The article explains that single‑turn AI chats cannot handle complex tasks, and introduces Harness—a six‑agent AI workflow that organizes AI roles, enforces 19 strict rules, and uses a five‑step setup to turn ad‑hoc prompts into a disciplined, self‑evolving production line for content and software development.
The article explains that effective use of Claude depends on establishing a stable personal work environment rather than merely crafting prompts, and it details 18 concrete actions organized into six layers—projects, personal instructions, fact sources, workflow cards, review loops, and boundaries—to create a reusable AI workbench.
This guide walks you through two free demos that show how a single developer can use Claude on Google Cloud to build fully automated AI agents, followed by a detailed 7‑day plan that transforms Claude from a chat tool into an AI employee capable of handling content creation, operations, code review, and more, all without requiring a computer science degree.
This article presents a step‑by‑step method for turning scattered personal prompt knowledge into versioned, testable, and shareable AI workflow assets, covering directory conventions, description design, test case creation, Bad Case feedback loops, Git management, team sharing mechanisms, and a 30‑day rollout plan.
As large language models mature, prompt‑writing roles are disappearing, prompting engineers to shift from crafting prompts to designing end‑to‑end AI workflows; this article outlines a three‑step system‑reconstruction protocol, common pitfalls, and practical guidelines for transitioning into workflow architecture.
This analysis explores the seven openness challenges of Agent Skills, the evolution of capability and trust models, combination security, lifecycle management, autonomous skill generation, multi‑modal extensions, ecosystem growth, commercialization pathways, long‑term human‑AI collaboration, and security risks, concluding with actionable recommendations for developers, enterprises, and ecosystem builders.
OpenAI’s newly open‑sourced openai‑agents‑python SDK is a lightweight, powerful Python framework for building multi‑agent AI workflows, quickly earning over 25,000 GitHub stars, supporting 100+ LLM providers, and offering sandbox agents, built‑in tracing, and human‑AI collaboration features.
The open‑source OpenAI Agents SDK provides a lightweight Python framework that enables multiple AI agents to collaborate like a team, offering features such as automatic handoff, sandboxed execution, safety guardrails, human‑in‑the‑loop control, full‑traceability, and support for over 100 LLM models, all with just a single pip install.
SkillClaw introduces a centralized evolution framework that transforms user interactions into structured evidence, allowing LLM agents to refine, create, or skip skills based on aggregated success and failure patterns, with nightly validation ensuring only proven improvements are deployed, resulting in consistent performance gains across diverse tasks.
This article walks you through a complete knowledge‑distillation workflow that turns Elon Musk’s decision‑making logic into a reusable AI skill, covering source collection, Obsidian setup, a six‑step prompting chain, adding personal commentary, and packaging the result for manual or automated AI use.
The article explains how multi‑agent AI systems create hidden bottlenecks and abnormal paths in customer‑service workflows, demonstrates how IBM Processing Mining automatically discovers end‑to‑end processes, quantifies performance, identifies variants and root causes, and provides concrete optimization steps that deliver measurable business value.
Learn how to turn daily repetitive Claude Code prompts into reusable Skills by identifying repeatable workflows, extracting five key Skill traits, applying a four‑step creation process, and iterating through observation, refinement, structuring, validation, and continuous improvement, illustrated with a real code‑review case study.
OpenAI’s newly open‑sourced Agents SDK for Python offers a lightweight, vendor‑neutral framework that lets developers define, orchestrate, and monitor multiple AI agents—each acting as a specialized tool or sandboxed worker—enabling rapid construction of complex, production‑grade AI collaboration workflows.
Claude Code now supports a 1 million‑token context window, but effective use hinges on disciplined context management—choosing when to continue, rewind, clear, compact, or delegate to sub‑agents, and applying three core concepts of context windows, compaction, and context rot to avoid performance pitfalls.
