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 bestdeejay-design/agent-skills --skill test-generatorgit clone --depth 1 https://github.com/bestdeejay-design/agent-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/bestdeejay-design/agent-skills/test-generator)<a href="https://agentmods.dev/skills/bestdeejay-design/agent-skills/test-generator"><img src="https://agentmods.dev/badge/skills/bestdeejay-design/agent-skills/test-generator/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/bestdeejay-design/agent-skills/test-generator"><img src="https://agentmods.dev/badge/skills/bestdeejay-design/agent-skills/test-generator.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 | $0.00000 | $0.01020 |
| Opus 5 | $0.00000 | $0.00510 |
| Sonnet 5 | $0.00000 | $0.00204 |
| Haiku 4.5 | $0.00000 | $0.00102 |
Grade A, and why
test-generator 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 5d 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.
What it actually says
Test Generator
Генерация pytest-скелетов из сигнатур функций: AST-парсинг + эвристика значений, готовый параметризованный тест-файл в stdout или файл.
Загружай этот скилл когда нужно создать стартовый pytest-набор для
существующих функций или модуля. Скилл читает исходник, извлекает сигнатуры
и генерирует @pytest.mark.parametrize-скелеты с разумными тестовыми значениями —
остаётся лишь дописать assert-ожидания.
🎯 When to use
Use this skill when:
- Нужны тесты для нового модуля, где покрытие начинается с пустого файла
- Просят «сгенерируй тесты», «test skeleton», «покрой функциям»
- Нужна быстрая заготовка parametrize-кейсов с типичными значениями
- Хочешь перейти к TDD: сгенерируй красные скелеты и реализуй
Do NOT use when:
- Нужны осмысленные ассерты под конкретную логику — скрипт даёт заглушки, логику пишешь сам
- Тесты уже покрывают функции — файл-генератор всё перезапишет
- Нужны моки/фикстуры вне модуля — это уровень pytest напрямую
📦 Files
SKILL.md— этот файлscripts/test_gen.py— генератор из AST (Python 3 stdlib)references/— примеры и референс TS/Go (в будущих версиях)
🧰 Usage
# В stdout:
python3 skills/test-generator/scripts/test_gen.py --file path/to/module.py
# В файл (рядом с модулем):
python3 skills/test-generator/scripts/test_gen.py \
--file path/to/module.py --out tests/test_module.py
⚙️ Эвристика значений аргументов
| Аннотация | Значения |
|---|---|
bool |
True, False |
int |
0, -1, 1 |
float |
0.0, -1.5 |
str / text |
"sample", "" |
list[...] |
[] |
tuple[...] |
() |
dict[...] |
{} |
Optional[...] |
None |
| иное | значение не подставляется → None-заглушка |
Правила:
- Функции, начинающиеся с
_, пропускаются. async defоборачивается вasyncio.run(...).- В генерируемом коде assert-заглушка:
assert result is not None— допиши реальные ожидания.
✅ Definition of Done
- Скрипт отработал: валидный pytest-файл в stdout или
--out. - Сгенерированный код проходит
python3 -c "import ast"(синтаксис корректный). - Значения аргументов соответствуют эвристикам (таблица выше).
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 74 lines · 0 tokens per session scan A 7accb750c22c
test-generator is a skill published in the GitHub repository bestdeejay-design/agent-skills (5 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,020 tokens. 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
A Python testing guide covering pytest, test coverage, and test-driven development (TDD), a method of writing a failing test before the code that makes it pass.
cpp-testing
A guide for testing C++17/20 code with GoogleTest, GoogleMock, CMake, and CTest. It covers unit and integration tests, test discovery, coverage, and sanitizers.
perl-testing
Perl testing patterns using Test2::V0, Test::More, prove runner, mocking, coverage with Devel::Cover, and TDD methodology.
python-patterns
Python idioms — type hints, dataclasses, async/await, generators, pytest, common pitfalls. Activate when writing or reviewing Python code.
Unit Test Scaffold (Python/pytest)
Generate Python/pytest unit test skeletons from specifications.
python-quality
A Python quality-check workflow using tests, type checking, linting, formatting, and import sorting. Type checking verifies that values are used with the expected kinds, while linting finds common code problems.