yuyonghao-agent-decision-engineAutonomous AI decision engine with multi-objective optimization, risk assessment, decision trees, and reinforcement learning for robust decision-making.
Install via ClawdBot CLI:
clawdbot install yuyonghao-123/yuyonghao-agent-decision-engineGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Generated May 9, 2026
A fintech startup uses the decision engine to optimize multi-objective trading strategies, balancing risk and return. The engine's Pareto optimization selects trade-offs between profit and volatility.
An automotive company integrates the decision tree and reinforcement learning to navigate complex urban environments. The agent assesses risks of different maneuvers and learns optimal driving policies.
A hospital uses multi-objective optimization to allocate ventilators and staff during a pandemic, minimizing cost while maximizing patient outcomes. Risk assessment identifies high-impact scenarios.
A utility company employs the engine to balance energy production and consumption across renewable sources. Q-learning adapts to demand patterns, reducing waste and cost.
Offer the DecisionEngine as a cloud API with usage-based pricing. Companies pay per optimization call or per active agent, enabling scalable access for startups and enterprises.
License the engine directly to hardware manufacturers or software vendors who integrate it into their products. One-time or annual license fees based on deployment scale.
Provide tailored solutions for clients needing custom objective weights, risk metrics, or reward functions. This includes integration support and ongoing maintenance contracts.
💬 Integration Tip
Start by importing the DecisionEngine class and familiarizing yourself with the four core methods (optimize, assessRisk, buildDecisionTree, qLearn) using the provided examples.
Scored May 9, 2026
Helps users discover and install agent skills when they ask questions like "how do I do X", "find a skill for X", "is there a skill that can...", or express interest in extending capabilities. This skill should be used when the user is looking for functionality that might exist as an installable skill.
Orchestrate multi-agent teams with defined roles, task lifecycles, handoff protocols, and review workflows. Use when: (1) Setting up a team of 2+ agents with different specializations, (2) Defining task routing and lifecycle (inbox → spec → build → review → done), (3) Creating handoff protocols between agents, (4) Establishing review and quality gates, (5) Managing async communication and artifact sharing between agents.
A unified OpenClaw skill that merges self-improvement and proactivity: learn from corrections, maintain active state, recover context fast, and keep work mov...
Meta-skill for AI agent self-improvement. Analyzes runtime logs to detect error patterns, regressions, and inefficiencies, then generates structured improvem...
Automatically assess task complexity and adjust reasoning level. Triggers on every user message to evaluate whether extended thinking (reasoning mode) would improve response quality. Use this as a pre-processing step before answering complex questions.
Associative memory with spreading activation for persistent, intelligent recall. Use PROACTIVELY when: (1) You need to remember facts, decisions, errors, or...