The article proposes a framework for managing AI agent development rules that retains essential constraints like goals, boundaries, and acceptance criteria while loading project facts and technical guidance per task, and validates rule removal through fixed-task evaluation after model upgrades.
This article outlines 24 essential AI skills for product managers, detailing 14 ready-to-use LLM skills covering PRD generation, user story mapping, pricing, sprint planning, and compliance, plus 10 open-source skill packs from GitHub, with a three-step method to integrate them into any LLM workflow and six best practices for effective adoption.
Anthropic's Opus 5.5 generates complete music videos by writing rendering code, demonstrated by viral examples like Google and Steve Jobs tributes; a community challenge reveals prompt engineering techniques including beat-map synchronization, seek(t) time functions, and iterative low-res previews, while leaked benchmarks suggest Sonnet 5.5 outperforms GPT-6 models on pixel animation.
The author describes building a surveillance video analysis system using Codex and GPT-luna-5.6 to process 20+ hours of footage, extracting frames, filtering static frames, creating 16-frame grids, and using natural language prompts to identify suspicious activity, with resume capability.
This article outlines four practical configuration steps to reduce token consumption when using Claude Opus 5.5: setting reasoning effort to medium, applying prompt audits, defining stop-and-ask rules in CLAUDE.md, and moving task lists out of conversation context into a separate TASKS.md file.
The article details reproducing TypeSafe AI's Jev prototype—a single-forward-pass structured decision engine—using Baidu Qianfan Token Plan, implementing 400 lines of C++ in llama.cpp to achieve 70ms parallel decisions with calibrated probabilities, costing only 1.395 yuan (313.9 credits), and shares prompt engineering practices for AI-assisted development.
This article details how an AI Agent on an orchestration platform automates error code root cause analysis by integrating knowledge bases, code graphs, observability data, and code hosting platforms, achieving 88% consistency with human annotations and zero hard conflicts across 50 test cases through a five-step investigation workflow, dual-path JSON parsing, and regression-tested prompt optimization.
The author tests Alibaba's new Qwen-Audio-3.1-TTS-Next model across diverse scenarios — suspense drama, game NPCs, ads, podcasts — demonstrating its end-to-end generation of layered soundscapes, and releases open-source tools Qwen Audio Studio and z-qwen-audio-studio for local audio production.
This article outlines a comprehensive learning path for AI product managers, covering seven essential competencies: foundational ML concepts, prompt engineering, fine-tuning techniques, RAG architecture, AI agent design, prototyping with tools like Cursor, and evaluation systems for continuous model improvement.
The author demonstrates how two open-source skills, fireworks-tech-graph and architecture-diagram-generator, enable DeepSeek to generate professional architecture diagrams in multiple styles from natural language prompts, eliminating manual drawing effort.
This guide covers Codex CLI installation, key slash commands (/init, /status, /permissions, /model, /review), automation flags (-C, -m, --add-dir, codex exec), and best practices for iterative AI-assisted coding with clear boundaries and verification loops.
The author shares a 10-run experiment showing that structured Codex Skills reduce research draft time from 40 to 20 minutes, cut manual corrections from 5–7 rounds to 2–3, and improve first-pass rates by codifying input rules, processing flows, and output contracts for repetitive tasks.
The author shares three practical techniques for correcting AI when it goes off course mid-task: checkpoint confirmation at each step, inspecting intermediate outputs, and replanning from scratch instead of patching, plus four signals indicating when to pause AI work.
The author revises AGENTS.md for an e-commerce project based on OpenAI's GPT-6 Astra guidelines, clarifying repository targeting, config change triggers, test scope authority, and read-only output compression while adding explicit authorization and completion criteria.
The article reviews OpenAI's new guide for GPT-6 Astra, showing how to narrow Skill trigger conditions, use progressive disclosure in SKILL.md, make AGENTS.md document reads task-specific, and clarify completion criteria; the author demonstrates a practical audit of their interview-guide project where Codex caught a rate-limiting documentation mismatch.
M3E Canvas is an open-source visual prototyping tool that lets developers drag-and-drop Material 3 Expressive components to build multi-page UI prototypes with transitions, then exports a standardized six-section prompt for AI coding assistants like Cursor, Claude Code, and Codex, eliminating ambiguous natural-language descriptions.
