多agent协同执行

A skill where the agent logs it's own findings for self-improvement

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...

A fast Rust-based headless browser automation CLI with Node.js fallback that enables AI agents to navigate, click, type, and snapshot pages via structured co...

Self-reflection + Self-criticism + Self-learning + Self-organizing memory. Agent evaluates its own work, catches mistakes, and improves permanently. Use when...

Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.

Typed knowledge graph for structured agent memory and composable skills. Use when creating/querying entities (Person, Project, Task, Event, Document), linkin...

Transform AI agents from task-followers into proactive partners that anticipate needs and continuously improve. Now with WAL Protocol, Working Buffer, Autonomous Crons, and battle-tested patterns. Part of the Hal Stack 🦞

Headless browser automation CLI optimized for AI agents with accessibility tree snapshots and ref-based element selection

基于五层拆解法的AI图片提示词生成器。将模糊的创意想法转化为结构严谨、可执行的图像生成规格书，支持多种风格预设和目标工具适配。

飞书端读取USER.md任务清单。当用户说"查看任务"、"我的任务"时触发，实时解析并返回格式化的分类任务列表，让用户快速了解当前所有可用任务和技能。

Agent 上下文管理方法论：通过分层文件体系实现跨 session 记忆延续、职责分离和高效上下文恢复。Use when: (1) 搭建新 agent 工作区, (2) 优化 agent 记忆和上下文管理, (3) 长期运行 agent 的记忆维护。