Squid Testing Python
iusztinpaul/squid
Write and evaluate effective Python tests using pytest. An agent skill from iusztinpaul/squid.
Write and evaluate effective Python tests using pytest. An agent skill from benchflow-ai/skillsbench.
$ npx skills add benchflow-ai/skillsbench --skill testing-python -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install benchflow-ai/skillsbench testing-python --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/fix-build-agentops/environment/skills/testing-python .claude/skills/testing-python && 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 "testing-python" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-build-agentops/environment/skills/testing-python into .claude/skills/testing-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "testing-python", 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/fix-build-agentops/environment/skills/testing-pythonType 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 testing-python -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install benchflow-ai/skillsbench testing-python --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/fix-build-agentops/environment/skills/testing-python .agents/skills/testing-python && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "testing-python" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-build-agentops/environment/skills/testing-python into .agents/skills/testing-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "testing-python", 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 testing-python -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install benchflow-ai/skillsbench testing-python --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/fix-build-agentops/environment/skills/testing-python .cursor/skills/testing-python && 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 "testing-python" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-build-agentops/environment/skills/testing-python into .cursor/skills/testing-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "testing-python", 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/fix-build-agentops/environment/skills/testing-python--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 testing-python -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install benchflow-ai/skillsbench testing-python --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/fix-build-agentops/environment/skills/testing-python .gemini/skills/testing-python && 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 "testing-python" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-build-agentops/environment/skills/testing-python into .gemini/skills/testing-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "testing-python", 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 testing-pythonInstalls 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 testing-python -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/fix-build-agentops/environment/skills/testing-python .github/skills/testing-python && 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 "testing-python" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-build-agentops/environment/skills/testing-python into .github/skills/testing-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "testing-python", 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 testing-python -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 testing-python --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/fix-build-agentops/environment/skills/testing-python .opencode/skills/testing-python && 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 "testing-python" agent skill from https://github.com/benchflow-ai/skillsbench/tree/main/tasks/fix-build-agentops/environment/skills/testing-python into .opencode/skills/testing-python/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "testing-python", 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.
testing-pythonWrite and evaluate effective Python tests using pytest. An agent skill from benchflow-ai/skillsbench.
Testing Python is an agent skill from benchflow-ai/skillsbench. Write and evaluate effective Python tests using pytest. Use when writing tests, reviewing test code, debugging test failures, or improving test coverage. Covers test design, fixtures, parameterization, mocking, and async testing.
Its SKILL.md is about 1.3k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.
It sits in Testing & QA, covering Unit testing, Test coverage and Failing and flaky tests. It works with Python and pytest. 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.
Shell commands in SKILL.md call:
uvpytestFrom the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md. Its commands use uv, which can reach the network depending on how they are called.
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.
Testing Python loads about 1.3k tokens when it runs. Until then it costs about 61 tokens; SKILL.md has 248 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). 248 words, ~1,341 tokens.
.claude/skills/testing-python/SKILL.md (or your agent's skills folder).Every test should be atomic, self-contained, and test single functionality. A test that tests multiple things is harder to debug and maintain.
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()import pytest
@pytest.mark.parametrize("input,expected", [
("hello", "HELLO"),
("World", "WORLD"),
("", ""),
("123", "123"),
])
def test_uppercase_conversion(input, expected):
assert input.upper() == expectedDon't parameterize unrelated behaviors. If the test logic differs, write separate tests.
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():
...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
...Pass FastMCP servers directly to clients:
from fastmcp import FastMCP
from fastmcp.client import Client
mcp = FastMCP("TestServer")
@mcp.tool
def greet(name: str) -> str:
return f"Hello, {name}!"
async def test_greet_tool():
async with Client(mcp) as client:
result = await client.call_tool("greet", {"name": "World"})
assert result[0].text == "Hello, World!"Only use HTTP transport when explicitly testing network features.
Use inline-snapshot for testing JSON schemas and complex structures:
from inline_snapshot import snapshot
def test_schema_generation():
schema = generate_schema(MyModel)
assert schema == snapshot() # Will auto-populate on first runCommands:
pytest --inline-snapshot=create - populate empty snapshotspytest --inline-snapshot=fix - update after intentional changes@pytest.fixture
def client():
return Client()
async def test_with_client(client):
result = await client.ping()
assert result is not Nonetmp_path for file operationsdef test_file_writing(tmp_path):
file = tmp_path / "test.txt"
file.write_text("content")
assert file.read_text() == "content"from unittest.mock import patch, AsyncMock
async def test_external_api_call():
with patch("mymodule.external_client.fetch", new_callable=AsyncMock) as mock:
mock.return_value = {"data": "test"}
result = await my_function()
assert result == {"data": "test"}Test your code with real implementations when possible. Mock external services, not internal classes.
