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Prompt Engineering

1587 articles · Page 1 of 16
Tencent Cloud Developer
Tencent Cloud Developer
Aug 21, 2026 · Artificial Intelligence

12 Unfiltered Observations on Working with AI

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.

AIBackend DevelopmentKnowledge Management
0 likes · 11 min read
12 Unfiltered Observations on Working with AI
ITPUB
ITPUB
Aug 18, 2026 · Artificial Intelligence

Redefining Skill Development: A Complete Tutorial and One‑Stop Assistant

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.

AI AgentDevOpsPrompt Engineering
0 likes · 23 min read
Redefining Skill Development: A Complete Tutorial and One‑Stop Assistant
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 18, 2026 · Artificial Intelligence

Mastering AI Context Engineering: The Four Core Components Explained

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.

AIContext AssemblyContext Engineering
0 likes · 12 min read
Mastering AI Context Engineering: The Four Core Components Explained
FunTester
FunTester
Aug 17, 2026 · Artificial Intelligence

Why Result Feedback Beats Enforced TDD for AI Coding Agents

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.

AI CodingAgentic codingMutation testing
0 likes · 15 min read
Why Result Feedback Beats Enforced TDD for AI Coding Agents
Data Party THU
Data Party THU
Aug 17, 2026 · Artificial Intelligence

How Real Feedback Drives Continuous Skill Evolution for AI Agents

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.

AI agentsFeedback iterationPrompt Engineering
0 likes · 12 min read
How Real Feedback Drives Continuous Skill Evolution for AI Agents
Open Source Tech Hub
Open Source Tech Hub
Aug 15, 2026 · Artificial Intelligence

7 Golden SubAgent Orchestration Rules to Let Expensive Models Only Talk and Cut Costs in Half

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.

AI workflowLLM cost optimizationPrompt Engineering
0 likes · 17 min read
7 Golden SubAgent Orchestration Rules to Let Expensive Models Only Talk and Cut Costs in Half
DataFunTalk
DataFunTalk
Aug 14, 2026 · Artificial Intelligence

Deep Dive into Agent Harness: Dissecting the Architecture Behind AI Agents

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.

AI agentsLLMPrompt Engineering
0 likes · 20 min read
Deep Dive into Agent Harness: Dissecting the Architecture Behind AI Agents
IT Services Circle
IT Services Circle
Aug 13, 2026 · Artificial Intelligence

How a Simple Skill.md Earned 18K Stars by Making AI Say the Answer First

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.

AI assistantsClaude CodeCodex
0 likes · 5 min read
How a Simple Skill.md Earned 18K Stars by Making AI Say the Answer First
Advanced AI Application Practice
Advanced AI Application Practice
Aug 12, 2026 · Artificial Intelligence

AI-Powered UI Automation of E‑Commerce Checkout with TestHub Agent Browser Skill

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.

AI automationAgent Browser SkillPrompt Engineering
0 likes · 8 min read
AI-Powered UI Automation of E‑Commerce Checkout with TestHub Agent Browser Skill
Java Tech Enthusiast
Java Tech Enthusiast
Aug 12, 2026 · Artificial Intelligence

Why Anthropic Cut 80% of Claude Code Prompts Without Dropping Performance

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.

AIAnthropicClaude
0 likes · 16 min read
Why Anthropic Cut 80% of Claude Code Prompts Without Dropping Performance
Woodpecker Software Testing
Woodpecker Software Testing
Aug 12, 2026 · Artificial Intelligence

2026 Guide to Transforming Test Teams with AI‑Driven Test Case Generation

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.

AI testingPrompt Engineeringsoftware quality
0 likes · 8 min read
2026 Guide to Transforming Test Teams with AI‑Driven Test Case Generation
FunTester
FunTester
Aug 12, 2026 · Artificial Intelligence

Turning AI Skills into Games: A Structured Design Approach

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.

