Multi-Agent AI Setup Guide

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

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

Multi-Model Council - parallel execution of multiple LLMs with voting/consensus.

Iteracyjnie doskonali kod lub tekst AI, wykrywając i naprawiając błędy, optymalizacje, bezpieczeństwo i styl w maksymalnie trzech krokach.

Runs multiple independent tasks simultaneously by routing each to an appropriate AI model tier for faster parallel execution and combined results.

Generate a daily news portal as a single HTML file. Uses fetch_news.py + web_fetch + LLM for summaries. 18 categories, dark/light toggle, responsive layout.