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 testing-pythongit 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/testing-python)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/testing-python"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/testing-python/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/testing-python"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/testing-python.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 | $0.00045 | $0.01211 |
| Opus 5 | $0.00023 | $0.00606 |
| Sonnet 5 | $0.00009 | $0.00242 |
| Haiku 4.5 | $0.00005 | $0.00121 |
Grade A, and why
testing-python 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.
This is a copy
100% identical to python-tests — 226 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 221 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Writing Effective Python Tests
Core Principles
Every test should be atomic, self-contained, and test single functionality. A test that tests multiple things is harder to debug and maintain.
Test Structure
Atomic unit tests
Each test should verify a single behavior. The test name should tell you what's broken when it fails. Multiple assertions are fine when they all verify the same behavior.
# Good: Name tells you what's broken
def test_user_creation_sets_defaults():
user = User(name="Alice")
assert user.role == "member"
assert user.id is not None
assert user.created_at is not None
# Bad: If this fails, what behavior is broken?
def test_user():
user = User(name="Alice")
assert user.role == "member"
user.promote()
assert user.role == "admin"
assert user.can_delete_others()
Use parameterization for variations of the same concept
import pytest
@pytest.mark.parametrize("input,expected", [
("hello", "HELLO"),
("World", "WORLD"),
("", ""),
("123", "123"),
])
def test_uppercase_conversion(input, expected):
assert input.upper() == expected
Use separate tests for different functionality
Don't parameterize unrelated behaviors. If the test logic differs, write separate tests.
Project-Specific Rules
No async markers needed
This project uses asyncio_mode = "auto" globally. Write async tests without decorators:
# Correct
async def test_async_operation():
result = await some_async_function()
assert result == expected
# Wrong - don't add this
@pytest.mark.asyncio
async def test_async_operation():
...
Imports at module level
Put ALL imports at the top of the file:
# Correct
import pytest
from fastmcp import FastMCP
from fastmcp.client import Client
async def test_something():
mcp = FastMCP("test")
...
# Wrong - no local imports
async def test_something():
from fastmcp import FastMCP # Don't do this
...
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 · 221 lines · 45 tokens per session scan A 2741f4b02a70
testing-python is a skill published in the GitHub repository benchflow-ai/skillsbench (1,764 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 45 tokens to every session and 1,211 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to python-tests, differing in 226 lines, and is treated as a copy.
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