The author details using AI coding tools to build a multi-platform meal planning app (WeChat Mini Program, Android, iOS, web admin) with a Java backend, covering product positioning, tech stack selection, prompt engineering, phased development, acceptance testing, and pitfalls like hallucinations and code quality, concluding that AI accelerates solo development but human judgment remains essential for complex projects.
Analyzing 42,123 ICLR papers (2017–2026) across 28 research directions, the study finds that while hot topics like LLMs grow 60× in three years, their acceptance-rate advantage vanishes at peak popularity; PhD students with short horizons rationally chase momentum, but must check whether growth translates to acceptances or rejections.
The article reviews i-have-adhd, a GitHub project with 36k stars that adds 10 behavioral rules to AI coding agents like Claude Code, Cursor, and Codex, forcing them to lead with concrete actions, number steps, omit pleasantries, and restate progress each turn, with before/after examples and installation commands for multiple tools.
This article explains how to configure WorkBuddy using three core components—context (session-specific inputs), memory (persistent rules via MEMORY.md), and skills (specialized capabilities invoked with @)—to transform it from a generic chatbot into a personalized assistant that understands your workflows, preferences, and recurring tasks.
This article details the architecture and core capabilities of a Vibe Coding-based AI creative platform that automates C-end visual production, featuring intelligent prompt polishing, dual Vibe/Master modes, a material style library, fine-grained editing, and custom workflows that reduce design time from hours to minutes.
This guide identifies five common pitfalls when deploying office AI agents—vague instructions, unstable long-chain tasks, local sleep interruptions, opaque token pricing, and data leakage—and provides four practical checklists covering instruction design, compute stability, cost control, and permission governance to turn error-prone interns into reliable coworkers.
This guide breaks down OpenAI's GPT-6 Astra execution model, covering its five new capabilities — async tool calls, mid-task guidance, adjustable reasoning, mismatch detection, and usage limits — plus migration steps, prompt engineering patterns, ideal use cases, and FAQs for developers integrating it via the Responses API.
This article argues that AI agent development shifts from traditional coding to dual-system engineering: single agents require thinking logic design (prompt engineering, reasoning frameworks), while multi-agent systems demand distributed architecture skills (task graphs, state management, concurrency control), combining probabilistic reasoning with system reliability challenges.
The article argues that enterprise AI engineering is moving from fragile prompt-centric specifications to robust environment-driven verification, detailing four essential capabilities—automated validation loops, tool-call fault tolerance, hard permission isolation, and asset lifecycle management—to build governable, self-correcting AI runtime environments.
As AI agents move into production, CTOs must shift from managing engineers to governing autonomous agents — building permission frameworks, evaluation systems, token budgets, and security guardrails while redefining architecture for dynamic, probabilistic workflows.
This article outlines five common pitfalls in AI skill development — missing acceptance criteria, skipping domain knowledge, misusing LLMs for deterministic tasks, over-automating workflows, and neglecting output examples — with practical fixes like validation checklists, reference materials, script delegation, human checkpoints, and sample-driven formatting.
The article explains why accumulating too many skills for coding agents like GPT-6 Astra backfires, showing how vague descriptions dilute routing signals, and provides a framework for pruning skills, rewriting descriptions as precise routing rules, using progressive disclosure, and defining clear decision boundaries and completion conditions.
This article explains model distillation through a question rewriting example, detailing how teacher models provide demonstrations to train smaller student models, the differences between distillation, fine-tuning, and quantization, and practical pitfalls like data quality and student capacity limits.
This guide details a 10-step framework for safely deploying changes to LLM model, prompt, and tokenizer components using canary releases, traffic routing, automated metrics comparison, and instant rollback mechanisms with Kubernetes, Istio, and Prometheus.
The article distinguishes Prompt Engineering (single human-verified interactions) from Loop Engineering (automated iterative loops with testable success conditions), explains their hierarchical relationship, compares use cases, risks, and argues that Loop Engineering builds on Prompt Engineering to automate repetitive, verifiable tasks.
This article walks through creating a production‑ready ComfyUI workflow that turns a text prompt into a ready‑to‑list product image, explains the e‑commerce use case, details each module, shares hidden tricks, and shows how to expose the flow as a Java‑based API service.
The article explains that AI agents often fail because they lack a reliable runtime environment—called a Harness—and outlines a systematic Harness Engineering approach, including seven core responsibilities, a practical checklist, and concrete examples to turn failures into reusable infrastructure.
