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 personamanagmentlayer/pcl --skill 5g-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/5g-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/5g-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/5g-expert/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/personamanagmentlayer/pcl/5g-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/5g-expert.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 | $0.00081 | $0.01175 |
| Opus 5 | $0.00041 | $0.00588 |
| Sonnet 5 | $0.00016 | $0.00235 |
| Haiku 4.5 | $0.00008 | $0.00118 |
Grade A, and why
5g-expert 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.
How it starts
The opening of the file, as written. The whole thing — 175 lines — stays where its author put it; the contents beside it link to each section on GitHub.
5G Expert
Design and deploy production-ready 5G network solutions including network slicing, edge computing, and ultra-low latency applications.
Learning Objectives
- Master 5G network architecture and components
- Implement network slicing for service differentiation
- Deploy Multi-access Edge Computing (MEC) applications
- Optimize for ultra-low latency use cases (URLLC)
- Integrate IoT devices with 5G networks
Prerequisites
- Strong understanding of networking protocols
- Knowledge of SDN and NFV concepts
- Familiarity with cloud-native architectures
- Understanding of wireless communication principles
Core Concepts
5G Network Architecture
Next-generation mobile network with service-based architecture (SBA), network functions virtualization (NFV), and software-defined networking (SDN). Supports enhanced mobile broadband (eMBB), ultra-reliable low-latency communication (URLLC), and massive machine-type communication (mMTC).
Network Slicing
Logical end-to-end networks running on shared physical infrastructure. Each slice optimized for specific service requirements (latency, bandwidth, reliability) enabling customized connectivity for diverse use cases.
Multi-Access Edge Computing (MEC)
Computing resources deployed at network edge near 5G base stations. Enables ultra-low latency applications by processing data locally instead of routing to distant cloud data centers.
Ultra-Reliable Low-Latency Communication (URLLC)
5G service category targeting <1ms latency and 99.999% reliability. Critical for industrial automation, autonomous vehicles, remote surgery, and real-time control systems.
Massive IoT (mMTC)
Support for millions of connected devices per square kilometer with optimized power consumption. Enables smart cities, agriculture, environmental monitoring, and industrial IoT applications.
Best Practices
Network Slicing Design
- Define clear service level agreements (SLAs) for each slice
- Implement resource isolation between slices
- Use dynamic resource allocation based on demand
- Monitor slice performance continuously
- Implement automated scaling and healing
- Design for multi-tenancy security
- Plan for slice lifecycle management
What ships with it
1 file 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 · 175 lines · 81 tokens per session scan A ee084f7b5be2
5g-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed 2d ago), licensed Apache-2.0. It adds 81 tokens to every session and 1,175 once invoked, about $0.0004 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 2026-09-05.
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