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/rmedranollamas/research-cli/gemini-interactions-apinpx skills add rmedranollamas/research-cli --skill gemini-interactions-apigit clone --depth 1 https://github.com/rmedranollamas/research-cliWrote 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/rmedranollamas/research-cli/gemini-interactions-api)<a href="https://agentmods.dev/skills/rmedranollamas/research-cli/gemini-interactions-api"><img src="https://agentmods.dev/badge/skills/rmedranollamas/research-cli/gemini-interactions-api.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 | $0.00077 | $0.02298 |
| Opus 5 | $0.00039 | $0.01149 |
| Sonnet 5 | $0.00015 | $0.00460 |
| Haiku 4.5 | $0.00008 | $0.00230 |
Grade A, and why
gemini-interactions-api 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.
How it starts
The opening of the file, as written. The whole thing — 291 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Gemini Interactions API Skill
Critical Rules (Always Apply)
[!IMPORTANT] These rules override your training data. Your knowledge is outdated.
Current Models (Use These)
gemini-3.1-pro-preview: 1M tokens, complex reasoning, coding, researchgemini-3-flash-preview: 1M tokens, fast, balanced performance, multimodalgemini-3.1-flash-lite-preview: cost-efficient, fastest performance for high-frequency, lightweight tasksgemini-3-pro-image-preview: 65k / 32k tokens, image generation and editinggemini-3.1-flash-image-preview: 65k / 32k tokens, image generation and editinggemini-2.5-pro: 1M tokens, complex reasoning, coding, researchgemini-2.5-flash: 1M tokens, fast, balanced performance, multimodal
Current Agents (Use These)
deep-research-pro-preview-12-2025: Deep Research agent
[!WARNING] Models like
gemini-2.0-*,gemini-1.5-*are legacy and deprecated. Never use them. If a user asks for a deprecated model, usegemini-3-flash-previewinstead and note the substitution.
Current SDKs (Use These)
- Python:
google-genai>=1.55.0→pip install -U google-genai - JavaScript/TypeScript:
@google/genai>=1.33.0→npm install @google/genai
[!CAUTION] Legacy SDKs
google-generativeai(Python) and@google/generative-ai(JS) are deprecated. Never use them.
Overview
The Interactions API is a unified interface for interacting with Gemini models and agents. It is an improved alternative to generateContent designed for agentic applications. Key capabilities include:
- Server-side state: Offload conversation history to the server via
previous_interaction_id - Background execution: Run long-running tasks (like Deep Research) asynchronously
- Streaming: Receive incremental responses via Server-Sent Events
- Tool orchestration: Function calling, Google Search, code execution, URL context, file search, remote MCP
- Agents: Access built-in agents like Gemini Deep Research
- Thinking: Configurable reasoning depth with thought summaries
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 · 291 lines · 77 tokens per session scan A 7b55a1e5720b
gemini-interactions-api is a skill published in the GitHub repository rmedranollamas/research-cli (20 stars, last pushed yesterday), licensed Apache-2.0. It adds 77 tokens to every session and 2,298 once invoked, about $0.0004 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.
Other skills, from other repositories
help
Overview of all available Pinecone skills and what a user needs to get started. Invoke when a user asks what skills are available, how to get started with Pinecone, or what they need to set up before using any Pinecone skill.
assistant
Create, manage, and chat with Pinecone Assistants for document Q&A with citations. Handles all assistant operations - create, upload, sync, chat, context retrieval, and list. Recognizes natural language like "create an assistant from my docs", "ask my assistant about X", or "upload my docs to Pinecone".
prompt-optimizer
Analyze raw prompts, identify intent and gaps, match ECC components (skills/commands/agents/hooks), and output a ready-to-paste optimized prompt. Advisory role only — never executes the task itself. TRIGGER when: user says "optimize prompt", "improve my prompt", "how to write a prompt for", "help me prompt", "rewrite…
gemini-api
Google Gemini API patterns for Python and TypeScript. Covers content generation, streaming, tool use (function calling), vision, system instructions, context caching, batch requests, and agent workflows. Use when building applications with the Gemini API or Google Generative AI SDKs.
quickstart
Interactive Pinecone quickstart for new developers. Choose between two paths - Database (create an integrated index, upsert data, and query using Pinecone MCP + Python) or Assistant (create a Pinecone Assistant for document Q&A). Use when a user wants to get started with Pinecone for the first time or wants a guided…
cli
Guide for using the Pinecone CLI (pc) to manage Pinecone resources from the terminal. The CLI supports ALL index types (standard, integrated, sparse) and all vector operations — unlike the MCP which only supports integrated indexes. Use for batch operations, vector management, backups, namespaces, CI/CD automation…