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 UitbreidenOS/UitKit --skill numpy-vector-array-computationsgit clone --depth 1 https://github.com/UitbreidenOS/UitKitWrote 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/uitbreidenos/uitkit/numpy-vector-array-computations)<a href="https://agentmods.dev/skills/uitbreidenos/uitkit/numpy-vector-array-computations"><img src="https://agentmods.dev/badge/skills/uitbreidenos/uitkit/numpy-vector-array-computations/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/uitbreidenos/uitkit/numpy-vector-array-computations"><img src="https://agentmods.dev/badge/skills/uitbreidenos/uitkit/numpy-vector-array-computations.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 | $0.00017 | $0.00150 |
| Opus 5.5 | $0.00007 | $0.00060 |
| Sonnet 5.5 | $0.00003 | $0.00030 |
| Haiku 4.5 | $0.00002 | $0.00015 |
Grade A, and why
numpy-vector-array-computations 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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 · 28 lines · 17 tokens per session scan A f9b49462c892
numpy-vector-array-computations is a skill published in the GitHub repository UitbreidenOS/UitKit (14 stars, last pushed 3mo ago), with no licence file. It adds 17 tokens to every session and 150 once invoked, about $0.0001 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.
Other skills, from other repositories
agent-django-fastapi-expert
Specialist subagent: Django ORM, Django REST Framework, FastAPI async patterns, and Pydantic v2 specialist. Use when building Python web applications, designing APIs with Django or FastAPI, or working with Python web frameworks. Trigger phrases: Django, FastAPI, DRF, Django REST Framework, Pydantic, ORM, queryset…
ai-ml-development
AI and machine learning development with PyTorch, TensorFlow, and LLM integration. Use when building ML models, training pipelines, fine-tuning LLMs, or implementing AI features.
agent-python-expert
Specialist subagent: Python best practices, type hints, async patterns, and ecosystem expertise. Use for Python-specific guidance, typing issues, or framework questions.
android-development
Android development with Kotlin, Jetpack Compose, and modern Android architecture. Use when building Android apps, implementing Material Design, or following Android best practices.
ios-development
Comprehensive guide for building native Apple platform applications.
macos-native
Native macOS development with AppKit, Catalyst, and macOS-specific APIs. Use when building Mac-native apps, menu bar apps, system extensions, or macOS-specific features.