gemma-skills is a collection of skills for coding assistants that work with Gemma models and model-agent interactions. It supports building applications with Gemma and training or fine-tuning Gemma models through methods such as supervised fine-tuning, DPO, RLHF, and reward modeling. The catalogue entries are the project's skills for these workflows.
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 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/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/google-gemma/gemma-skills/gemma-dev"><img src="https://agentmods.dev/badge/skills/google-gemma/gemma-skills/gemma-dev.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- 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 | $0.00044 | $0.02354 |
| Opus 5.5 | $0.00018 | $0.00942 |
| Sonnet 5.5 | $0.00009 | $0.00471 |
| Haiku 4.5 | $0.00004 | $0.00235 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- gemma-dev — 100% identical, 37 lines differ
How it starts
The opening of the file, as written. The whole thing — 134 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.
- 5d ago Changed 37f36759f64f
- 17d ago Changed · +1 lines 16368b2e59d4
- 1mo 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 (1,030 stars, last pushed 5d ago), licensed Apache-2.0. It adds 44 tokens to every session and 2,354 once invoked, about $0.0002 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
agent-platform-tuning
Agent Platform Model Tuning. Use when you need to fine-tune open models or Gemini models using Agent Platform infrastructure. Don't use for model training outside Agent Platform, model deployment to endpoints (use agent-platform-deploy), or managing serving endpoints (use agent-platform-endpoint-management).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
agent-platform-prompt-management
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-inference
Deploys and optimizes AI/ML inference workloads on GKE, using GPUs, TPUs, and model servers. Use when deploying GKE inference servers, configuring GKE GPU resources for inference, or deploying LLMs on GKE. Don't use for migrating existing AI workloads to GKE (use google-cloud-solution-guided-gke-ai-migration), GKE RAG…
agent-platform-endpoint-management
Manages Agent Platform serving endpoints. Use when you need to create, list, describe, update, or delete serving endpoints for model deployment on Agent Platform. Also use when troubleshooting endpoint permission, quota, or resource busy errors. Don't use for deploying models to endpoints or for running model…