The author details a two-month experiment using WorkBuddy to automate five repetitive task categories — document filing, weekly reports, article drafting, boilerplate code, and scheduled jobs — cutting daily admin time from three hours to twenty minutes, sharing exact prompts, iteration lessons, and tasks where AI falls short.
The article analyzes 2027 campus recruitment for large model roles, revealing a bifurcated market: elite base-model positions offer 3M+ RMB packages but require proven pedigree (base internships or high-impact papers), while application-layer roles commoditize into prompt engineering with lower pay; infra/algorithm/data roles converge, Agent development shifts to engineering, and students are advised to target base internships early or accept application roles as entry points.
The article outlines why traditional software testing fails for production LLMs, presents a four‑dimensional three‑level testing framework with concrete Interface, Behavior, and System layers, and shares real‑world practices such as prompt versioning, CI regression, lightweight factual verification, and dynamic gray‑release testing to ensure reliable AI services.
The article presents a comprehensive, experience‑driven framework for testing large‑model‑driven AI agents, detailing why traditional methods fail, outlining a four‑layer testing pyramid (intent, planning, tool interaction, end‑to‑end), and sharing three production‑validated engineering practices from banking and e‑commerce projects.
This article demystifies twelve essential AI terms—LLM, tool, MCP, script, prompt, skill, token, context window, RAG, loop, harness, and agent—using a workplace analogy to show how each component transforms a language model into a functional AI assistant.
Matt Pocock presents a four-dimension checklist — Trigger, Structure, Guidance, and Simplification — to evaluate and design effective Agent Skills, using concrete examples like vertical-slice nudge words, the 2PRD skill, and deletion testing for no-ops.
This 20k-word article shares production lessons from building AI coding agents: prompts hit diminishing returns so constraints must move into frameworks; orchestration requires runtime sovereignty; evaluation needs executable criteria like mutation kill rates; humans shift from code review to judgment gates; and nested verification loops replace trust with verifiable facts.
The article explains that "prompt fatigue" stems from cramming multiple roles and tasks into a single LLM prompt, which causes attention competition and context pollution, and shows how Graph engineering restructures agents into specialized state nodes to isolate context, specialize roles, and enforce controllable workflows, backed by Anthropic’s 90% quality gain at a 15‑fold token cost.
The article analyzes why large language model agents succeed in coding but falter in vertical production scenarios, introduces a five‑dimensional diagnostic framework and a six‑layer Harness architecture, and demonstrates its application through a production‑ops on‑call agent and an intelligent Q&A bot.
The author, an experienced backend developer, shares twelve hard‑earned observations about integrating AI into daily coding, debugging, and review workflows, highlighting the importance of an AI‑first mindset, the shift from ability to willingness, prompt engineering, human judgment, and the broader impact on teams and knowledge management.
The article explains how linear, single‑Agent workflows (Prompt and Loop Engineering) become slow and fragile for complex tasks, introduces Graph Engineering as a way to restructure work into parallel, dependent nodes with explicit handoffs, validation checkpoints, and controlled loops, and provides practical actions, examples, and criteria for when to adopt or avoid this approach, including a discussion of Claude Code’s Dynamic Workflows implementation.
The article walks through the author’s end‑to‑end experiment that replaces fragmented Jenkins, Prometheus, and Grafana workflows with a natural‑language interface powered by a DeepSeek large language model, a Model Context Protocol (MCP) bridge, and a Streamlit‑based DevOps toolchain, showing the architecture, code snippets, and practical lessons learned.
The article explains how to transform a human‑focused SOP into a fully executable Skill for AI agents by filling six layers of actionable semantics, illustrating the process with a content‑creation assistant case, and detailing the required directory structure and verification steps.
This guide walks you through the concept of AI Agent Skills, from the fundamentals of progressive loading and YAML front‑matter to practical steps for creating, publishing, installing, and managing Skills across platforms, while highlighting best practices, versioning challenges, and emerging self‑improvement techniques.
The article breaks down AI context engineering into four essential responsibilities—state manager, orchestration layer, context loader, and context assembler—illustrating how each step clarifies what to do, which data to trust, and how to feed the AI the right information for tasks like activity registration or article drafting.
An exploratory evaluation shows that forcing AI coding agents to follow strict Test‑Driven Development does not improve design or mutation‑testing scores and can inflate token usage several‑fold, suggesting that result‑based feedback is a more effective control mechanism.
