ReCall enhances LLMs by integrating reinforcement learning for effective reasoning with search and tool calls. This innovative approach helps AI agents to use external tools, improving their functionality and decision-making capabilities.
claude install Agent-RL/ReCallhttps://attractive-almandine-935.notion.site/ReCall-Learning-to-Reason-with-Tool-Call-for-LLMs-via-Reinforcement-Learning-1d7aec91e9bb8006ad40f9edbfe2191a
Enhancing AI responses by integrating real-time data from external APIs.
Automating interactions with various tools to streamline workflows and improve efficiency.
Improving search accuracy in AI queries by utilizing advanced reasoning with tool calls.
Facilitating complex decision-making processes through the integration of multiple tool outputs.
claude install Agent-RL/ReCallgit clone https://github.com/Agent-RL/ReCallCopy 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.
Using ReCall with Automa, identify [NUMBER] potential leads from [WEBSITE] based on [CRITERIA]. For each lead, extract their name, company, and contact information. Then, use the extracted data to create a personalized email template for outreach. Finally, suggest a follow-up sequence for each lead.
Based on the criteria provided, ReCall with Automa identified 15 potential leads from the website 'TechInnovators.com'. The leads include individuals from companies such as TechSolutions Inc., Innovatech, and FutureTech. The extracted data includes names like John Doe from TechSolutions Inc., Jane Smith from Innovatech, and Alex Johnson from FutureTech, along with their respective contact information. A personalized email template was created for each lead, addressing them by name and mentioning their company. The follow-up sequence includes a follow-up email in 3 days, a LinkedIn connection request in 5 days, and a phone call in 7 days.
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