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.
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 benchflow-ai/skillsbench --skill python-scala-librariesgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/python-scala-libraries)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/python-scala-libraries"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/python-scala-libraries/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/benchflow-ai/skillsbench/python-scala-libraries"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/python-scala-libraries.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Data Exfiltration · line 204 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 204 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 201 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 205 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 220 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 224 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00054 | $0.02397 |
| Opus 5 | $0.00027 | $0.01198 |
| Sonnet 5 | $0.00011 | $0.00479 |
| Haiku 4.5 | $0.00005 | $0.00240 |
Grade B, and why
python-scala-libraries scanned grade B with 2 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.
Sends data to an external URLmediumData exfiltration
A POST to an outside endpoint may be telemetry or may be exfiltration; either way the mod talks to somewhere, and you should know where.
response = requests.post( "https://api.example.com/data", Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get("https://api.example.com/data") Copies of this mod
1 near-identical copy found in the catalogue:
- python-scala-libraries — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python to Scala Library Mappings
JSON Handling
# 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]
Date and Time
# 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()
File Operations
# 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)
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.
- 9d ago First seen · 447 lines · 54 tokens per session scan B aab0e5fdfc33
python-scala-libraries is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 54 tokens to every session and 2,397 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 2 findings (sends data to an external url, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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