Gemini Multimodal Researcher
Leverage Gemini's multimodal capabilities to analyze images, documents, audio, and video alongside text for comprehensive research.
While optimized for Gemini 3, this prompt is compatible with most major AI models.
A prompt that triggers a recursive loop where Gemini 3 researches a topic, identifies knowledge gaps, and asks itself follow-up questions.
Standard search gives you links. Gemini 3 can browse, read, and cross-reference thousands of sources. This prompt sets up a recursive loop where the model acts as an autonomous researcher, asking itself follow-up questions to build a PhD-level thesis without user intervention.
Edit the prompt above then feed it directly to your favorite AI model
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Leverage Gemini's multimodal capabilities to analyze images, documents, audio, and video alongside text for comprehensive research.
Analyzes large documents (up to 256k tokens) to extract insights, find patterns, and answer complex questions across the entire content.
Synthesizes information from multiple sources (papers, articles, videos) into coherent insights, identifying agreements, contradictions, and gaps.
Synthesize long-form historical research from multiple sources, identifying patterns, causal relationships, and historiographical debates with comprehensive source analysis.
Leverage Gemini's multimodal capabilities to analyze images, documents, audio, and video alongside text for comprehensive research.
Conduct comprehensive multi-source research and synthesize findings into cohesive reports, leveraging Opus 4.5s advanced reasoning for complex information retrieval and analysis.
Agent communication platform for agent to agent messaging via MCP. Messages, channels, skills.
An AI agent is a system that uses a language model to perceive its environment, make decisions, and take actions to reach a goal. Unlike a simple chatbot that only responds to prompts, an agent can loop: observe state, plan next steps, call tools, review results, and adapt until the task is done. Agents are built from several components. A planner breaks a goal into subtasks. A memory module stores conversation history and working context. A tool interface lets the agent call APIs, run code, query databases, or interact with other systems. A feedback loop checks whether each step moved the agent closer to the goal. Simple agents might answer a question by searching the web. Complex agents can write and test code, file pull requests, or coordinate with other agents. The more autonomy an agent has, the more important safety guardrails become, such as human approval for destructive actions and clear logging for every decision.
Analyzes large documents (up to 256k tokens) to extract insights, find patterns, and answer complex questions across the entire content.