Agentic

Universal skills loader for AI coding agents - npm i -g openskills - numman-ali/openskills

Claude Code skill that removes signs of AI-generated writing from text - blader/humanizer

Stand on the shoulders of giants — Create AI skills built on proven methodologies from domain experts. - GBSOSS/skill-from-masters

macOS app to manage your Codex skills

Hi, welcome to my website! This is where I share my thoughts on things that interest me, mostly tech, philosophy, history, and math.

A comprehensive collection of Agent Skills for context engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, or debugging agent systems that require e...

Devin is cooked, chat.

Enable your agents to share universal knowledge, principles, and best practices that compound across all interactions.

tl;dr: Anthropic recently introduced the idea of agent skills. Skills are simply folders containing a SKILL.md file along with any associated files (e.g., documents or scripts) that an agent can discover and load dynamically to perform better at specific tasks. We've added skills support to deepagents-CLI. The Rise of Generalist Agents General purpose agents like Claude Code and Manus have gained widespread adoption. While we might expect generalist agents to use many tools, a surprising tren

Some findings of using Skills and the loadout pattern with MCP.

If you’ve built an agent, you know that the delta between “it works on my machine” and “it works in production” can be huge. Traditional software assumes you mostly know the inputs and can define the outputs. Agents give you neither: users can say literally anything, and the space

Over the past month at LangChain, we shipped four applications on top of the Deep Agents harness: * DeepAgents CLI: a coding agent * LangSmith Assist: an in-app agent to help with various things in LangSmith * Personal Email Assistant: an email assistant that learns from interactions with each user * Agent Builder: a no-code agent building platform powered by meta deep agents Building and shipping these agents meant adding evals for each of them, and we learned a lot along the way! In this

Deep agents can't be evaluated like simple LLM tasks. After building and testing 4 production agents over the past few months, we learned that evaluating deep agents requires: 1. Bespoke test logic for each datapoint — each test

The best agent products aren't the most flexible, they're the most opinionated. Learn why agents need fewer knobs, not more, and how to design around model intelligence spikes.

Claude can now discover, learn, and execute tools dynamically to enable agents that take action in the real world. Here’s how.

Learn how to make stateless LLM agents remember conversations, manage context, and implement memory banks.

A step-by-step practical guide on building AI agents using Gemini 3 Pro, covering tool integration, context management, and best practices for creating effective and reliable agents.

A brain dump of all the ways I've been using Claude Code.

Build powerful multi-agent systems with Agent Development Kit

This blog has been co-authored by Hanfei Sun, Vertex AI Agent Engine, Software Engineer, and Huang Xia, Vertex AI Agent Engine, Software Engineer. TL;DR: Vertex AI Agent Engine now integrates with the Live API to enable real-time, bidirectional streaming agents. This allows for low-latency, human-like conversations using text and audio. This post demonstrates how to quickly build a streaming agent with the Agent Development Kit (ADK), leveraging a fully managed, serverless platform that hand...

Learn how to get the most out of the context window in Amp, with the least amount of work.

Setting up the coding agent rust project with Gemini API

As AI agents powered by Large Language Models (LLMs) become increasingly versatile and capable of addressing a broad spectrum of tasks, ensuring their security has become a critical challenge. Among the most pressing threats are prompt injection attacks, which exploit the agent's resilience on natural language inputs -- an especially dangerous threat when agents are granted tool access or handle sensitive information. In this work, we propose a set of principled design patterns for building AI agents with provable resistance to prompt injection. We systematically analyze these patterns, discuss their trade-offs in terms of utility and security, and illustrate their real-world applicability through a series of case studies.