agent-optimizerV6.1 Agent 性能优化器 - 基于轨迹分析和奖励反馈的轻量级优化框架
Install via ClawdBot CLI:
clawdbot install sandmark78/agent-optimizerGrade Fair — based on market validation, documentation quality, package completeness, maintenance status, and authenticity signals.
Calls external URL not in known-safe list
https://github.com/openclaw/openclawAudited Apr 17, 2026 · audit v1.0
Generated Mar 22, 2026
Optimizes tutorial generation by recording trajectories and user ratings, then analyzing patterns to improve content quality and user satisfaction. Ideal for content creation platforms or educational tools.
Enhances financial prediction accuracy by tracking ROI forecasts and rewarding based on error reduction, leading to better investment decisions. Suitable for fintech or investment advisory services.
Improves web scraping reliability by monitoring success rates and retry counts, optimizing performance for data collection tasks. Useful for data analytics or automation companies.
Records support interactions and user feedback to refine response strategies, increasing resolution rates and customer satisfaction. Applicable to customer service platforms.
Tracks campaign outputs and ROI metrics to adjust strategies, boosting engagement and conversion rates. Beneficial for digital marketing agencies.
Offers the optimizer as a cloud-based service with tiered pricing based on usage and features, targeting businesses seeking scalable AI performance improvements. Generates recurring revenue through monthly or annual subscriptions.
Provides tailored optimization services, including setup, analysis, and integration for specific client needs, ideal for enterprises with complex AI workflows. Revenue comes from project-based fees and ongoing support contracts.
Offers a free basic version for small-scale use, with advanced features like detailed trend analysis and A/B testing available in paid tiers. Attracts a broad user base and converts high-value customers.
💬 Integration Tip
Start by setting up the optimizer in a test environment to record initial trajectories before deploying in production, ensuring minimal disruption.
Scored Apr 19, 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.
Persistent memory for AI agents to store facts, learn from actions, recall information, and track entities across sessions.
Search and discover OpenClaw skills from various sources. Use when: user wants to find available skills, search for specific functionality, or discover new s...
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.
Give your AI agent eyes to see the entire internet. 7500+ GitHub stars. Search and read 14 platforms: Twitter/X, Reddit, YouTube, GitHub, Bilibili, XiaoHongS...
A self-evolution engine for AI agents. Analyzes runtime history to identify improvements and applies protocol-constrained evolution. Communicates with EvoMap...