.2k tokens (SKILL.md is roughly 8.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.
Skills that share tags, products or a category with Python Scala Libraries: MCP Server Builder (anthropics/skills, 180k stars), PDF Processing (anthropics/skills, 180k stars), NotebookLM Research Assistant (PleasePrompto/notebooklm-skill, 7.8k stars) and Manim Video Production (browser-use/video-use, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
benchflow-ai (a GitHub organization) maintains it in benchflow-ai/skillsbench, which has 1,832 GitHub stars. The repository holds 178 skills in this directory. The repository was last updated on July 23, 2026.
Source: benchflow-ai/skillsbench on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.