This article explains how Claude Code custom commands eliminate repetitive prompt typing and prompt drift by storing reusable markdown prompts in a version‑controlled .claude/commands directory, then demonstrates ten practical commands—ranging from environment checks to automated PR generation—applied to a real FastAPI ML inference service.
In the AI coding era, engineers must shift from hand‑writing repetitive code to designing robust AI‑driven workflows, mastering specification writing, task decomposition, quality gating, and system thinking to stay valuable as AI agents generate the bulk of production code.
This article examines the rapid rise of AI skills across industries, explains how workflow automation tools like Zapier and n8n, as well as emerging agentic systems, can transform routine tasks, enhance productivity, and become essential competencies for staying competitive in the 2026 job market.
The article outlines a practical SOP that transforms AI agents from passive responders to autonomous executors by introducing task decomposition, exception handling, and human‑in‑the‑loop audit checkpoints, enabling organizations to supervise multi‑model collaborations while avoiding chaos and ensuring alignment with business goals.
The article envisions AI‑driven “skill” agents that encapsulate the expertise of colleagues and the core capabilities of platforms, illustrating how everyday tasks like ordering food, booking rides, shopping, travel, job hunting, housing, and investing could be handled through proactive, personalized, continuously running skill services instead of isolated apps.
This article dissects Claude Code's skill system, tracing its evolution from early prompt engineering to the modern skill framework, detailing the loading pipeline, SKILL.md structure, lazy compilation, command routing, and the system's strengths and limitations.
The article explains ReAct—a Reason‑Act framework for large language model agents that observes, reasons, takes actions via tools, receives feedback, and iterates—highlighting its distinction from plain QA, its step‑by‑step workflow, practical importance, and a weather‑query example.
The article explains how to avoid the extremes of fully automated AI or no AI at all by defining clear Human-in-the-Loop patterns, identifying irreversible, high‑responsibility, and high‑exception steps, and applying tailored approval, edit, and escalation nodes in finance, contract, and other critical business processes.
In 2026, a major company's automated campaign failed due to hidden AI workflow failures, and our lab identified three invisible crash points—context overflow, permission loop deadlock, and data pollution—explaining their symptoms, root causes, and practical remediation techniques to build robust, long‑running AI systems.
This article walks through creating a LangGraph workflow in Python, first using OpenAI’s GPT‑5‑nano model, then swapping to Alibaba’s Qwen 3.5‑plus model, showing how to suppress warnings, filter out thinking responses, visualize the graph, and troubleshoot common errors, all without any prior AI coding experience.
Superpowers introduces a structured, Git‑worktree‑based AI development workflow that tackles plan deviation by combining brainstorming, automated planning, sub‑agent execution, mandatory TDD, and continuous back‑check, offering a practical alternative to simple plan modes and heavyweight frameworks.
The article argues that merely sharing AI prompts and tool lists is insufficient; instead, documentation and tools must be transformed into AI‑friendly, callable skills, illustrating the shift with concrete OpenClaw and CoPaw examples that enable self‑healing, redundancy, and truly automated workflows.
The article explains that the brainstorming skill in Superpowers acts as a gatekeeper, preventing agents from jumping into implementation before the problem is fully defined, by enforcing a structured design process that yields a reviewed spec ready for planning and execution.
This article analyzes the trade‑offs between Model Context Protocol (MCP) and Skills for Claude‑based AI workflows, offering a three‑question test, real‑world examples, and architectural diagrams to help engineers choose the most efficient approach for their tasks.
This guide explains why Claude Code often forgets previous context, demonstrates the shortcomings of a flat CLAUDE.md file, and provides a step‑by‑step method to reorganize project knowledge into a .claude directory with rules, memory, skills, and templates, enabling seamless cross‑project reuse and faster AI‑assisted development.
Recent AI news—from the open‑source daVinci‑MagiHuman video model and Sora app shutdown to Spline’s Omma 3D‑AI integration and Google’s TurboQuant cache optimization—illustrates a clear industry pivot toward stable, cost‑effective workflows rather than flashy demos.
