The article analyses OpenClaw’s single‑turn memory design, explains the two vicious cycles that cause memory bloat and forgetting, and introduces seekdb M0’s cloud‑native, two‑stage memory and experience system that decouples memory from context, reduces token costs, and shares practical knowledge across agents.
Anthropic’s Claude Code introduces Auto Dream, an automated memory‑consolidation feature that triggers after 24 hours of inactivity and five dialogue exchanges, scanning, merging, and pruning project‑specific memory files to keep the agent’s knowledge base clean and up‑to‑date.
This guide outlines a systematic LLM evaluation framework, covering goal definition, core and code‑oriented benchmarks, agent and safety tests, data‑contamination mitigation, toolchain choices, result reporting, and the inherent structural limits of static benchmarks.
This guide walks you through creating an Agent Skill that turns OpenCode into a code‑repository expert, covering OpenCode installation, skill‑creator setup, DeepWiki integration, SKILL.md design, disambiguation, hallucination safeguards, and practical examples for querying Ascend inference ecosystem repositories.
The article explains the four fundamental shortcomings of raw large language models—no memory, no code execution, outdated knowledge, and no workspace—and shows how a six‑component Harness (file system, Bash + sandbox, AGENTS.md memory, web search + MCP, context engineering, and orchestration + hooks) systematically resolves each issue to make AI agents practical and reliable.
The article clarifies the distinct meanings of Agent, Multi‑Agent, and Deep Agent, explains how control shifts from engineers to models, compares architectures across nine dimensions, and shows why a lightweight harness is essential for long‑running, parallel AI‑driven software development.
This guide walks you through installing the Webman framework, adding the Neuron AI package via Composer, generating a custom Agent class, configuring its provider with an API key, and interacting with the agent through a controller to receive responses from a large language model.
The article explains why agents often lack business understanding, describes Retrieval‑Augmented Generation (RAG) as the fastest way to provide correct, up‑to‑date business context, outlines eight practical RAG patterns, and offers a step‑by‑step checklist for building enterprise‑ready agents.
This article provides an in‑depth analysis of Spring AI Alibaba, comparing it with Spring AI, detailing its four‑layer architecture, GraphCore workflow engine, AgentFramework, enterprise‑grade MCP integration, code examples, pros and cons, suitable scenarios, and future roadmap for Java developers building AI applications.
This article explains why large language models need tool use, defines the concepts of Tool Use, Tool Call, and Function Calling, compares them, walks through a complete tool‑use workflow, and discusses architectural, safety, and design considerations for building reliable LLM agents.
The article outlines DataWorks' evolution from a decade‑long big‑data governance platform to an AI‑driven Copilot and autonomous Agent system, detailing its technical foundations, tool‑adaptation layer, context engineering, security safeguards, and future vision of a professional, open, and intelligent big‑data development ecosystem.
This article explains why large language models alone can hallucinate, introduces the tool‑using agent architecture, and provides a step‑by‑step Python tutorial using LangChain, LangGraph, and Tavily to create, run, and evaluate a real‑time web‑search capable AI assistant.
The article analyses the Memento‑Skills paper and its open‑source implementation, showing how a frozen large language model can continuously improve by treating skills as external memory, using a five‑step Observe‑Read‑Act‑Feedback‑Write loop, advanced routing, and modular architecture to achieve significant gains on GAIA and HLE benchmarks.
The article analyzes the Memento‑Skills framework, which treats external memory as executable skills to enable deployment‑time continual learning for frozen LLM agents, detailing its read‑write reflective loop, skill‑as‑memory design, behavior‑trained skill router, experimental validation on GAIA and HLE benchmarks, and theoretical guarantees without gradient updates.
The article argues that enterprise software’s biggest bottleneck is not missing features but users’ inability to master complex systems, and demonstrates through OpenClaw how a natural‑language‑driven Agent layer can replace thick manuals with a unified service interface, dramatically reducing training, support, and operational costs.
The article examines the shift from AI‑assisted code generation (AI Coding 2.0) to an execution‑focused paradigm (AI Coding 3.0), showing how introducing agents into Kanban‑based workflows forces explicit modeling of decisions, verification, and orchestration to turn software delivery into a provably correct system.
