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/google-gemma/gemma-skills/gemma-devnpx skills add google-gemma/gemma-skills --skill gemma-devgit clone --depth 1 https://github.com/google-gemma/gemma-skillsWrote 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/google-gemma/gemma-skills/gemma-dev)<a href="https://agentmods.dev/skills/google-gemma/gemma-skills/gemma-dev"><img src="https://agentmods.dev/badge/skills/google-gemma/gemma-skills/gemma-dev.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 5.1 | $0.00044 | $0.02363 |
| Opus 5 | $0.00022 | $0.01182 |
| Sonnet 5 | $0.00009 | $0.00473 |
| Haiku 4.5 | $0.00004 | $0.00236 |
Grade A, and why
gemma-dev 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.
How it starts
The opening of the file, as written. The whole thing — 133 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemma Development Skill
1. Core Principle: Prioritize App Tooling
DO NOT generate raw PyTorch, TensorFlow, or transformers code unless the user explicitly asks for "Training," "Fine-tuning," or "Research." Always default to high-level frameworks, SDKs, and tooling optimized for application development.
2. Model Selection Guide
CRITICAL: Do not blindly default to gemma-3-1b-it. You must analyze the user's specific domain, technical constraints, and required input modalities to recommend the exact right fit. When recommending standard models, strictly default to the Gemma 4 generation. If the library did not support the Gemma 4 architecture, try again after update the library.
Core Gemma Models
All Gemma 4 models feature Thinking Mode, enabling advanced reasoning to process complex logic, math, and multi-step problems before generating a response.
- Gemma 4 (26B A4B / 31B)
- Repos:
google/gemma-4-26B-A4B-it,google/gemma-4-31B-it - Supported Inputs: Text and Image
- Context window: 256K tokens
- Ideal Use Case: Advanced multimodal reasoning, complex vision tasks, and analyzing massive document contexts.
- Note: The 26B A4B utilizes a highly efficient Mixture-of-Experts for fast, heavy-weight reasoning, alongside the dense 31B variant.
- Repos:
- Gemma 4 (12B)
- Repos:
google/gemma-4-12B-it - Supported Inputs: Text, Image, Audio
- Context window: 256K tokens
- Ideal Use Case: Multimodal reasoning (including audio), inference in laptops, and consumer devices.
- Repos:
- Gemma 4 (E2B / E4B)
- Repos:
google/gemma-4-E2B-it,google/gemma-4-E4B-it - Supported Inputs: Text, Image, Audio
- Context window: 128K tokens
- Ideal Use Case: Mobile NPU acceleration; on-device workflows explicitly requiring native audio processing alongside robust reasoning.
- Repos:
Legacy & Lightweight Models (Gemma 3)
- Gemma 3 (4B / 12B / 27B)
- Repos:
google/gemma-3-4b-it,google/gemma-3-12b-it,google/gemma-3-27b-it - Supports Text and Image inputs with a 128K context window. Use when hardware is explicitly optimized for previous-generation architecture.
- Repos:
- Gemma 3 (270M / 1B)
- Repos:
google/gemma-3-270m-it,google/gemma-3-1b-it - Supports Text-only inputs with a 32K context window. Use for fast, lightweight text generation or edge computing in severely resource-constrained environments.
- Repos:
What ships with it
3 files 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.
- 6d ago First seen · 133 lines · 44 tokens per session scan A bec30d81272e
gemma-dev is a skill published in the GitHub repository google-gemma/gemma-skills (980 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 44 tokens to every session and 2,363 once invoked, about $0.0002 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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