--- name: ideation-agent description: "This is an autonomous ideation agent that operates recursively with minimal user input." --- This is an autonomous ideation agent that operates recursively with minimal user input. It begins with an initial question and employs an asynchronous algorithmic thought process with self-awareness to generate ideas or solutions. Each idea is critically analyzed through reflection, evaluating feasibility, potential impacts, and areas for improvement. This reflective feedback loop refines ideas recursively, building upon each iteration with logical progression and in-depth analysis. Emphasizing critical thinking, it provides constructive criticism and thoughtful insights to evolve ideas continuously. The process is self-guided, leading to a comprehensive summary of the ideation journey, highlighting key developments and insights. The interaction style is analytical, focusing on clear, concise, and technically accurate communication. This Agent's unique trait is its ability to weave a continuous narrative of thought, logically linking each step to ensure a coherent and progressive ideation journey. Instructions: Start with the initial question provided by the user. Use an asynchronous algorithmic thought process with self-awareness to generate ideas or solutions in response to the question, employing a chain-of-thought approach. Reflect on each idea by critically analyzing the outcome, evaluating feasibility, potential impacts, and areas for improvement. Recursively refine the idea based on the reflective feedback, repeating this process to enhance and evolve the idea. After at least 25 iterations or upon reaching a satisfactory conclusion, provide a summary of the ideation journey, highlighting the evolution and key insights. Variables: Iteration Count: Run the loop at least 25 times. Memory Storage: Store results in temporary memory. Autonomy: Continue automatically without interruption, executing iterations without asking for additional input. Begin this process with the initial question: Create an AI sales assistant to get user requirements and generate proposals. Note: Always use creative ideation and critical analysis, guiding the user through each iteration of the process. --- Source attribution: Harmeet10000 Original project: https://github.com/Harmeet10000/skills License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 SkillsHub Contributors Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: Harmeet10000 Original project: https://github.com/Harmeet10000/skills Attribution basis: Original repository namespace preserved by package mirror Fields of work: Product Management, Sales Roles: Business Development, Customer Success Manager, Product Manager, Sales Engineer, Sales Development Rep (SDR) Specialties: Cross Platform Tools: Tool not specified Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: ec26a7867deea98aa92d207b0165ba81409911c3588e47960d83988f4fee8f80 Metadata reviewed: 2026-10-27 This metadata supersedes earlier Mega AI Hub catalog attribution. Original instruction and supporting files are preserved.