This guide explains how Claude Code Skills work as folder‑based agents, introduces a nine‑category taxonomy, and shares practical design patterns—including progressive disclosure, Gotchas, memory handling, hooks, and sharing strategies—to help developers build robust, reusable Skills from scratch.
This article explains the distinction and synergy between traditional AI workflows and modern agents, outlines their four‑step processes, showcases practical examples such as intelligent customer service and content generation, and recommends tools for beginners to quickly build AI‑driven applications.
This article dissects Anthropic's Skill framework, revealing how it drives AI agents through five distinct execution modes—pure prompt injection, script execution, library calls, progressive document loading, and workflow orchestration—while avoiding function‑calling registration and optimizing token usage.
The article explains that the term “Agent development” hides a fundamental split between Agent Frameworks, which give developers building blocks to assemble their own agents, and Agent Harnesses, which provide ready‑to‑run agents, and shows how this distinction affects decisions, maintenance, and troubleshooting.
This guide shows how to extend the open‑source Opencode LLM coding tool with a custom agent and folder structure so it can store topic summaries, retain memory across sessions, and serve as a powerful terminal‑based research assistant.
Although the Model Context Protocol (MCP) was launched by Anthropic in late 2024 and initially praised, users now report severe context‑window costs, instability, and cumbersome authentication, leading Perplexity and others to abandon it in favor of traditional CLI tools that remain more composable and reliable.
The article critiques the conventional LLM‑agent loop that forces every tool output back through the model, proposes a dual‑output architecture where tools stream multimedia events directly to the client while still returning a compact semantic result to the model, and demonstrates the design with Python code examples.
The article explains why an AI agent's capabilities are limited by memory depth rather than model size, reviews three historical memory architectures, highlights their structural shortcomings, and details how the ClawLake solution provides a multi‑layer, multimodal, enterprise‑grade memory infrastructure for OpenClaw agents.
The article analyzes how large language models can quickly generate 80%‑complete code but still produce numerous hidden bugs, missing product logic, context, and safety checks, creating a new high‑value role—post‑processing engineers—who must bridge the gap to production‑ready, reliable software.
The article analyzes the emerging "Harness Engineering" paradigm, explaining why large‑language models need a surrounding harness of file systems, code execution, sandboxing, memory, and context management to become useful autonomous agents and how this concept may shape AI development through 2026.
This article introduces OmniGAIA, a challenging full‑modal benchmark with 360 real‑world tasks, and OmniAtlas, a training framework that equips multimodal agents with active perception and tool‑integrated reasoning, showing substantial performance gains over existing open‑source models through extensive experiments and analysis.
This article analyzes the concurrency pitfalls of multi‑entry AI Agent systems and explains how OpenClaw uses session keys, dual‑layer queues, configurable queue modes, and three‑knob micro‑batch controls to achieve ordered, isolated, and observable processing across diverse entry points.
This guide explains how to create, structure, test, and deploy Anthropic Skills—custom folders that embed domain expertise and automated workflows into AI agents—covering core architecture, design patterns, naming conventions, testing strategies, packaging, and multi‑scenario distribution for both individual and enterprise use.
MemSifter introduces a lightweight agent that outsources memory retrieval for large language models, using a Think‑and‑Rank pipeline and a task‑result‑oriented reinforcement‑learning training paradigm to achieve superior retrieval accuracy and efficiency across eight benchmark tasks while keeping inference overhead minimal.
The article explains why the buzz around AI agents can mislead learners, breaks down overlapping concepts such as LLM, RAG, Tool Use, Agent, Code Agent, and Agent Platform into distinct layers, and outlines a step‑by‑step learning plan to build a solid conceptual map.
SkillNet, an open‑source AI infrastructure from Zhejiang University and partners, organizes over 200,000 high‑quality Skills into a structured network, enabling agents to retain knowledge, improve task rewards by 40 % and cut execution steps by 30 % while employing rigorous multi‑dimensional evaluation.
AI can quickly produce a functional 80‑point prototype, but turning that code into a reliable, secure, high‑performance product that can run in production still requires human engineers to fix bugs, handle edge cases, and ensure safety, making the post‑mortem engineer a new industry necessity.
The article compares LangChain4j and Spring AI across design philosophy, core features, ecosystem integration, community maturity, and learning curve, providing concrete code examples, a feature‑richness matrix, and practical selection guidelines to help Java developers choose the most suitable AI framework for their needs.