The article outlines a systematic Prompt Engineering process that starts with defining task goals and metrics, structures prompts into modular components, uses offline evaluation and bad‑case analysis, incorporates RAG or tools when needed, and continuously monitors accuracy, hallucination, latency and cost.
YC CEO Garry Tan open‑sourced gstack, a Claude Code skill pack that turns an AI coding assistant into a virtual engineering team with defined roles, a linear workflow, and a one‑command install, sparking debate over its productivity claims, technical merits, and the broader shift toward AI‑driven workflow design.
The report details how the GhostClaw/GhostLoader campaign leverages trusted GitHub repositories and AI‑assisted development workflows to deliver a multi‑stage macOS payload that steals credentials, contacts a single C2 domain, and establishes persistence, while providing blue‑team detection and mitigation guidance.
The Agency Agents project on GitHub showcases a lightweight, shell‑script‑driven collection of specialized AI agents that act as a virtual company, offering developers and creators a fast way to access front‑end, community, creative, and reality‑checking expertise.
Anthropic's 33‑page official guide explains the design philosophy, three‑layer progressive disclosure architecture, usage categories, five practical patterns, authoring tips, debugging methods, success metrics, and a step‑by‑step workflow for building Claude Skills that give AI agents reusable professional capabilities.
This step‑by‑step tutorial shows how to install and configure the self‑hosted OpenClaw AI gateway, set up essential skills, connect Feishu, and create two practical workflows—curtain image generation and viral content dissection—while covering API setup, budgeting, troubleshooting, and pricing considerations.
The article examines how Anthropic’s Claude Cowork with plugins lets lawyers build custom AI workflows that cut operating costs dramatically, contrasts this DIY approach with vertical AI platforms like Harvey, and analyzes how these dynamics are reshaping the legal market and its competitive landscape.
The author shows how a quick GitHub search uncovered LinguaGacha, which combined with Claude Code enabled fast, consistent translation of 130 k‑word books, and how packaging the workflow as a reusable Skill saved hours and turned the process into a single‑command operation.
The article explains how to replace monolithic prompts with reusable, lazy‑loaded Skill files, compares Skills with Prompt, MCP and Function Calling, shows concrete Skill structures and examples, and demonstrates a Spring Boot AI interview platform with open‑source repositories.
This guide explains why a single OpenClaw agent quickly hits limits, describes the internal isolation units of an agent, shows how to detect when to split agents, and provides step‑by‑step instructions for creating agents, configuring routing, applying three levels of isolation, and performing pre‑deployment security checks.
The transaction terminal team created an AI‑driven workflow that automatically collects, cleans, classifies, alerts, distributes, attributes, and reviews user feedback, using a four‑step LLM model to ensure controllable, consistent, and explainable sentiment analysis while boosting efficiency and trust.
The article explains how to create a personal AI prompt—called a Master Prompt or Personal OS—by documenting identity, goals, style preferences, and using it each session, with step‑by‑step guidance, examples, and tips for automating the creation and adapting it to multiple roles.
The article proposes a hybrid architecture where Kimi Claw serves as the primary knowledge and search hub while a self‑hosted OpenClaw node supplements it for intranet security and multi‑model experiments, detailing scenarios, data boundaries, step‑by‑step implementation, and model‑tool selection.
The article examines the anxiety and hype surrounding AI, argues that product managers should shift from building AI‑native apps to embedding AI capabilities, defines the "business‑AI translator" role, and provides concrete frameworks, scenarios, and step‑by‑step methods for discovering and validating AI‑enabled opportunities in everyday product work.
planning-with-files is a Claude Code plugin that replaces volatile AI context with durable Markdown files—task_plan.md, findings.md, and progress.md—using hooks to automatically sync goals, record research, and log actions, enabling reliable task tracking, error persistence, and seamless session recovery for complex coding projects.