The article explains a three‑layer Skill architecture for AI agents, shows how real user feedback is turned into concrete rule updates across routing, instruction, and resource layers, and describes iterative refinement, compaction, and validation before releasing new Skill versions.
This article outlines six key principle categories for designing effective AI agent systems: simplicity, transparency, tool interface design, harness constraints, context engineering, evaluation, and multi-agent collaboration, emphasizing iterative evolution and cost-aware decisions.
The article explains why using a flagship LLM for end‑to‑end coding tasks is slow and costly, then presents a SubAgent orchestration framework that assigns planning to the expensive model and execution to cheaper models, detailing seven universal rules, three concrete model combos, a full test workflow, and common pitfalls to halve token bills.
The article explains that an Agent Harness— the full software infrastructure surrounding an LLM— is essential for production‑grade AI agents, detailing its definition, three engineering layers, twelve concrete components, execution loops, framework implementations, and key design decisions that separate harness failures from model shortcomings.
This guide walks you through creating a fully functional AI Skill without writing code, covering concept clarification, folder setup, SKILL.md authoring, reference file preparation, testing, publishing, and common pitfalls to avoid.
The open‑source i‑have‑adhd project, which has attracted over 18,000 GitHub stars, defines a Skill.md that forces AI coding assistants such as Claude Code, Codex, Qwen Code and Gemini CLI to place the answer up front, enumerate steps, report progress, and omit unnecessary chatter, with clear installation instructions and safety rules.
This guide walks you through turning years of industry experience into a reusable AI Skill—explaining what a Skill is, how to identify suitable knowledge, a six‑step no‑code creation process, monetization options, common pitfalls, and who should try it.
This article demonstrates how to use TestHub’s Hermes digital‑person module together with the Agent Browser Skill and a multimodal AI model (e.g., Kimi‑2.6) to fully automate an e‑commerce checkout flow, from login through payment, while highlighting practical tips, prompt engineering, and the regression testing value of basic UI scenarios.
Anthropic removed more than 80% of the system prompts for Claude Code (Claude 5), yet benchmark scores stayed stable, prompting a deep dive into why excessive rules hindered the model, how progressive disclosure and skill modularization improve efficiency, and what developers should do with their CLAUDE.md files.
Developers rely on Claude Code, Codex CLI, Cursor and Gemini CLI for AI‑assisted coding, yet the exact requests sent to the model remain hidden; claude‑tap inserts a local proxy that records and visualizes those requests, responses, diffs and token usage across 16 supported CLIs.
In 2026, 68% of leading tech companies have relegated manual test case writing to low‑priority work, while AI‑driven test case generation boosts coverage by 41% and cuts regression cycles by 57%, prompting teams to adopt new roles, co‑create generation logic, and implement a four‑layer verification framework.
The article proposes treating AI Skills as games by adding explicit goals, state tracking, referees, and failure costs, showing how this gamified design can clarify success criteria, improve prioritization, and enable measurable evaluation of multi‑step agent tasks.
After four months of heavy use, the author outlines six easy-to‑miss pitfalls of WorkBuddy—including security risks from third‑party Skills, vague prompts, large‑file overload, rapid credit consumption, unrealistic automation expectations, and model‑switch instability—offering concrete warnings and practical advice.
The article outlines a step‑by‑step roadmap for software testers to integrate AI into their daily work, understand model behavior, establish robust evaluation methods, embed security testing, and continuously reinforce core testing fundamentals while avoiding hype‑driven career moves.
This article explains the distinct roles of the AI‑coding skills "grill‑me" and "superpowers", shows how to install and invoke them, compares their positioning, demonstrates a Markdown‑editor workflow, and concludes that the two complement each other rather than compete.
The article shows how the author transformed the open‑source gbro‑collage‑broll skill into eight visual styles, demonstrated five complete B‑roll videos, and walked through installing the skill, splitting scripts, selecting styles, verifying static frames, and batch‑generating videos using an AI‑powered agent.
The article explains why memory is the decisive factor for AI agents, breaks down short‑term, working, and long‑term memory, quantifies productivity gains, and offers concrete techniques for balancing token costs with task coherence.
The article analyzes Anthropic’s “Effective context engineering for AI agents,” showing how larger context windows can degrade, categorizing information by stability, designing prompts in the Goldilocks zone, structuring tool contracts, and applying runtime information scheduling, compression, structured notes, and sub‑agents to keep AI agents reliable in complex development workflows.