AI agentsPrompt Engineeringevaluation
0 likes · 15 min read
Turning AI Skills into Games: A Structured Design Approach
Senior Tony
Senior Tony
Aug 11, 2026 · Artificial Intelligence

Six Common Pitfalls When Using WorkBuddy – A 4‑Month Review

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.

AI AgentCreditsFile Handling
0 likes · 6 min read
Six Common Pitfalls When Using WorkBuddy – A 4‑Month Review
FunTester
FunTester
Aug 11, 2026 · Artificial Intelligence

How Testers Can Build a Sustainable AI Career Path

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.

AI testingModel EvaluationPrompt Engineering
0 likes · 13 min read
How Testers Can Build a Sustainable AI Career Path
SpringMeng
SpringMeng
Aug 11, 2026 · Artificial Intelligence

Interview Question: Superpowers vs. grill‑me – Why You Should Use Both

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.

AI CodingClaude CodePrompt Engineering
0 likes · 10 min read
Interview Question: Superpowers vs. grill‑me – Why You Should Use Both
ShiZhen AI
ShiZhen AI
Aug 11, 2026 · Artificial Intelligence

How I Turned One Open‑Source B‑roll Skill into Five Distinct Video Styles

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.

AI video generationAgent workflowB-roll
0 likes · 12 min read
How I Turned One Open‑Source B‑roll Skill into Five Distinct Video Styles
Big Data and Microservices
Big Data and Microservices
Aug 11, 2026 · Artificial Intelligence

How to Build an AI Agent That Remembers Everything

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.

AI agentsLong-Term MemoryPrompt Engineering
0 likes · 11 min read
How to Build an AI Agent That Remembers Everything
Architect
Architect
Aug 10, 2026 · Artificial Intelligence

Anthropic Deep Dive: Context Engineering Lessons from Real‑World R&D

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.

AI agentsAnthropicContext Engineering
0 likes · 19 min read
Anthropic Deep Dive: Context Engineering Lessons from Real‑World R&D
Big Data and Microservices
Big Data and Microservices
Aug 10, 2026 · Artificial Intelligence

AI Agent Development: Four Essential Challenges to Master

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%.

AI AgentCost ControlLLM Operations
0 likes · 12 min read
AI Agent Development: Four Essential Challenges to Master
Linyb Geek Road
Linyb Geek Road
Aug 10, 2026 · Artificial Intelligence

Is Loop Engineering Dead? Understanding the New Paradigm of Graph Engineering

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.

Graph EngineeringLangGraphLoop Engineering
0 likes · 23 min read
Is Loop Engineering Dead? Understanding the New Paradigm of Graph Engineering
AI Large-Model Wave and Transformation Guide
AI Large-Model Wave and Transformation Guide
Aug 9, 2026 · Artificial Intelligence

Why Explaining Ontology Beats Technology in AI Agent Deployments

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.

AI agentsLLM hallucination mitigationOntology
0 likes · 8 min read
Why Explaining Ontology Beats Technology in AI Agent Deployments
IT Services Circle
IT Services Circle
Aug 9, 2026 · Artificial Intelligence

Why Anthropic Cut 80% of Claude Code System Prompts Without Dropping Performance

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.

AI Model OptimizationClaudeProgressive Disclosure
0 likes · 16 min read
Why Anthropic Cut 80% of Claude Code System Prompts Without Dropping Performance
Ubiquitous Tech
Ubiquitous Tech
Aug 9, 2026 · R&D Management

How EARS Rewrites Requirements to Make AI Coding More Accurate

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.

AI CodingEARSPrompt Engineering
0 likes · 18 min read
How EARS Rewrites Requirements to Make AI Coding More Accurate
Java Tech Enthusiast
Java Tech Enthusiast
Aug 8, 2026 · Artificial Intelligence

Cutting 80% of Claude’s System Prompts Still Yields Strong Performance

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.

AI CodingAnthropicAttention Budget
0 likes · 15 min read
Cutting 80% of Claude’s System Prompts Still Yields Strong Performance
macrozheng
macrozheng
Aug 7, 2026 · Artificial Intelligence

Why Shorter Prompts Work Better: Lessons from OpenAI’s GPT‑5.6 Guide

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.