Use descriptive names that explain the scenario:
# Good
def test_login_fails_with_invalid_password():
def test_user_can_update_own_profile():
def test_admin_can_delete_any_user():
# Bad
def test_login():
def test_update():
def test_delete():import pytest
def test_raises_on_invalid_input():
with pytest.raises(ValueError, match="must be positive"):
calculate(-1)
async def test_async_raises():
with pytest.raises(ConnectionError):
await connect_to_invalid_host()uv run pytest -n auto # Run all tests in parallel
uv run pytest -n auto -x # Stop on first failure
uv run pytest path/to/test.py # Run specific file
uv run pytest -k "test_name" # Run tests matching pattern
uv run pytest -m "not integration" # Exclude integration testsBefore submitting tests:
@pytest.mark.asyncio decorators© 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/fix-build-agentops/environment/skills/testing-python of benchflow-ai/skillsbench.
Open the folder on GitHubat commit 9a1f4dd
Testing Python 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 |
|---|---|---|---|---|---|---|
| Testing Python this skillbenchflow-ai/skillsbench | 1.8k | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| Squid Testing Pythoniusztinpaul/squid | 203 | — | ~1.3k | Automated safety check: Pass | Apache-2.0 | |
| ONNX Runtime Test Runnermicrosoft/onnxruntime | 22k | — | ~1.8k | Automated safety check: Pass | MIT | |
| Test Coverage Reviewareed1192/finance-news-aggregator | 149 | — | ~2.6k | Automated safety check: Pass | MIT | |
| ONNX Runtime GPU Transformers Testsmicrosoft/onnxruntime | 22k | — | ~2.9k | Automated safety check: Pass | MIT | |
| Temporal Python Testingwshobson/agents | 40k | 12 repos | ~1.2k | Automated safety check: Pass | MIT |
iusztinpaul/squid
Write and evaluate effective Python tests using pytest. An agent skill from iusztinpaul/squid.
microsoft/onnxruntime
Runs and debugs ONNX Runtime tests: Google Test executables for C++ and unittest or pytest for Python, with filters and build-directory guidance.
areed1192/finance-news-aggregator
Audit, plan, write, and verify unit tests for Python projects using pytest.
microsoft/onnxruntime
Runs the ONNX Runtime transformers Python tests against a GPU wheel and proves the cuDNN flash attention path was used rather than a silent fallback.
wshobson/agents
Test Temporal workflows with pytest, time-skipping, and mocking strategies.
buildfastwithai/gen-ai-experiments
Measures how well a Python pytest suite catches behavior changes through diff-scoped mutation testing, then proposes and verifies tests for surviving mutants.
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.
Categories
Write and evaluate effective Python tests using pytest. An agent skill from benchflow-ai/skillsbench. Testing Python is an agent skill from benchflow-ai/skillsbench. Write and evaluate effective Python tests using pytest.
Testing Python fits situations like: reviewing test code; debugging test failures; improving test coverage.
Run `npx skills add benchflow-ai/skillsbench --skill testing-python -a claude-code`. Or copy the skill folder (tasks/fix-build-agentops/environment/skills/testing-python in benchflow-ai/skillsbench) into .claude/skills/testing-python in your project. Claude Code loads it when a task matches its description.
Run `npx skills add benchflow-ai/skillsbench --skill testing-python -a codex`. Or copy the skill folder (tasks/fix-build-agentops/environment/skills/testing-python in benchflow-ai/skillsbench) into .agents/skills/testing-python 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 testing-python -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/testing-python, .gemini/skills/testing-python, .github/skills/testing-python and .opencode/skills/testing-python in your project.
Going by SKILL.md and its folder, Testing Python needs the command-line tools its instructions call (uv and pytest). Our summary lists: Python 3.
SKILL.md contains no URLs. Its commands use uv, which can reach the network depending on how they are called. 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.
Testing Python 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 1.3k tokens (SKILL.md is roughly 5.4k 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 Testing Python: Squid Testing Python (iusztinpaul/squid, 203 stars), ONNX Runtime Test Runner (microsoft/onnxruntime, 22k stars), Test Coverage Review (areed1192/finance-news-aggregator, 149 stars) and ONNX Runtime GPU Transformers Tests (microsoft/onnxruntime, 22k 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.