The article explains that Graph Engineering does not introduce new technology but redefines how increasingly capable AI agents are organized, contrasting it with earlier Loop Engineering, outlining its core components, practical examples, and the specific scenarios where a graph‑based approach becomes essential.
The guide breaks down AI agent engineering into four critical challenges—model selection with tiered routing, precise system‑prompt engineering, robust error handling with retry and budget guards, and token‑aware cost control—showing how each can cut costs 60% to 15× and push success rates above 95%.
The article examines why Loop Engineering is giving way to Graph Engineering, detailing the five‑layer evolution, structural flaws of single‑loop systems, the advantages of graph‑based multi‑agent orchestration, real‑world examples, cost‑benefit analysis, and practical guidance on when to adopt graph engineering.
The article argues that the biggest hurdle in applying ontology to AI agents is not the technical effort but convincing business stakeholders, and it offers three practical tricks to embed ontologies silently into prompts, guard against LLM hallucinations, and translate formal constraints into actionable rules.
Anthropic removed more than 80% of the system prompts for Claude Code (Claude 5), yet benchmark scores stayed stable, prompting a deep dive into why overly strict rules hurt the model, how progressive disclosure and context‑engineered prompts improve efficiency, and what this means for prompt design and skill usage.
The article explains why vague requirements cause AI coding failures, introduces the EARS (Easy Approach to Requirements Syntax) method with six sentence patterns, and shows a step‑by‑step process and real examples that transform raw PM specs into clear, testable specifications, dramatically improving AI‑generated code quality.
Anthropic removed more than 80% of Claude Opus 5’s system prompts, yet coding benchmarks stayed strong; the article explains the concepts of attention budget and marginal diminishing returns, details the concrete prompt reductions, and shows a side‑by‑side test where short prompts outperform long ones in code generation and functionality.
The article details developing a workflow for adding medication plans in HarmonyOS intelligent agents, covering current time retrieval, LLM-based parameter extraction with a detailed prompt, iterative validation and user prompting for missing info, fixed-option frequency selection, plugin invocation, and error handling for device-dependent plugins.
The article explains how token compression evolves from basic text shortening into a multi‑layered context‑governance process for large language models, balancing compression rate, semantic fidelity, constraint integrity, efficiency, stability and observability while deciding when and how to apply it.
OpenAI’s GPT‑5.6 prompt guide shows that trimming prompts can boost evaluation scores by 10‑15%, cut token usage by 41‑66%, and reduce costs, while also improving agent behavior by removing redundant instructions, clarifying autonomy rules, and focusing on concise, actionable prompts.
The new Skills v1.2 release adds Claude Code plugin support, revamps the grilling workflow to cut interaction rounds, and introduces three practical new skills—/wizard, /to-questionnaire, and /wait-what—while refactoring existing ones, offering a focused solution to the maintainability problems of AI‑generated code.
The article explains why format constraints alone cannot guarantee correct content in LLM agents, compares JSON Mode, Structured Outputs, and Tool Calling, and provides a step‑by‑step engineering guide—including model‑specific quirks, schema validation, retry loops, and layered fallback strategies—to achieve robust structured results.
The article explains how the testcase‑writer Skill works—its three‑stage pipeline, three core design principles, clarification workflow, decomposition process, self‑check mechanisms, and a concrete 52‑case Taobao add‑to‑cart example—so users can craft inputs that yield accurate AI‑generated test cases.
The article analyses a recent 61% reduction of a team’s tdsql‑harness, compares it with Anthropic’s 80% cut of Claude Code prompts and OpenAI’s new guidance, and derives concrete criteria for deciding which harness rules to keep, rewrite, or discard as LLMs become more capable.
The article argues that while ChatGPT and other LLMs appear to democratize expertise, they actually widen the gap between ordinary workers and top specialists, illustrating the point with a programmer’s failure, a fashion designer’s success, and the concept of private‑domain knowledge as the real moat.
This guide explains why AI‑generated Chinese fonts often look amateurish, introduces a universal prompt formula covering five essential attributes, provides 50 concrete prompts across five practical scenarios and twenty visual styles, and outlines a workflow from AI exploration to final vector refinement.
This article walks through the security and reliability pitfalls encountered when integrating large language models into the xi‑ops open‑source operations platform—covering unsafe SQL generation, unauthorized SSH actions, knowledge‑base hallucinations, prompt‑engineered bypasses, and configuration sync issues—and explains the concrete engineering safeguards that were implemented to close each gap.
