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 5.5 | $0.00025 | $0.02304 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 (3,772 stars, last pushed 2d ago), licensed Apache-2.0. It adds 62 tokens to every session and 5,761 once invoked, about $0.0002 per session on Opus 5.5. 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.
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python
Use when writing, reviewing, or modernizing Python 3.11+ code. Produces fully type-annotated modules, async I/O, dataclasses and protocols, pytest suites, and a lint/type gate built on ruff and mypy --strict. Trigger on "add type hints to this", "mypy is complaining", "convert this to asyncio", "set up pytest for this…
testing
Python test-writing best practices covering test structure, fixtures, parametrization, and mocking with pytest. Use when writing, reviewing, or refactoring tests, adding test coverage, or working in tests directories and conftest.py.
tooling
Python development tooling configuration and best practices for ruff, mypy, pytest, pyscn, and uv. Use when setting up a Python project, configuring linters, formatters, type checkers, or test frameworks, or when editing pyproject.toml or tool config files.
ci-tests
Run the test suite for the current repo, auto-detecting Python (pytest/uv), Node (vitest/pnpm), or Rust (cargo test).
dev-python-quality
Use when Python-specific typing, async behavior, packaging, pytest, ruff/mypy, scripting, or performance quality is central to the request. Do not trigger for generic small code edits merely written in Python.