This article explains how to maximize the efficiency of Claude Code by understanding context windows, selecting optimal tokens, using commands like /upgrade, /model, and /init, and leveraging subagents, MCP servers, and skills to reduce token consumption while maintaining high‑quality AI responses.
Claude’s new Interactive Tools, powered by the Model Context Protocol, embed Slack, Figma, Asana and other SaaS UIs directly in the chat, letting users draft, preview, and send messages or generate FigJam diagrams without leaving the conversation, though the feature is limited to paid plans and raises trust and privacy concerns.
This guide reveals five powerful techniques—treating CLAUDE.md as a living document, keeping it concise, modularizing for large projects, respecting case-sensitive filenames, and letting Claude audit its own instructions—to transform a simple config file into a dynamic, evolving project knowledge repository.
This tutorial explains the planning-with-files Claude Code skill, its three‑file architecture, installation steps, activation methods, real‑world use cases such as a 2025 front‑end framework research demo, and advanced tips for recovery, collaboration, and version control, showing how external file persistence transforms AI conversations into durable, multi‑step workflows.
Claude Code Workflow Studio is a VSCode extension that lets you visually design, export, and run AI‑driven automation workflows without writing code, offering low cognitive load, AI‑assisted editing, Slack sharing, and Git‑friendly version control for test and data processing scenarios.
The article analyzes how AI‑driven search is reshaping content strategy, introduces the concept of Answer Engine Optimization (AEO), and breaks down AirOps’s three‑layer architecture, product capabilities, and its role as a prototype for the next five years of vertical AI workflow startups.
The article introduces Spring AI Alibaba DeepResearch, a Java‑based system that automates end‑to‑end research by integrating multi‑agent workflows, configurable RAG pipelines, data ingestion, observability, and dynamic report generation, and provides detailed deployment and extension guidance.
OpenAI will unveil Agent Builder at DevDay, a visual drag‑and‑drop canvas that lets users assemble AI agents from templates, logic nodes, connectors and tools, aiming to lower the barrier for creating production‑grade AI solutions and compete with automation platforms like n8n and Zapier.
The article explains the differences between AI Agents and AI Workflows, compares their characteristics, introduces the hybrid Agentic Workflow concept, and offers practical recommendations for building enhanced LLM applications using simple prompts or advanced frameworks.
Learn how to serialize LangChain components—including prompts, chains, and agents—using JSON and YAML, enabling reproducibility, collaboration, persistence, and decoupling, with step‑by‑step code examples for dumping objects to files and loading them back into executable LLM pipelines.
The author builds an AI‑driven end‑to‑end assistant that transforms a research paper into a structured reading note, generates reproducible code, runs experiments, summarizes results, and creates a report, demonstrating how large language models like Kimi K2 can streamline the entire paper‑to‑implementation workflow.
This article shares three evidence‑backed Claude Code workflows—strategic CLAUDE.md context files, autonomous three‑stage pipelines, and rapid design‑to‑code prototyping—that dramatically cut research time, improve code quality, and transform developers into strategic collaborators.
This article explores how an AI workflow platform built on the open‑source Dify project automates data collection, processing, and report generation, reducing manual effort while improving analysis accuracy and providing actionable insights for project progress.
This article describes how a development team integrated a suite of AI‑driven workflows—including AI‑generated documentation, code scaffolding, rule‑based IDE guidance, MCP server connectivity, and memory banks—to reduce repetitive tasks, accelerate coding, improve code review, and streamline operations across the entire software lifecycle.
The article explains how breaking complex LLM tasks into sequential steps—known as prompt chaining—improves answer accuracy, debuggability, flexibility, and enables sophisticated AI workflows such as report generation, chatbots, and content creation using tools like n8n and Ollama.
This article demonstrates how to convert a Dify‑built product‑review classification workflow into a Spring AI Alibaba project, detailing the visual design in Dify, DSL export, code generation via Studio or CLI, and showcasing performance gains, flexibility improvements, and practical deployment steps.