AI agentsGPT-5.6OpenAI
0 likes · 11 min read
Why Shorter Prompts Work Better: Lessons from OpenAI’s GPT‑5.6 Guide
Java Architecture Diary
Java Architecture Diary
Aug 7, 2026 · Artificial Intelligence

Why Skills v1.2 Is a Must-Have for AI Coding (Matt’s Top 5 Skills Lead the Leaderboard)

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.

AI CodingClaude CodeJava
0 likes · 8 min read
Why Skills v1.2 Is a Must-Have for AI Coding (Matt’s Top 5 Skills Lead the Leaderboard)
AI Engineer Programming
AI Engineer Programming
Aug 7, 2026 · Artificial Intelligence

How to Ensure Reliable Structured Outputs in LLM Agents

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.

AgentJSON ModeLLM
0 likes · 13 min read
How to Ensure Reliable Structured Outputs in LLM Agents
Advanced AI Application Practice
Advanced AI Application Practice
Aug 6, 2026 · Artificial Intelligence

Why AI‑Generated Test Cases Miss the Mark and How Understanding the Skill Design Fixes It

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.

AI testingPrompt EngineeringQuality Assurance
0 likes · 12 min read
Why AI‑Generated Test Cases Miss the Mark and How Understanding the Skill Design Fixes It
21CTO
21CTO
Aug 5, 2026 · Industry Insights

Don’t Be Fooled by AI: Why Only 1% of People Truly Win with ChatGPT

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.

AILLMPrompt Engineering
0 likes · 9 min read
Don’t Be Fooled by AI: Why Only 1% of People Truly Win with ChatGPT
Advanced AI Application Practice
Advanced AI Application Practice
Aug 4, 2026 · Artificial Intelligence

Stop Messing Up AI Fonts: 5 Scenarios, 20 Styles, 50 Ready‑to‑Copy Prompts

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.

AI font generationChinese typographyPrompt Engineering
0 likes · 18 min read
Stop Messing Up AI Fonts: 5 Scenarios, 20 Styles, 50 Ready‑to‑Copy Prompts
Xike
Xike
Aug 4, 2026 · Operations

How We Fixed the AI‑Powered xi‑ops Ops Platform’s Critical Pitfalls

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.

AI opsLLMMCP
0 likes · 21 min read
How We Fixed the AI‑Powered xi‑ops Ops Platform’s Critical Pitfalls
Old Zhang's AI Learning
Old Zhang's AI Learning
Aug 4, 2026 · Frontend Development

Recreating Wang Hong’s Hand‑Written PPT Style with AI: Prompts and Open‑Source Skills

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.

AI image generationHTML CSSNotability
0 likes · 13 min read
Recreating Wang Hong’s Hand‑Written PPT Style with AI: Prompts and Open‑Source Skills
Big Data and Microservices
Big Data and Microservices
Aug 4, 2026 · Artificial Intelligence

How Much Can AI Remember? Understanding Tokens and Context Windows

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.

Chinese NLPCost OptimizationLarge Language Models
0 likes · 10 min read
How Much Can AI Remember? Understanding Tokens and Context Windows
Test Development Learning Exchange
Test Development Learning Exchange
Aug 3, 2026 · Backend Development

Stop Hand‑Writing Prompts: Use LangChain Templates to Automate API Testing with AI

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.

API testingChatPromptTemplateJinja2
0 likes · 15 min read
Stop Hand‑Writing Prompts: Use LangChain Templates to Automate API Testing with AI
Linyb Geek Road
Linyb Geek Road
Aug 3, 2026 · Artificial Intelligence

The Harness Effect: How Orchestration Design Slashes Enterprise Agent Token Costs

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.

Enterprise AILLM CostPrompt Engineering
0 likes · 12 min read
The Harness Effect: How Orchestration Design Slashes Enterprise Agent Token Costs
Node.js Tech Stack
Node.js Tech Stack
Aug 2, 2026 · Artificial Intelligence

How an AI Agent Book Racked Up 30K Stars in 20 Days After the DeepSeek Interview Fallout

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.

