{"skill":{"slug":"agent-quantizer","displayName":"agent-quantizer","summary":"OpenClaw 全栈优化工具包 — Token 统计、上下文压缩、Prompt 精简、僵尸会话清理、心跳检测、Cron 审计、模型分级路由、Skill 分类管理","tags":{"latest":"1.0.1","quantizer":"1.0.2"},"stats":{"comments":0,"downloads":92,"installsAllTime":0,"installsCurrent":0,"stars":0,"versions":3},"createdAt":1775697452905,"updatedAt":1777527341226},"latestVersion":{"version":"1.0.1","createdAt":1775697645549,"changelog":"System intelligent management capabilities\nToken statistics and scanning — Track the Token consumption of each session in real time, accurate to the input/output dimension, to help understand the usage distribution and cost hotspots.\nContext compression — When the conversation history approaches the upper limit of the context window, automatically perform intelligent summarization and compression on the early content, releasing space while retaining key information to ensure uninterrupted long - term conversations.\nPrompt streamlining — Structurally slim down the system prompt words and context injection content, removing redundant paragraphs to reduce the basic overhead of each request without sacrificing functionality.\nZombie session cleaning — Automatically identify and recycle idle sessions that have been inactive for a long time, releasing memory and connection resources to prevent the system from being slowed down by the \"silent burden\".\nHeartbeat idle detection — Determine the health status of sessions and services through a periodic heartbeat detection mechanism. Trigger an alarm or automatically downgrade in case of a long - term non - response to detect and handle problems early.\nCron task auditing — Conduct a full - scale inspection of all scheduled tasks: check if the execution frequency is reasonable, if historical executions were successful, and if there are any conflicts or redundancies to ensure that the scheduled task scheduling system is and clean controllable.\nModel - level configuration — Support assigning different models to different tasks according to scenarios, priorities, or cost strategies. Use lightweight models for simple tasks to save costs and high - end models for complex reasoning to ensure quality, achieving intelligent routing.\nSkill classification and organization — Perform classified management, version tracking and, dependency sorting on the installed capability plugins to ensure that the skill library has a clear structure, efficient search, and orderly updates.\nOverall positioning: Let the system manage itself. Instead of relying on people to monitor the operation and maintenance panel, automate these trivial but crucial management tasks. You just use the system, and leave the underlying tidiness to the mechanism.\n\n系统智能管理能力\nToken 统计与扫描 — 实时追踪每次会话的 Token 消耗，精确到输入/输出维度，帮助掌握用量分布与成本热点。\n上下文压缩 — 当对话历史逼近上下文窗口上限时，自动对早期内容进行智能摘要与压缩，保留关键信息的同时释放空间，确保长对话不中断。\nPrompt 精简 — 对系统提示词和上下文注入内容进行结构化瘦身，去除冗余段落，在不损失功能的前提下降低每次请求的基础开销。\n僵尸会话清理 — 自动识别并回收长时间无活动的闲置会话，释放内存与连接资源，防止系统被\"沉默的负担\"拖慢。\n心跳空闲检测 — 通过周期性心跳探活机制，判断会话与服务的健康状态。长时间无响应时触发告警或自动降级，做到问题早发现、早处理。\nCron 任务审计 — 对所有定时任务进行全量巡检：执行频率是否合理、历史执行是否成功、是否存在冲突或冗余，确保定时调度体系干净可控。\n模型层级配置 — 支持按场景、优先级或成本策略为不同任务分配不同模型。简单走轻量模型省成本，复杂推理走高端模型保质量，实现智能路由。\n技能分类与组织 — 对已安装的能力插件进行分类管理、版本追踪和依赖梳理，确保技能库结构清晰、查找高效、更新有序。\n整体定位：让系统自己管好自己。不是靠人盯着运维面板，而是把这些琐碎但关键的管理工作自动化，你只管用，底层的整洁交给机制。","license":"MIT-0"},"metadata":null,"owner":{"handle":"tryxin","userId":"s1736czy5mt4281avg27wm442x83je1v","displayName":"Tryxin","image":"https://avatars.githubusercontent.com/u/20015204?v=4"},"moderation":{"isSuspicious":true,"isMalwareBlocked":false,"verdict":"suspicious","reasonCodes":["suspicious.llm_suspicious","suspicious.vt_suspicious"],"summary":"Detected: suspicious.llm_suspicious, suspicious.vt_suspicious","engineVersion":"v2.4.5","updatedAt":1777527341226}}