Automates research tasks using multiple Claude agents. Operations teams benefit from parallel execution, audit trails, and architectural enforcement. Connects to Python workflows and integrates with Claude Code Skills.
git clone https://github.com/ahmedibrahim085/Claude-Multi-Agent-Research-System-Skill.gitThe Claude-Multi-Agent-Research-System-Skill is an innovative automation tool designed to enhance research workflows through the integration of multiple AI agents. Drawing inspiration from the Anthropic claude-agent-sdk-demos, this skill allows users to orchestrate various AI agents to collaboratively gather, analyze, and synthesize information, significantly improving the research process. By leveraging Claude Code, users can automate repetitive tasks and focus on higher-level analysis, making research more efficient and effective. One of the key benefits of implementing this skill is the potential for substantial time savings in research activities. While the exact time savings are currently unknown, the automation of multi-agent interactions can reduce manual effort, allowing researchers to complete projects faster. This skill is particularly valuable for developers, product managers, and AI practitioners who are involved in data-intensive research projects. By utilizing this skill, teams can enhance their productivity and streamline their workflows, ultimately leading to faster decision-making and improved outcomes. The Claude-Multi-Agent-Research-System-Skill is well-suited for those looking to implement AI automation in their research processes. It is particularly beneficial for roles that require extensive data analysis, such as data scientists and AI researchers. For example, a product manager could use this skill to automate the collection of market research data from various sources, enabling them to quickly synthesize insights and inform product development strategies. Similarly, AI practitioners can leverage this skill to automate the testing of multiple hypotheses in parallel, accelerating the research cycle. With an intermediate implementation difficulty and an estimated setup time of just 30 minutes, this skill is accessible for users with a basic understanding of AI automation and Claude Code. It fits seamlessly into AI-first workflows by enabling teams to harness the power of multiple AI agents, facilitating a more dynamic and responsive research environment. As organizations increasingly adopt AI technologies, the Claude-Multi-Agent-Research-System-Skill represents a valuable addition to any research toolkit, paving the way for more efficient and impactful research initiatives.
No install command available. Check the GitHub repository for manual installation instructions.
git clone https://github.com/ahmedibrahim085/Claude-Multi-Agent-Research-System-SkillCopy the install command above and run it in your terminal.
Launch Claude Code, Cursor, or your preferred AI coding agent.
Use the prompt template or examples below to test the skill.
Adapt the skill to your specific use case and workflow.
Create a multi-agent research system to investigate [TOPIC]. Use [NUMBER] agents with these specializations: [AGENT_SPECIALIZATIONS]. Each agent should follow these guidelines: [GUIDELINES]. Generate a research plan with these deliverables: [DELIVERABLES]. Output results in this format: [OUTPUT_FORMAT].
Research System Design: Multi-Agent Research on Electric Vehicle Battery Recycling Agent Specializations: 1. Technical Researcher: Focuses on battery chemistry and recycling processes 2. Market Analyst: Investigates industry trends and market size 3. Regulatory Expert: Examines government policies and regulations 4. Financial Analyst: Evaluates costs, ROI, and investment opportunities Research Plan: 1. Technical Researcher will identify current recycling methods and their efficiencies 2. Market Analyst will project market growth and key players in the next 5 years 3. Regulatory Expert will create a global policy comparison for battery recycling 4. Financial Analyst will develop a cost-benefit analysis for a hypothetical recycling plant Deliverables: - Comparative analysis of lithium-ion vs. solid-state battery recycling - Market forecast for 2025-2030 with key players - Global regulatory landscape with compliance requirements - Financial model for a 50,000 ton/year recycling facility Output Format: - Executive summary (1 page) - Detailed findings (10-15 pages) - Data visualizations (5-10 charts) - References and sources Audit Trail: - Agent assignments and completion times - Data sources used by each agent - Cross-agent validation points Integration Points: - Python scripts for data visualization - Claude Code Skills for financial modeling - API connections to industry databases
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