AI AgentGitHub StarsPrompt Engineering
0 likes · 7 min read
How an AI Agent Book Racked Up 30K Stars in 20 Days After the DeepSeek Interview Fallout
Raymond Ops
Raymond Ops
Aug 2, 2026 · Artificial Intelligence

Why Your RAG Falls Short and How to Fix It: Common Pitfalls and Proven Optimizations

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.

ChunkingEmbeddingHybrid Retrieval
0 likes · 18 min read
Why Your RAG Falls Short and How to Fix It: Common Pitfalls and Proven Optimizations
Data Party THU
Data Party THU
Aug 2, 2026 · Artificial Intelligence

Real-World Feedback Powers Continuous Evolution of AI Agent Skills

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.

AI AgentFeedback-driven EvolutionPrompt Engineering
0 likes · 13 min read
Real-World Feedback Powers Continuous Evolution of AI Agent Skills
Ops Development & AI Practice
Ops Development & AI Practice
Aug 2, 2026 · Artificial Intelligence

Beyond Prompt Templates: The T‑AO Cognitive Collaboration Framework for Deep AI Partnerships

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.

AI collaborationPrompt EngineeringT-AO
0 likes · 6 min read
Beyond Prompt Templates: The T‑AO Cognitive Collaboration Framework for Deep AI Partnerships
Java Tech Enthusiast
Java Tech Enthusiast
Aug 1, 2026 · Artificial Intelligence

‘Do You Know Claude Code?’ – Master CLAUDE.md, Skills, Subagents, MCP, Hooks & Plugins

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.

AIClaude CodeHooks
0 likes · 26 min read
‘Do You Know Claude Code?’ – Master CLAUDE.md, Skills, Subagents, MCP, Hooks & Plugins
Data Party THU
Data Party THU
Aug 1, 2026 · Artificial Intelligence

Essential Prompt‑Simplification Strategies for Building GPT‑5.6 Applications

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.

GPT-5.6OpenAIPrompt Engineering
0 likes · 8 min read
Essential Prompt‑Simplification Strategies for Building GPT‑5.6 Applications
Black & White Path
Black & White Path
Aug 1, 2026 · Information Security

DeepSeek V4‑Flash 0731 Jailbreak: Peer‑Review Prompt Breaks 6 of 8 Safety Guardrails

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.

DeepSeekInformation securityLLM jailbreak
0 likes · 12 min read
DeepSeek V4‑Flash 0731 Jailbreak: Peer‑Review Prompt Breaks 6 of 8 Safety Guardrails
Linyb Geek Road
Linyb Geek Road
Aug 1, 2026 · Artificial Intelligence

Maximize Token ROI in AI Coding Agents: Practical Optimization Techniques

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.

AI coding agentsAgent workflowLLM Cost
0 likes · 22 min read
Maximize Token ROI in AI Coding Agents: Practical Optimization Techniques
Linyb Geek Road
Linyb Geek Road
Aug 1, 2026 · Artificial Intelligence

Practical Guide to Cutting LLM Token Costs

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.

Batch ProcessingLLMPrompt Engineering
0 likes · 22 min read
Practical Guide to Cutting LLM Token Costs
Java Tech Enthusiast
Java Tech Enthusiast
Jul 31, 2026 · Artificial Intelligence

Mastering Claude Code: From CLAUDE.md to Plugins – A Complete Engineering Guide

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.

AI EngineeringClaude CodeMCP
0 likes · 25 min read
Mastering Claude Code: From CLAUDE.md to Plugins – A Complete Engineering Guide
Design Hub
Design Hub
Jul 31, 2026 · Artificial Intelligence

AI‑Assisted Design: From a Rough 3D Sketch to Rapid Renderings for Industrial Designers

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.