This guide explains how to organize a knowledge base, select embedding and rerank models, clean user queries, and construct a Dify DSL workflow that iteratively retrieves and merges information before handing it to a large language model for answer generation.
This guide walks you through installing and configuring Dify—a open‑source LLM application platform—on your local machine using Docker, integrating it with Ollama for custom models, and exploring its core features such as chat assistants, agents, workflows, and tool extensions, all illustrated with step‑by‑step screenshots and code snippets.
This guide explains how to construct a deep research workflow in Dify that leverages AI agents, loop variables, and structured outputs to automatically explore complex topics, gather sources, and synthesize comprehensive reports with proper citations.
This article explains how to overcome Dify's lack of built‑in scheduling and monitoring by integrating it with external task‑scheduling systems such as GitHub Actions and XXL‑JOB, detailing setup steps, limitations, and the advantages of using XXL‑JOB for precise, enterprise‑grade workflow automation.
This article introduces LangGraph, the next‑generation framework built on LangChain for constructing complex, stateful AI applications, compares it with LangChain, showcases real‑world deployments, and provides a step‑by‑step Python tutorial for building a smart customer‑service chatbot with looped reasoning, tool integration, and human‑in‑the‑loop support.
OpenAI has updated its Agents SDK to support Anthropic's Model Context Protocol (MCP), enabling developers to connect AI agents with diverse data sources and tools through a standardized interface, while providing official documentation, example code, caching, and tracing features to streamline AI workflow integration.
Huolala’s Wukong platform tackles the common “technology hype, implementation difficulty” dilemma of generative AI by unifying multimodal enterprise knowledge, enabling dynamic multi‑agent workflows, and providing low‑code tools, observability, and stable deployment across dozens of business scenarios.
This article explains how the Resources data hub, Tools capability engine, and Prompts interaction templates work together to create a secure, extensible workflow that enables large language models to ingest data, execute tasks, and generate structured outputs.
This step‑by‑step guide shows how to use Coze’s AI application development features to create, test, and publish a no‑code WeChat mini‑program that generates avatars, covering app creation, workflow design, UI building, online debugging, and one‑click deployment.
This article introduces LangChain.js, explains its origins, core concepts such as chats, templates, tools, and chains, demonstrates practical JavaScript code examples, and explores the LangChain Execution Language (LCEL) for building flexible, conditional AI workflows.
This article introduces LangChain.js, its core concepts such as chats, templates, tools, and chains, demonstrates how to use LCEL for flexible workflow composition, and shows practical JavaScript code examples for building AI-powered applications with large language models.
Java engineer Wu Ming shares his experience with CodeFuse, an AI-powered code completion tool, describing how large language models enhance coding efficiency, the challenges of early versions, practical tips for integrating AI assistants into workflows, and his vision for AI’s expanding role across the entire software development lifecycle.
This article explains how cloud‑native AI leverages container‑based architectures and advanced scheduling algorithms—such as resource queues, gang scheduling, bin‑packing, GPU topology‑aware and Tor‑aware dispatch—to improve resource and engineering efficiency, and introduces Baidu’s AI workflow engine PaddleFlow with its design, features, and deployment options.
This article examines industrial intelligence from the perspectives of flow and fusion, detailing its current state, talent needs, pain points, AI development processes, edge‑cloud architecture, and key characteristics such as timeliness, reliability, explainability, and applicability in manufacturing.
SQLFlow is an open‑source system that lets users describe and execute end‑to‑end AI tasks—including data extraction, preprocessing, model training, evaluation, prediction, and explanation—entirely with SQL, simplifying workflow construction across multiple databases and machine‑learning engines while supporting scalable execution on Kubernetes.
This article analyzes the challenges of managing the entire machine‑learning lifecycle, reviews existing traceability approaches, and proposes comprehensive methods for versioned management of model training, prediction, and online service to improve efficiency, reproducibility, and maintenance of AI systems.