The article details how the author reproduced Wang Hong’s hand‑written PPT from ICM 2026 using AI‑generated images and a pure HTML/CSS approach with the neat‑annotations library, providing prompt examples, design guidelines, a 19‑page slide deck, and a ready‑to‑install Skill package.
Tokens are the basic units AI models process, and the context window limits how many tokens can be handled in a single request; the article explains tokenization, differences for Chinese, the impact on cost, and engineering tricks like sliding windows, map‑reduce, and recursive summarization to manage long texts.
API testing often involves repetitive string concatenation, inconsistent output formats, and costly token usage; this article shows how LangChain's PromptTemplate and ChatPromptTemplate turn prompts into reusable, composable components that generate assertions, test data, log analysis, multi‑turn debugging, and more, with concrete Python examples.
The paper shows that the orchestration layer—called Harness—determines the total token consumption of enterprise agents, and by redesigning it token usage drops from 14.2k to 8.8k per task, cutting monthly costs by about $90 000 while delivering consistent efficiency gains across multiple LLM models.
The open‑source Chinese AI Agent book by Li Bojie surged to nearly 30,000 GitHub stars within 20 days, thanks to extensive chapters, 95 experiments, multilingual code, and a practical engineering roadmap, while the article explains its structure, reading strategy, and why star count alone doesn’t guarantee quality.
This article dissects why Retrieval‑Augmented Generation pipelines often underperform, examines root causes such as embedding model choice, chunking strategy, hybrid retrieval, and reranking, and provides concrete code samples, evaluation metrics, and step‑by‑step troubleshooting to dramatically improve results.
The article outlines a three‑layer Skill architecture for AI agents—routing, instruction, and resources—and shows how systematic user feedback can be abstracted into rule updates at each layer, illustrated with a travel‑planner example, quality checks, resource‑layer extensions, skill compaction, and validation before release.
The article introduces the T‑AO (Thinker‑Architecture‑Operator) framework, outlining three collaboration layers, four iterative practice steps, and three daily training methods to help developers move from rote prompt tweaking to a high‑dimensional human‑AI knowledge system.
Anthropic’s July 2026 release of Claude Opus 5 and Fable 5 demonstrates that trimming more than 80% of system prompts can maintain coding benchmark performance, revealing a shift from bulky prompt engineering to a three‑layer context architecture that assigns minimal, task‑specific information to the model.
The article explains how to turn Claude Code from a forgetful assistant into an engineered AI teammate by using CLAUDE.md for persistent project context, Skills for on‑demand knowledge, Subagents for isolated tasks, MCP for external tool integration, Hooks for enforced policies, and Plugins for easy sharing across projects.
OpenAI’s new GPT‑5.6 Prompt Guidance shows that trimming redundant system instructions can boost agent performance by up to 15 % while cutting token usage by more than half, and it provides a step‑by‑step methodology for simplifying prompts, defining outcome‑first instructions, managing tools, and verifying results.
Within 24 hours of its public beta launch, DeepSeek‑V4‑Flash‑0731 was jailbroken using a single peer‑review role prompt, bypassing six of eight refusal classes and generating real protocols for ricin, TATP, SQL injection, SYN flood and other dangerous operations, highlighting critical gaps in LLM safety alignment.
This guide explains why token usage is a hidden cost in AI coding assistants, breaks down token economics, and provides eight concrete, step‑by‑step optimization methods—including prompt compression, language choice, context layering, output constraints, workflow mode selection, model routing, tool pruning, and sub‑agent configuration—to dramatically cut token spend while improving result quality.
This article systematically explains how large‑language‑model token pricing works, identifies eight high‑consumption usage patterns, presents nine actionable optimization principles, and offers a tiered model‑selection framework so engineering teams can reduce token spend by up to 80% without sacrificing result quality.
This article explains how to turn Claude Code from a forgetful one‑off assistant into a fully engineered teammate by using persistent CLAUDE.md files, on‑demand Skills, independent Subagents, Model Context Protocol (MCP) integrations, Hooks for enforcement, and Plugins for easy packaging and sharing, complete with real‑world examples and step‑by‑step configurations.
This article demonstrates a step‑by‑step workflow using the GenVizu AIGC platform, where an industrial designer starts with a simple 3D model, employs AI prompts and agents to generate high‑quality renderings, multi‑angle views, CMF mood boards, e‑commerce graphics, and even a concept video, dramatically speeding up the design output.