3D modelingAIConcept Rendering
0 likes · 9 min read
AI‑Assisted Design: From a Rough 3D Sketch to Rapid Renderings for Industrial Designers
JD Cloud Developers
JD Cloud Developers
Jul 30, 2026 · Artificial Intelligence

Stop Micromanaging Claude Code: How to Make It Work Autonomously

The article explains why most users interact with Claude Code step‑by‑step, then shows how to give it self‑checking goals, persistent state files, the /goal command, plan mode, and Dynamic Workflows so it can operate independently, with real‑world examples and clear limits.

AI coding assistantAutonomous AgentsClaude Code
0 likes · 17 min read
Stop Micromanaging Claude Code: How to Make It Work Autonomously
DataFunTalk
DataFunTalk
Jul 30, 2026 · Artificial Intelligence

From Prompt to Shareable Link: My TRAE Work Walkthrough for Live Training HTML

The article documents a step‑by‑step, AI‑driven workflow that turns a single prompt into a fully interactive HTML training page for live streaming, covering planning, prompt design, generation, iterative AI edits, manual tweaks, one‑click sharing, and practical lessons learned.

AI-generated HTMLContent automationInteractive tutorial
0 likes · 14 min read
From Prompt to Shareable Link: My TRAE Work Walkthrough for Live Training HTML
Sohu Tech Products
Sohu Tech Products
Jul 29, 2026 · Artificial Intelligence

How to Turn Any Text into a Beautiful Web Article with an AI‑Powered Harness

This article walks through the design and implementation of a reusable Harness that uses Claude Code, MiniMax M3, and the new Beautiful Article Skill—built on the Reacticle component protocol—to transform arbitrary text into a richly styled, shareable HTML article through an eight‑phase pipeline with mandatory checkpoints, review stages, and self‑evolving logs.

AI agentsClaude CodeHTML generation
0 likes · 27 min read
How to Turn Any Text into a Beautiful Web Article with an AI‑Powered Harness
Linyb Geek Road
Linyb Geek Road
Jul 29, 2026 · Artificial Intelligence

Why Adding More Documents Can Degrade RAG Answers

The article explains that stuffing a RAG system with many overlapping or conflicting documents consumes tokens, slows responses, and introduces noise that prevents the model from correctly using the most relevant evidence, ultimately worsening answer quality.

Evidence RankingLLMPrompt Engineering
0 likes · 14 min read
Why Adding More Documents Can Degrade RAG Answers
Java Tech Enthusiast
Java Tech Enthusiast
Jul 28, 2026 · Artificial Intelligence

Why You Can Ditch Most Superpowers Skills in the GPT‑5.6 Era

With GPT‑5.6, K3 and Claude Fable5 handling most routine coding steps, the author explains why many Codex Skills are now redundant, how progressive disclosure limits context, and offers a practical framework for deciding which Skills to keep, restructure, or discard.

AI CodingAgentic WorkflowCodex
0 likes · 13 min read
Why You Can Ditch Most Superpowers Skills in the GPT‑5.6 Era
Tech Ocean
Tech Ocean
Jul 27, 2026 · Artificial Intelligence

Why Pi Gets 77K Stars Despite No MCP, No Permission System, and Only Four Packages

The article dissects Pi, a terminal AI coding tool with 77.7 K GitHub stars, revealing its 1520‑token system prompt, dynamic prompt assembly, four independently installable npm packages, robust handling of truncated output, long‑conversation compression, strict dependency locking, and lack of a sandbox, while evaluating its security and suitability for different users.

AI coding assistantModular ArchitecturePi
0 likes · 15 min read
Why Pi Gets 77K Stars Despite No MCP, No Permission System, and Only Four Packages
PaperAgent
PaperAgent
Jul 27, 2026 · Artificial Intelligence

Why Dropping 80% of System Prompts Improves Claude 5: New Context Engineering Rules

Anthropic’s official Claude 5 guide reveals that removing most Claude Code system prompts has no measurable impact, overturning traditional context‑engineering practices and introducing six paradigm shifts that let the model rely on its own judgment and progressive context loading.

