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 hybridlabor-api/bdb-dev-optimized-agent-skills --skill gemini-api-devgit clone --depth 1 https://github.com/hybridlabor-api/bdb-dev-optimized-agent-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/hybridlabor-api/bdb-dev-optimized-agent-skills/gemini-api-dev)<a href="https://agentmods.dev/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/gemini-api-dev"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-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/hybridlabor-api/bdb-dev-optimized-agent-skills/gemini-api-dev"><img src="https://agentmods.dev/badge/skills/hybridlabor-api/bdb-dev-optimized-agent-skills/gemini-api-dev.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 | $0.00021 | $0.01261 |
| Opus 5 | $0.00010 | $0.00630 |
| Sonnet 5 | $0.00004 | $0.00252 |
| Haiku 4.5 | $0.00002 | $0.00126 |
Grade A, and why
gemini-api-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.
This is a copy
98% identical to gemini-api-dev — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini API Development Skill
Overview
The Gemini API provides access to Google's most advanced AI models. Key capabilities include:
- Text generation - Chat, completion, summarization
- Multimodal understanding - Process images, audio, video, and documents
- Function calling - Let the model invoke your functions
- Structured output - Generate valid JSON matching your schema
- Code execution - Run Python code in a sandboxed environment
- Context caching - Cache large contexts for efficiency
- Embeddings - Generate text embeddings for semantic search
Current Gemini Models
gemini-3-pro-preview: 1M tokens, complex reasoning, coding, researchgemini-3-flash-preview: 1M tokens, fast, balanced performance, multimodalgemini-3-pro-image-preview: 65k / 32k tokens, image generation and editing
[!IMPORTANT] Models like
gemini-2.5-*,gemini-2.0-*,gemini-1.5-*are legacy and deprecated. Use the new models above. Your knowledge is outdated.
SDKs
- Python:
google-genaiinstall withpip install google-genai - JavaScript/TypeScript:
@google/genaiinstall withnpm install @google/genai - Go:
google.golang.org/genaiinstall withgo get google.golang.org/genai
[!WARNING] Legacy SDKs
google-generativeai(Python) and@google/generative-ai(JS) are deprecated. Migrate to the new SDKs above urgently by following the Migration Guide.
Quick Start
Python
from google import genai
client = genai.Client()
response = client.models.generate_content(
model="gemini-3-flash-preview",
contents="Explain quantum computing"
)
print(response.text)
JavaScript/TypeScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({});
const response = await ai.models.generateContent({
model: "gemini-3-flash-preview",
contents: "Explain quantum computing"
});
console.log(response.text);
Go
package main
import (
"context"
"fmt"
"log"
"google.golang.org/genai"
)
func main() {
ctx := context.Background()
client, err := genai.NewClient(ctx, nil)
if err != nil {
log.Fatal(err)
}
resp, err := client.Models.GenerateContent(ctx, "gemini-3-flash-preview", genai.Text("Explain quantum computing"), nil)
if err != nil {
log.Fatal(err)
}
fmt.Println(resp.Text)
}
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 First seen · 140 lines · 21 tokens per session scan A c71810df4e2b
gemini-api-dev is a skill published in the GitHub repository hybridlabor-api/bdb-dev-optimized-agent-skills (6 stars, last pushed 4d ago), licensed Apache-2.0. It adds 21 tokens to every session and 1,261 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 98% identical to gemini-api-dev, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
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Mem0 memory layer integration for AI agents. Implement persistent, semantic memory for long-term context retention and personalization.
chain-of-thought-prompts
Chain-of-thought and step-by-step reasoning prompts for complex problem solving.
chroma-integration
Chroma local vector database setup and operations for development and production.
few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance.
haystack-pipeline
Haystack NLP pipeline configuration for document processing and QA.