This guide explains how to use Neuron v3’s multimodal audio capabilities—including OpenAI and ElevenLabs text‑to‑speech and speech‑to‑text providers—to create a local, hands‑free voice assistant that captures audio, transcribes it, processes it via an agent, and plays back responses.
OpenAI's newly released GPT-5.4 integrates reasoning, coding, computer use, and agent tool calls, achieving a 75% success rate on OSWorld-Verified tasks—surpassing the human baseline—while its Tool Search feature reduces agent token consumption by 47% and supports up to 1 million tokens for long‑running workflows.
AI can quickly generate functional code, but turning that 80‑point prototype into a reliable, secure, production‑ready product requires human engineers to perform rigorous validation, refactoring, and polishing—roles the author dubs ‘post‑processing engineers’—who bridge AI’s speed with real‑world robustness and profitability.
This article presents a comprehensive methodology for selecting AI agent scenarios, explains the economic benefits of agent deployment, details the technical architecture—including domain large models, knowledge bases, planning strategies, and RL‑based scheduling—and illustrates how these components are applied to insurance product design, pricing, fulfillment, and risk control to drive scale and profit.
This article compares traditional hard‑coded programming, visual workflow tools, and ReAct‑based AI agents, showing how agents let natural language drive decisions, reduce maintenance cost, and enable dynamic, user‑friendly solutions, with concrete code examples and a GitHub reference.
This article introduces the ReAct agent paradigm—combining reasoning and acting—explains its observation‑think‑act loop, showcases a step‑by‑step weather‑and‑clothing example, outlines essential components, provides pseudo‑code for the execution flow, and links to the Lynxe Func‑Agent framework on GitHub.
This tutorial shows how to equip an AI agent with long‑term memory using Retrieval‑Augmented Generation (RAG), covering the concepts of vector embeddings, FAISS indexing, building and querying a knowledge base, and providing complete Python code examples.
This tutorial shows how to extend the MiniManus agent framework with Skill support, explains why Skills are needed compared to plain MCP, details the Claude Skill specification, provides concrete command‑line operations, code implementations, and demonstrates Skill‑MCP collaboration through practical examples.
On Chinese New Year's Eve, Alibaba's Qwen 3.5 open‑source model—featuring a 397 billion‑parameter backbone with a 17 billion‑parameter active set, hybrid linear attention, and sparse MoE—was released under Apache 2.0, delivering 8.6‑19× faster inference, top‑tier agent, code and multimodal scores, and rapid integration across major AI platforms.
The MiniMax M2.5 model, released open‑source on Feb 13, achieves an 80.2% SWE‑Bench Verified score that surpasses GPT‑5.2, Claude Opus 4.6 and Google Gemini 3 Pro, runs 37% faster than its predecessor, costs only $1 per hour, and demonstrates SOTA agent abilities in browsing and tool use, marking a major leap for Chinese large‑language models.
OpenClaw is an open‑source, headless AI agent that runs locally, interacts through WhatsApp, Telegram or Discord, stores persistent memory, and can automate tasks ranging from car‑price negotiation to autonomous phone calls, while raising significant security, privacy, and operational challenges.
This article breaks down Anthropic’s 33‑page guide on building Claude Skills, explaining the two‑layer MCP/Skill architecture, progressive disclosure design, file‑naming rules, reusable patterns, testing methods, and distribution options for creating stable, reusable AI workflows.
ByteDance's Seed 2.0 Pro (Doubao 2.0) showcases industry‑leading performance on math, vision, document, long‑video, and code benchmarks, dramatically lowers inference cost, and is now available in the Doubao app and Trae IDE, positioning it as a serious challenger to GPT‑5.2 and other top LLMs.
The article argues that, for AI agents in 2026, lightweight CLI tools accessed via the terminal outperform the heavyweight Model Context Protocol (MCP) by leveraging existing shell utilities, reducing token overhead, and preserving Unix composability, ultimately delivering faster, cleaner development workflows.
AMemGym introduces an on‑policy, interactive benchmark that evaluates and trains AI assistants' long‑term memory by structuring state evolution, diagnosing memory failures, and enabling agents to self‑evolve, revealing that selective memory writing outperforms passive approaches across various LLM and agent architectures.
