Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill hypothesis-testinggit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/hypothesis-testing)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/hypothesis-testing"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/hypothesis-testing/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/foryourhealth111-pixel/vibe-skills/hypothesis-testing"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/hypothesis-testing.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00062 | $0.05761 |
| Opus 5 | $0.00031 | $0.02880 |
| Sonnet 5 | $0.00012 | $0.01152 |
| Haiku 4.5 | $0.00006 | $0.00576 |
Grade A, and why
hypothesis-testing 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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 873 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypothesis Property-Based Testing
Hypothesis is a powerful property-based testing library that automatically generates test cases to find edge cases and validate properties of your code.
Core Concept
Traditional example-based testing:
def test_addition():
assert add(2, 3) == 5
assert add(0, 0) == 0
assert add(-1, 1) == 0
Property-based testing with Hypothesis:
from hypothesis import given
import hypothesis.strategies as st
@given(st.integers(), st.integers())
def test_addition_commutative(a, b):
"""Addition is commutative for ALL integers."""
assert add(a, b) == add(b, a)
Hypothesis generates hundreds of test cases automatically, including edge cases you might not think of.
Installation
# Install hypothesis with pytest integration
uv add --dev hypothesis pytest
# Optional plugins
uv add --dev hypothesis[numpy] # NumPy strategies
uv add --dev hypothesis[pandas] # Pandas strategies
uv add --dev hypothesis[django] # Django model strategies
Configuration
pyproject.toml Configuration
[tool.pytest.ini_options]
# Hypothesis settings
addopts = [
"--hypothesis-show-statistics", # Show test statistics
"--hypothesis-seed=0", # Reproducible tests (optional)
]
[tool.hypothesis]
# Maximum number of examples to generate
max_examples = 200 # Default: 100, CI: 200+
# Deadline for each test case (milliseconds)
deadline = 1000 # Default: 200ms, None to disable
# Verbosity level (quiet, normal, verbose, debug)
verbosity = "normal"
# Fail fast on first error
derandomize = false # Set to true for deterministic tests
# Database for example storage
database = ".hypothesis/examples" # Store found failures
# Profile-specific settings
[tool.hypothesis.profiles.dev]
max_examples = 50
deadline = 1000
verbosity = "normal"
[tool.hypothesis.profiles.ci]
max_examples = 500
deadline = 5000
verbosity = "verbose"
[tool.hypothesis.profiles.debug]
max_examples = 10
deadline = null
verbosity = "debug"
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.
- 8d ago First seen · 873 lines · 62 tokens per session scan A 5c3164e4d4ae
hypothesis-testing is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 11d ago), licensed Apache-2.0. It adds 62 tokens to every session and 5,761 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
python-testing
Guidelines for writing and running tests in the Agent Framework Python codebase. Use this when creating, modifying, or running tests.
test-generator
Generate pytest test cases for Python functions and classes.
python-quality-gate
Python quality checks: ruff, pytest, mypy, bandit in deterministic order.
python-rules
Python coding rules: style, patterns, security, testing. Triggers: .py, .pyi, pyproject.toml, requirements.txt, Pipfile, FastAPI, Django, Flask, pytest, SQLAlchemy, ruff, mypy.
pytest-patterns
Pytest best practices including fixtures, parametrize, markers, and assertion patterns for Python test suites.
swarmauri-add-community-standalone
Add a second-class standalone Swarmauri package under pkgs/community. Use when Codex needs community package scaffolding, workspace membership, pyproject metadata, README branding, entry points, second-class citizenship rows, exports, tests, and validation.