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 oyi77/1ai-skills --skill gemini-api-devgit clone --depth 1 https://github.com/oyi77/1ai-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/oyi77/1ai-skills/gemini-api-dev)<a href="https://agentmods.dev/skills/oyi77/1ai-skills/gemini-api-dev"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/gemini-api-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/oyi77/1ai-skills/gemini-api-dev"><img src="https://agentmods.dev/badge/skills/oyi77/1ai-skills/gemini-api-dev.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 | $0.00052 | $0.02896 |
| Opus 5.5 | $0.00021 | $0.01158 |
| Sonnet 5.5 | $0.00010 | $0.00579 |
| Haiku 4.5 | $0.00005 | $0.00290 |
Grade A, and why
gemini-api-dev scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
| "I will just use curl" | SDK handles retries, streaming, auth, and type safety | How it starts
The opening of the file, as written. The whole thing — 283 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Api Dev
When to Use
Trigger phrases:
-
"gemini api dev"
-
"When building apps powered by Google Gemini models"
-
"When processing multimodal inputs (text + images + audio + video)"
-
"When implementing function calling or tool use with Gemini"
-
When building apps powered by Google Gemini models
-
When processing multimodal inputs (text + images + audio + video)
-
When implementing function calling or tool use with Gemini
-
When using Gemini for grounding with Google Search
When NOT to Use
- For OpenAI-compatible APIs (use OpenAI skills)
- For local model inference (use Ollama skills)
- For fine-tuning (use Vertex AI training skills)
Overview
The Google Gemini API provides access to Google's most capable AI models — Gemini 2.5 Pro, 2.5 Flash, and Gemini Nano — through a unified SDK. It supports text generation, multimodal understanding (images, audio, video, PDFs), function calling, and grounding with Google Search. The API is available via the google-genai Python SDK and @google/genai TypeScript SDK, both providing first-class async support, streaming generators, and type-safe response handling.
Gemini 2.5 Pro excels at complex reasoning, code generation, and multi-turn conversations with a 1-million-token context window. Gemini 2.5 Flash is optimized for speed and cost-efficiency while maintaining strong reasoning capabilities, making it suitable for high-volume production workloads. Nano models run on-device for Android and Chrome, handling summarization, smart reply, and text classification without network calls.
The API architecture follows a content-based message model: each request sends contents arrays with role (user or model) and parts — which can be text, inline_data (base64-encoded images/audio), file_data (PDFs, videos), function_call, or function_response. Configuration options include systemInstruction for persona setting, temperature (0.0–2.0), topP, topK, maxOutputTokens, stopSequences, and safetySettings for content filtering.
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 · 283 lines · 52 tokens per session scan A e1b084f8c2bc
gemini-api-dev is a skill published in the GitHub repository oyi77/1ai-skills (16 stars, last pushed 2d ago), licensed MIT. It adds 52 tokens to every session and 2,896 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
execution-hygiene
Runs computations so they finish, reproduce and fit the machine that will re-execute them, detaching long jobs through computejob instead of blocking or sleeping shells, checkpointing so reruns resume, fixing seeds and capping BLAS and OpenMP threads, streaming large tables with explicit dtypes, writing outputs…
generate-image
Generates or edits images with the native generateimage tool (Nano Banana Pro through Ace or a Gemini key, GPT Image 2 through an OpenAI key), for illustrations, cover art, graphical abstracts, conceptual visuals, photo-style figures and edits of existing images, saved straight into the workspace and checked before…
awesome-chatgpt
Search this repository's awesome-ChatGPT-repositories list for open-source ChatGPT and LLM projects such as RAG frameworks, agents, chatbots, CLIs, prompts, and browser extensions. Use when the user asks to find tools, libraries, or repos related to ChatGPT, LLMs, or AI development; not for editing or adding list…
langchain
Use when building LLM applications with LangChain — LCEL chains (prompt | model | parser), tool-calling agents via createagent, @tool definitions, RAG pipelines (loaders, splitters, vector stores, retrievers), short-term memory with LangGraph checkpointers, and GIS workflows such as natural-language PostGIS queries or…
llamaindex
Use when building RAG and agent applications over documents with LlamaIndex — SimpleDirectoryReader loading, node parsers, VectorStoreIndex with local or external vector stores (Chroma, PGVector, Qdrant), query engines and response modes, chat engines, RouterQueryEngine routing, event-driven Workflows, and…
LLM
Implement large language model (LLM) chat completions using the z-ai-web-dev-sdk. Use this skill when the user needs to build conversational AI applications, chatbots, AI assistants, or any text generation features. Supports multi-turn conversations, system prompts, and context management.