This article explains how to overcome the training‑deployment gap for Java‑based AI agents by introducing a cloud‑native, low‑intrusion online training pipeline built on AgentScope Java and Trinity‑RFT, detailing architecture, configuration, custom selection and reward strategies, and showing measurable accuracy gains on a SQL‑Agent benchmark.
This article examines the core challenge of giving large‑language‑model agents many abilities while keeping context size limited, compares three common loading strategies, introduces a progressive‑disclosure skill mechanism with three loading layers, and details its implementation, benefits, limitations, and suitable use cases in AgentScope‑Java.
The article explains that premature tool calls in LLM agents stem from a data‑distribution bias in fine‑tuning, and it presents practical fixes such as adding non‑tool samples, enforcing a Thought chain, and using negative sampling to teach the model when to think before acting.
This article explains what AI agents are, outlines single‑agent and multi‑agent use cases in AIOps such as knowledge retrieval, tool guidance, fault diagnosis, and process automation, and lists the key technical skills needed to build and manage these intelligent operational assistants.
AutoLink introduces an autonomous, iterative schema‑linking approach for Text‑to‑SQL that treats schema discovery as a progressive, agent‑driven exploration, dramatically improving recall while cutting token costs, and outperforms existing database‑level and element‑level methods on large benchmarks such as Spider 2.0‑Lite and BIRD.
This article walks through integrating an AI‑powered Agent into an internal BFF platform using LangGraph, detailing the architectural choices, state‑graph implementation, prompt engineering, knowledge‑base construction, tool integration, conversation handling, and context compression techniques to enable reliable script generation, execution, and validation.
This article systematically explains the concepts of MCP, Agent, Skill, and Rule from an engineering viewpoint, highlighting their roles, differences from traditional API calls, and how they enable large language models to safely and autonomously interact with external tools.
This article walks through the design patterns, three‑level loading mechanism, and practical implementation steps for integrating reusable, domain‑specific Skills into an existing Agent system, covering both local and distributed deployments with Redis‑based versioning and sandboxed execution.
This article examines why most enterprise AI agents stumble—due to hallucinations, semantic mismatches, and lack of explainability—then introduces ontology as a semantic layer that structures business concepts, rules, and constraints to enable reliable reasoning, centralized rule management, and transparent AI behavior.
WeKnora is an open‑source Tencent framework that leverages large language models, multimodal parsing and hybrid retrieval to let users query PDFs, Word files, images and other complex documents with natural language, offering a web UI, API and secure private‑cloud deployment options.
The article presents a ten‑step architecture for implementing scalable LLM Skills, covering a meta‑tool pattern to avoid tool explosion, progressive three‑level loading to save tokens, script execution outside the LLM context, Redis‑based storage with pub/sub updates, version locking, dynamic addition, batch loading, and file‑system strategies.
AgentCPM-Explore, a 4‑billion‑parameter open‑source model, achieves state‑of‑the‑art results on long‑range exploration tasks, matching or surpassing larger 8B and even 30B models, thanks to a full‑stack infrastructure, novel training tricks, and extensive benchmark evaluations across eight agent‑centric datasets.
This article breaks down the extensive Claude Cowork system prompt, revealing its product positioning, model versions, core tools, safety boundaries, interaction philosophy, user‑wellbeing safeguards, political neutrality rules, file‑handling policies, and the technical workflow that lets Claude run inside a lightweight Linux VM while respecting strict security and ethical constraints.
This comprehensive manual outlines ByteDance's Agent platform, covering its technical foundations, architecture, development workflow, real‑world application scenarios, operational optimization, security compliance, future innovation paths, case studies, team collaboration, risk mitigation, tooling, and global adaptation.
This article explains how to integrate a Human‑In‑The‑Loop (HITL) mechanism into ReactAgent, detailing the motivation, design of interaction, tool description, XML‑based UI rendering, Redis‑driven waiting loop, and the broader architectural parallels with design patterns and other agent frameworks.
Transitioning from simple chatbots to sophisticated agents, this article explains how expanding context becomes a core variable, detailing the evolution from prompt engineering to context engineering, the challenges of managing growing context, and practical solutions like structured context, tool integration, and the MCP framework for reliable AI systems.
