Genkit is an open-source framework for building AI-powered applications with JavaScript, Go, Python, and Dart. Developers use it to connect language models and other AI providers while implementing chatbots, automations, recommendations, multimodal features, structured outputs, tools, and agent workflows. The catalogue skills support development with Genkit.
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 genkit-ai/genkit --skill eli5git clone --depth 1 https://github.com/genkit-ai/genkitWrote 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/genkit-ai/genkit/eli5)<a href="https://agentmods.dev/skills/genkit-ai/genkit/eli5"><img src="https://agentmods.dev/badge/skills/genkit-ai/genkit/eli5/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/genkit-ai/genkit/eli5"><img src="https://agentmods.dev/badge/skills/genkit-ai/genkit/eli5.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 | $0.00016 | $0.00102 |
| Opus 5.5 | $0.00006 | $0.00041 |
| Sonnet 5.5 | $0.00003 | $0.00020 |
| Haiku 4.5 | $0.00002 | $0.00010 |
Grade A, and why
eli5 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.
What it actually says
You are a patient teacher explaining things so a five-year-old could understand.
Rules:
- Use simple words and short sentences.
- Prefer everyday analogies — toys, food, pets, games.
- Avoid jargon entirely. If a technical term is unavoidable, immediately restate it in plainer words.
- Keep the response under ten sentences.
Stay warm and encouraging throughout.
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 · 15 lines · 16 tokens per session scan A 584bcbf6e905
eli5 is a skill published in the GitHub repository genkit-ai/genkit (6,523 stars, last pushed yesterday), licensed Apache-2.0. It adds 16 tokens to every session and 102 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 2026-08-30.
Other skills, from other repositories
playwright
Use when the task requires capturing or automating a real browser from the terminal.
extract-wisdom
Extract wisdom, insights, and actionable takeaways from YouTube videos, blog posts, articles, or text files. Use when asked to extract wisdom or key insights from a given content source.
transformer-attention
Use when reasoning about Transformer self-attention, multi-head attention, positional encoding, masked decoder attention, or why attention replaced recurrence/convolutions in sequence models; not for generic NLP or unrelated attention topics.
ml-llm-wiki
Use when answering questions from this machine-learning knowledge base. Triggers: questions about transformers, attention cost and efficiency, and long-context scaling; 'what do we know about attention', 'check the ML wiki'. Read-only querying of compiled knowledge; to add, update, supersede, lint, audit, or critique…
notebooklm
Programmatic access to Google NotebookLM for creating notebooks, adding sources (URLs, YouTube, PDFs, audio, video, images), chatting with content, generating artifacts (podcasts, videos, reports, quizzes, mind maps, flashcards, infographics), and downloading results. Includes capabilities beyond the web UI.
explain
Explain a concept, system or piece of code as a diagram-first terminal answer, or as an interactive HTML page.