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 DaviBonetto/multi-agent-skill-factory --skill text_generationgit clone --depth 1 https://github.com/DaviBonetto/multi-agent-skill-factoryWrote 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/davibonetto/multi-agent-skill-factory/text_generation)<a href="https://agentmods.dev/skills/davibonetto/multi-agent-skill-factory/text_generation"><img src="https://agentmods.dev/badge/skills/davibonetto/multi-agent-skill-factory/text_generation/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/davibonetto/multi-agent-skill-factory/text_generation"><img src="https://agentmods.dev/badge/skills/davibonetto/multi-agent-skill-factory/text_generation.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 | $0.02795 |
| Opus 5 | $0.00000 | $0.01398 |
| Sonnet 5 | $0.00000 | $0.00559 |
| Haiku 4.5 | $0.00000 | $0.00280 |
Grade A, and why
TEXT_GENERATION 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 6d 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.
- 6d ago First seen · 388 lines · 0 tokens per session scan A c8365fc8f57e
TEXT_GENERATION is a skill published in the GitHub repository DaviBonetto/multi-agent-skill-factory (5 stars, last pushed 24d ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 2,795 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
seo-llm
Use when optimizing content for LLM-powered search engines (ChatGPT, Perplexity, Gemini, Claude, Bing AI, Qwen), implementing RAG optimization, prompt engineering for search visibility, semantic SEO, and ensuring content ranks highly in AI-driven search results. Includes techniques for ChatGPT SEO, Perplexity…
langchain
Use when building LLM applications with LangChain, implementing chains, agents, tools, memory, prompts, and retrieval systems. Includes best practices for prompt engineering, tool integration, and agent development. Based on LangChain/LangGraph official documentation and agent development best practices.
llm-integrations
Use when integrating LLM providers (OpenAI, DeepSeek, OpenRouter, Anthropic, Google), configuring API keys, optimizing costs, implementing rate limiting, and managing LLM usage across projects. Includes best practices for cost optimization and API management. Based on OpenAI, Anthropic, Google, and other LLM provider…
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt needs clearer production wording, less generic prose, or precise vocabulary for camera, lighting, motion, VFX, audio, and constraints. Route blocked prompts through seedance-filter for context and boundary review.
omh-model-optimization
This is a Hermes-native model-optimization workflow skill.
llm-router
Selects the optimal LLM model and provider for each task based on complexity, cost budget, and capability requirements. Routes cheap tasks to Haiku/GPT-4o-mini and complex tasks to Sonnet/Opus/o1. Use when deciding which model to call, optimizing LLM costs, or building multi-model agent systems. Activate on "which…