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 id-andyyy/LabAI --skill lab-setupgit clone --depth 1 https://github.com/id-andyyy/LabAIWrote 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/id-andyyy/labai/lab-setup)<a href="https://agentmods.dev/skills/id-andyyy/labai/lab-setup"><img src="https://agentmods.dev/badge/skills/id-andyyy/labai/lab-setup/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/id-andyyy/labai/lab-setup"><img src="https://agentmods.dev/badge/skills/id-andyyy/labai/lab-setup.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 | $0.00008 | $0.02993 |
| Opus 5.5 | $0.00003 | $0.01197 |
| Sonnet 5.5 | $0.00002 | $0.00599 |
| Haiku 4.5 | $0.00001 | $0.00299 |
Grade A, and why
lab-setup 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Пользователь хочет настроить проект для выполнения лабораторной работы. Нужно проверить окружение, собрать данные о студенте и работе, распарсить задание и сохранить всё в config.json.
Что делать
Шаг 1. Проверка Python
Проверь наличие Python:
- Выполни
python3 --version(если не найден — попробуйpython --version). - Если Python найден — запомни путь (например,
python3). - Если Python не найден — сообщи пользователю, что генерация DOCX/PDF не будет работать, а парсинг PDF/DOCX потребует ручного ввода текста. Продолжай настройку.
Шаг 2. Создание venv и установка зависимостей
Если Python найден:
- Создай виртуальное окружение:
python3 -m venv .claude/lab/venv - Установи зависимости:
.claude/lab/venv/bin/pip install -r .claude/lab/scripts/requirements.txt - На Windows пути через
Scripts/вместоbin/.
Если Python не найден — пропусти этот шаг.
Шаг 3. Опрос пользователя
Если config.json уже существует (.claude/lab/config.json) — прочитай его, покажи текущие значения и спроси, хочет ли пользователь что-то изменить. Если хочет — задай только те вопросы, которые нужно обновить.
Если config.json не существует — задай все вопросы через AskUserQuestion (разбей на блоки):
Блок 1 — студент и учебное заведение:
- ФИО студента.
- Группа студента.
- Полное название учебного заведения.
- Город.
Блок 2 — предмет и работа:
- Название предмета/курса.
- Тип работы: Лабораторная работа / Практическая работа / Задание (для титульного листа). По умолчанию — «Лабораторная работа».
- Номер лабораторной работы.
- Название лабораторной работы (или пусто — определим из задания).
- ФИО преподавателя (рецензента) — опционально, для титульного листа.
- ОС пользователя, на которой он будет работать (Windows / macOS / Linux — с версией, если знает).
Блок 3 — предпочтения:
- Формат итогового отчёта: docx / pdf / md.
- Имя итогового файла (без расширения). По умолчанию —
report. Примеры:Лабораторная_работа_3,ЛР3_Иванов. - Стиль отчёта: первое лицо («я выполнил, я настроил») / безличное («было выполнено, было настроено»).
- Нужно ли дополнительное исследование в отчёте? (Да / Нет)
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 · 241 lines · 8 tokens per session scan A df6adc3439af
lab-setup is a skill published in the GitHub repository id-andyyy/LabAI (3 stars, last pushed 5mo ago), licensed MIT. It adds 8 tokens to every session and 2,993 once invoked, about $0.0000 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 2026-08-31.
Other skills, from other repositories
offensive-vuln-classes
Exploit development curriculum covering core vulnerability classes with real-world CVE case studies: stack/heap buffer overflows, use-after-free, integer overflows, format strings, type confusion, and race conditions. Use when learning or teaching vuln classes, researching specific CVE patterns, or building exploit…
model-prompt-pack
Use when asked to write prompts for image-to-3D or text-to-3D tools, generate 3D models of a topic's parts, plan the assets for a 3D explorer, or build a labelled parts list with sources. Produces prompts for image-to-3D tools, one per object, plus a parts list for labelling in which every label names a trusted…
explorer-interface-brief
Use when asked to design the interface for an interactive 3D explorer, plan a UI for exploring a 3D model of a topic, or write an image-model prompt for a 3D learning app mock-up. Produces, from a topic, the UI design brief (audience, layout, panels, interactions, label style, colour palette, accessibility) and an…
explorer-pipeline
Use when asked to make an interactive 3D explorer of a topic from start to finish, go from a topic to a clickable 3D learning page, or run the whole 3D explorer process. Produces a staged run of explorer-interface-brief, model-prompt-pack and explorer-viewer-builder in order for one topic, with the hand-offs between…
spot-ai-mistakes
Learn to recognize where and how AI tends to go wrong — the specific failure patterns — so you catch its mistakes on sight instead of getting burned by confident errors. Use when asked how do I know when AI is wrong, what are AI's common mistakes, how do I catch AI errors, or where does AI mess up. Produces the…
financial-statement-explainer
Explain a financial statement (P&L, balance sheet, or cash flow) in plain English. Use when asked to explain a P&L / income statement, a balance sheet, a cash flow statement, or to make financials understandable to a non-finance reader. Produces a plain-language walkthrough — what each section means, the line items…