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 agentmods add skills/noesisvision/nasde-toolkit/python-testingnpx skills add NoesisVision/nasde-toolkit --skill python-testinggit clone --depth 1 https://github.com/NoesisVision/nasde-toolkitWhat 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 | $0.00024 | $0.04420 |
| Opus 5 | $0.00012 | $0.02210 |
| Sonnet 5 | $0.00005 | $0.00884 |
| Haiku 4.5 | $0.00002 | $0.00442 |
Grade A, and why
python-testing scanned grade A with 1 finding 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 yesterday.
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.
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") This is a copy
100% identical to python-testing — 0 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 — 817 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Testing Patterns
Comprehensive testing strategies for Python applications using pytest, TDD methodology, and best practices.
When to Activate
- Writing new Python code (follow TDD: red, green, refactor)
- Designing test suites for Python projects
- Reviewing Python test coverage
- Setting up testing infrastructure
Core Testing Philosophy
Test-Driven Development (TDD)
Always follow the TDD cycle:
- RED: Write a failing test for the desired behavior
- GREEN: Write minimal code to make the test pass
- REFACTOR: Improve code while keeping tests green
# Step 1: Write failing test (RED)
def test_add_numbers():
result = add(2, 3)
assert result == 5
# Step 2: Write minimal implementation (GREEN)
def add(a, b):
return a + b
# Step 3: Refactor if needed (REFACTOR)
Coverage Requirements
- Target: 80%+ code coverage
- Critical paths: 100% coverage required
- Use
pytest --covto measure coverage
pytest --cov=mypackage --cov-report=term-missing --cov-report=html
pytest Fundamentals
Basic Test Structure
import pytest
def test_addition():
"""Test basic addition."""
assert 2 + 2 == 4
def test_string_uppercase():
"""Test string uppercasing."""
text = "hello"
assert text.upper() == "HELLO"
def test_list_append():
"""Test list append."""
items = [1, 2, 3]
items.append(4)
assert 4 in items
assert len(items) == 4
Assertions
# Equality
assert result == expected
# Inequality
assert result != unexpected
# Truthiness
assert result # Truthy
assert not result # Falsy
assert result is True # Exactly True
assert result is False # Exactly False
assert result is None # Exactly None
# Membership
assert item in collection
assert item not in collection
# Comparisons
assert result > 0
assert 0 <= result <= 100
# Type checking
assert isinstance(result, str)
# Exception testing (preferred approach)
with pytest.raises(ValueError):
raise ValueError("error message")
# Check exception message
with pytest.raises(ValueError, match="invalid input"):
raise ValueError("invalid input provided")
# Check exception attributes
with pytest.raises(ValueError) as exc_info:
raise ValueError("error message")
assert str(exc_info.value) == "error message"
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.
- yesterday First seen · 817 lines · 24 tokens per session scan A b304a8c503af
python-testing is a skill published in the GitHub repository NoesisVision/nasde-toolkit (12 stars, last pushed 8d ago), licensed MIT. It adds 24 tokens to every session and 4,420 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). It is 100% identical to python-testing, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
ifixai
Guide the user through an independent iFixAi audit of their own agent, checking whether it does the job it is supposed to do given their business rules and org structure. Prefer pointing it at the user's REAL deployed agent over its HTTP endpoint (its actual tools, retrieval, and governance) with --provider http…
code-reviewer
Performs comprehensive code reviews with security, quality, and best practice checks.
generate-tests
Generate EvalView test cases — either from a SKILL.md file using LLM-powered generation, or by capturing real agent interactions through a proxy.
run-eval
Run EvalView regression checks against golden baselines to detect regressions in AI agent behavior after code, prompt, or model changes.
watch
Start EvalView watch mode to automatically re-run regression checks whenever project files change.
procrastination-buster
Beat procrastination with task breakdown, 2-minute starts, and accountability tracking.