research-reprompter

What This Skill Does

The research-reprompter is an advanced cognitive framework for OpenClaw AI designed to transform vague, high-level research intent into high-fidelity, executable multi-agent swarm configurations using the USACF (Unified Swarm Agent Coordination Framework) standard. Instead of merely performing a search, this skill acts as a research architect. It takes your rough, unstructured thoughts and guides you through a structured interview process to define project titles, subject types, research depth, and objectives. Once the parameters are set, it generates a complete super-prompt that initializes a multi-phase swarm capable of discovery, deep analysis, adversarial review, and final synthesis. By mapping your input to either Chain-of-Thought (CoT), Tree-of-Thought (ToT), or Graph-of-Thought (GoT) algorithms, it ensures your research is not just comprehensive but structurally sound and rigorously stress-tested.

Installation

To integrate this skill into your environment, run the following command in your terminal: clawhub install openclaw/skills/skills/dorukardahan/research-reprompter Ensure you have the latest version of the OpenClaw agent runtime to support full USACF command execution.

Use Cases

• Strategic Due Diligence: Rapidly assess the risk and market viability of a potential business venture or startup acquisition.
• Technical Gap Analysis: Compare existing software architectures against specific emerging technologies or industry standards.
• Market Research: Automatically synthesize competitor data, sentiment analysis, and market movement reports into actionable executive summaries.
• Academic Inquiry: Conduct deep dives into complex multi-domain topics with adversarial red-teaming to eliminate confirmation bias.

Example Prompts

1. "Research this: investigate the viability of moving our entire infrastructure to a decentralized cloud stack by Q4."
2. "I need a deep dive into the recent performance issues of our React frontend; compare the latest framework updates against our current codebase."
3. "Researcher, can you help me perform a competitive gap analysis on the top five open-source AI agents currently available on GitHub?"

Tips & Limitations

• Be Specific: While the skill handles vague inputs well, the more context you provide during the interview phase, the more accurate the initial quality score will be.
• Adversarial Review: Do not skip the adversarial phase. It is the core of the USACF framework and is designed specifically to challenge your assumptions and catch hallucinations.
• Limitations: The agent currently relies on its base model knowledge and accessible tools. It is not a substitute for live, primary-source data collection if that data is behind proprietary or restricted access portals.