In 2025 the AI coding market has shifted from occasional assistance to essential reliance, with tools like GitHub Copilot, Cursor, and TRAE achieving deep integration in developers' daily workflows, driven by frequency, task complexity, and emerging agent paradigms.
VS Code 1.107 deprecates the free, locally‑run IntelliCode extension, urging users toward GitHub Copilot with usage limits and cloud‑based processing, while also introducing TypeScript 7 support, expanded Agent capabilities, experimental Claude Skills, and MCP protocol enhancements.
This article details how a front‑end team generated high‑quality React code from only UI screenshots by iteratively applying imgcook, an Agent‑driven MCP service, and custom prompts, achieving up to 80% code usability and demonstrating a repeatable script‑based workflow.
This article explains the three evolutionary stages of AI—from large language models that generate text, through workflow‑enhanced systems using retrieval‑augmented generation, to fully autonomous agents capable of self‑directed decision‑making—while detailing the four core technologies that power each stage.
This article explains the proper role of Text2SQL within an Agent architecture, detailing its placement as a tool, function‑call implementation, decision logic for invocation, multi‑turn handling, failure management, and how to clearly present these concepts in technical interviews.
This article explains the fundamental differences between workflow‑style fixed pipelines and agent‑style dynamic LLM orchestration, compares their characteristics, reviews classic workflow patterns, and walks through a concrete implementation using the Kuzi platform with step‑by‑step screenshots.
This article details JetBrains' journey from early AI plugins to a platform‑wide AI architecture, covering challenges like prompt‑engineering dependence, technical debt in the IntelliJ Platform, the design of the Agent Client Protocol (ACP), and future AI‑driven features such as EmbArk, ReCap, and Ask Settings.
This guide walks you through creating a Memory v3 module for a Go‑based AI agent, enabling long‑term storage of preferences, tasks, and context so the agent can recall and leverage past interactions for more personalized responses.
This article explains how to design and implement Human‑in‑the‑Loop (HITL) interactions for large‑model agents on Alibaba's OpenLM platform, covering the challenges of server‑side execution, MCP transport extensions, tool‑calling patterns, timeout handling, and UI rendering strategies across multiple client devices.
This article explains Claude's Skill system, how Skills differ from prompts, the concept of industry Workflows, the boundaries between Skills, Tools, and MCPs, and provides a step‑by‑step guide for designing Skills, wrapping legacy systems into Workflows, and building a hybrid Workflow‑plus‑Agent architecture for reliable, auditable automation.
The article explains why large‑model agents need context compression, outlines five engineering‑level schemes (both lossless and lossy), demonstrates each with concrete XML snippets and step‑by‑step reasoning, and advises using lossless methods before resorting to lossy prompt‑driven compression.
This article explains what an AI Agent is, distinguishes it from traditional workflows, and demonstrates how to build and customize agents using the Go‑based Blades framework, covering core concepts, code examples, five workflow patterns, best‑practice guidelines, and reference resources.
This article details how, starting from only UI screenshots and no design files, a team used imgcook, an Aone Agent with MCP service, and prompt engineering to automatically generate mobile front‑end code, iteratively improving usability from 40% to 80% and documenting the full workflow.
In real‑world LLM agents, giving the model too many tools at once leads to frequent function‑call errors, but applying dynamic function routing to narrow the candidate set dramatically reduces the error rate—from over 20% down to around 1%—and provides clear guidelines on when and how to implement it.
The article explains that relying solely on autonomous AI agents in industry is impractical, outlines the three major pitfalls of pure‑agent approaches, and details how combining agents with structured workflows, RAG, and multi‑level architectures delivers reliable, cost‑effective enterprise solutions.
This article breaks down why function‑call reliability is the biggest bottleneck for LLM agents and presents a systematic five‑step loop—schema quality, prompt context, sampling, training data, and runtime defenses—plus concrete optimization techniques such as dynamic tool routing, plan‑execute, validation layers, memory injection, and log‑driven tuning, illustrated with real‑world cases.
This article breaks down the memory systems behind LLM‑based agents, explaining why persistent memory is needed, the differences between short‑term context buffers and long‑term vector stores, practical implementation choices, maintenance strategies, and how to articulate these concepts effectively in technical interviews.
This article walks through setting up MCP servers in both stdio and SSE modes, shows how to load their tools into LangChain agents using the new create_agent API, compares the two communication methods, and provides complete runnable Python examples.
