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 agentmods add skills/wentorai/research-plugins/generative-ai-guidenpx skills add wentorai/research-plugins --skill generative-ai-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote 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/wentorai/research-plugins/generative-ai-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/generative-ai-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/generative-ai-guide.svg" alt="Measured on agentmods" 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 | $0.00019 | $0.01489 |
| Opus 5 | $0.00010 | $0.00745 |
| Sonnet 5 | $0.00004 | $0.00298 |
| Haiku 4.5 | $0.00002 | $0.00149 |
Grade A, and why
generative-ai-guide 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 — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generative AI Guide
A skill providing a comprehensive, curated guide to generative AI research and practice, covering large language models (LLMs), diffusion models, transformer architectures, prompt engineering, and evaluation methodologies. Based on the awesome-generative-ai-guide repository (25K stars), this skill equips researchers with structured knowledge of the rapidly evolving generative AI landscape.
Overview
Generative AI has become one of the most active areas of research across computer science, with implications spanning natural language processing, computer vision, audio synthesis, code generation, scientific discovery, and creative applications. The pace of development makes it challenging for researchers to maintain a current understanding of the field. This skill provides a structured map of the generative AI landscape, organized by topic and application area, with guidance on key papers, methods, and practical considerations.
Whether you are an AI researcher staying current with the field, a domain scientist exploring how generative AI can accelerate your work, or a student entering the field, this skill provides the orientation and resources needed to navigate the space effectively.
Large Language Models
Architecture Foundations
- Transformer architecture: self-attention mechanism, positional encoding, layer normalization
- Scaling laws: the relationship between model size, data, compute, and performance
- Training objectives: causal language modeling, masked language modeling, instruction tuning
- Context windows: evolution from 512 tokens to 100K+ tokens and associated techniques
- Mixture of Experts (MoE): sparse activation for efficient scaling
Key Model Families
- GPT series (OpenAI): decoder-only architecture, scaling-driven approach
- Claude series (Anthropic): emphasis on safety, instruction following, and long context
- Llama series (Meta): open-weight models enabling community research
- Gemini series (Google): multimodal from the ground up
- Open-source ecosystem: Mistral, Qwen, DeepSeek, and community fine-tunes
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 · 140 lines · 19 tokens per session scan A 3382e4e145c4
generative-ai-guide is a skill published in the GitHub repository wentorai/research-plugins (287 stars, last pushed 2mo ago), licensed MIT. It adds 19 tokens to every session and 1,489 once invoked, about $0.0001 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 2026-08-30.
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