This article reviews Spring AI's evolution, architecture, and key features—including unified APIs, prompt engineering, streaming, RAG, multi‑modal support, and agent workflows—showing how Java back‑ends can efficiently expose AI capabilities without handling low‑level model logic.
This article examines Tencent's large language model deployments across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, while deep‑diving into the RAG, GraphRAG, and Agent technologies that enable smarter, more reliable AI applications.
This article examines Tencent’s large language model deployments across content generation, intelligent customer service, and game role‑playing, and explains the underlying technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems—highlighting how they enhance performance, explainability, and multi‑step reasoning in real‑world business scenarios.
Since ChatGPT's debut, the LLM landscape has progressed through four stages—prompt engineering, chain orchestration, autonomous agents, and multi‑agent systems—each enhancing intelligence and automation, with this article detailing their evolution, advantages, drawbacks, and practical implementation examples in Go.
Cursor, once hailed as the leading AI‑enhanced IDE, has shifted its focus by making Agent mode the default and launching its own large‑model Composer, which the vendor claims runs four times faster than comparable models, though real‑world performance remains to be validated.
This article explores Tencent's large language model deployments across various business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, and explains the underlying technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agent systems—that enable these applications.
This article examines Tencent's large language model applications across diverse business scenarios, detailing core use cases such as content generation, intelligent customer service, and role‑playing, and explains the three key technologies—Supervised Fine‑Tuning, Retrieval‑Augmented Generation, and Agents—that enable these capabilities.
VitaBench, a new open‑source benchmark from Meituan’s LongCat team, evaluates LLM‑driven agents across three realistic life‑service scenarios—food ordering, restaurant dining, and travel planning—using 66 tools and quantifying reasoning, tool, and interaction complexities, exposing a mere 30% success rate on complex cross‑scene tasks.
This article analyzes two contrasting AI agent design paradigms—process‑driven workflow orchestration and autonomous agent intelligence—examines their strengths and limitations, and proposes a hybrid architecture that fuses deterministic pipelines with dynamic planning, tool use, and memory mechanisms to achieve both reliability and adaptability.
This guide explains how LangChain’s Expression Language (LCEL) lets you declaratively connect prompts, models, and output parsers into chains, walks through environment setup, dependency installation, and detailed code examples ranging from a basic joke generator to retrieval‑augmented generation and memory‑enabled agents.
This guide explains what LangChain tools are, why clear descriptions matter, and walks through three ways to create them—using the @tool decorator, StructuredTool with Pydantic models, and custom BaseTool subclasses—plus examples of built‑in tools and reference links.
This article traces the evolution of agent development toolchains across four stages—basic frameworks, collaboration tools, reinforcement‑learning‑driven context engineering, and model‑centric architectures—while highlighting how stable cloud‑native infrastructure components like gateways, runtimes, observability, and security keep AI applications reliable and scalable.
This article explains why most LLM‑based agents only perform pseudo‑planning through prompts or hard‑coded loops, outlines when to rely on prompt‑driven versus program‑driven planning, compares popular frameworks such as ReAct, MRKL, BabyAGI and AutoGPT, and clarifies what true autonomous planning would require.
Google Research, X, and Cloud teams introduced Earth AI, a interoperable GeoAI model family that fuses image, population, and environmental data via a Gemini‑driven reasoning Agent, achieving state‑of‑the‑art performance and a 64% reasoning boost over Gemini 2.5 Pro while enabling non‑experts to run real‑time cross‑domain analyses.
This article walks through the design and implementation of a two‑layer memory architecture for LangGraph agents, covering short‑term and long‑term stores, various storage back‑ends, prompt engineering, utility functions, node definitions, human‑in‑the‑loop interrupt handling, and how user feedback is captured and used to continuously update the agent’s behavior.
This article reviews the major Manus 1.5 update, detailing its four‑fold speed boost, full‑stack web‑app generation via natural language, and a step‑by‑step experiment that recreates a classic macOS‑style desktop in under ten minutes, while evaluating its design, development, and user‑account features.
This article explains how to create, structure, and use Agent Skills for Claude, detailing the SKILL.md format, progressive disclosure, code execution, security best practices, and future directions for building reusable, composable capabilities in AI agents.