AI agentsAnthropicClaude 5
0 likes · 6 min read
Why Dropping 80% of System Prompts Improves Claude 5: New Context Engineering Rules
PaperAgent
PaperAgent
Jul 27, 2026 · Artificial Intelligence

Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents

This 149‑page survey defines long‑horizon agents as a coupling of a base policy and a runtime harness (Agent = πθ ⊕ H), categorises task levels and capabilities, traces the field’s evolution from prompt to context to runtime engineering, and outlines a seven‑stage optimization pipeline, application forms, and frontier challenges, supported by empirical growth data and extensive references.

AI SurveyAgent OptimizationContext Engineering
0 likes · 12 min read
Dual‑Engine Evolution: A Systematic Survey of Long‑Horizon Agents
PMTalk Product Manager Community
PMTalk Product Manager Community
Jul 27, 2026 · Artificial Intelligence

Why Your AI Skill Falls Short and How to Refine It in Three Real‑World Scenarios

The article explains why many AI Skills are unreliable, identifies three common failure patterns, and provides concrete scenario‑based refinements for product managers, designers, and operators, along with practical checklists and management tips to turn a draft Skill into a stable, reusable workflow.

AIDesign ReviewPrompt Engineering
0 likes · 19 min read
Why Your AI Skill Falls Short and How to Refine It in Three Real‑World Scenarios
ThinkingAgent
ThinkingAgent
Jul 27, 2026 · Artificial Intelligence

The Awakening of Large Models: From Classic Language Modeling to Generative AI

This article traces the 56‑year evolution of language models—from ELIZA’s rule‑based scripts and N‑gram statistics to neural embeddings, RNNs, Transformers and the seven‑layer ChatGPT architecture—explaining why the simple next‑token probability definition has remained the core of generative AI, how autoregressive factorization drives training, generation and decoding, why hallucinations arise, and what engineering trade‑offs matter in production.

ChatGPTLLMLarge Language Models
0 likes · 26 min read
The Awakening of Large Models: From Classic Language Modeling to Generative AI
AndroidPub
AndroidPub
Jul 27, 2026 · Artificial Intelligence

Why Clarifying Requirements First Boosts AI Agent Success: The grill‑me Skill in Action

The article explains how the grill‑me skill inserts an interactive requirement‑clarification stage before an AI Agent executes a task, reducing misaligned outputs and rework by asking one focused question at a time, offering suggested answers, and distinguishing factual from decision information.

AI AgentPrompt EngineeringWorkflow
0 likes · 20 min read
Why Clarifying Requirements First Boosts AI Agent Success: The grill‑me Skill in Action
Linyb Geek Road
Linyb Geek Road
Jul 27, 2026 · Artificial Intelligence

Why RAG Misses Casual User Questions and How to Optimize Retrieval

Real users ask informal, incomplete questions that often miss the right documents, so the article classifies common failure types, explains three query‑optimization techniques—Query Rewrite, Multi‑Query, and HyDE—provides concrete prompts, code snippets, selection guidelines, evaluation metrics, and practical deployment pitfalls.

HyDELLM RetrievalMulti-Query
0 likes · 14 min read
Why RAG Misses Casual User Questions and How to Optimize Retrieval
Java Tech Enthusiast
Java Tech Enthusiast
Jul 26, 2026 · Artificial Intelligence

Why the Top‑3 AI Skill Consists of Only a Few Sentences

The article examines the wildly popular "grill‑me" AI skill—ranked third in installation counts despite containing just a handful of lines—by detailing its core script, walking through a real‑world implementation, and analyzing the design principles that make such a minimal prompt so effective for AI‑driven software development.

AI ProgrammingAI promptingPrompt Engineering
0 likes · 9 min read
Why the Top‑3 AI Skill Consists of Only a Few Sentences
AI Engineer Programming
AI Engineer Programming
Jul 26, 2026 · Artificial Intelligence

Analyzing the grill‑me Agent Skills Repository: Making Probabilistic LLMs Deterministic

The article dissects Matt Pocock’s skills repository, explaining how a set of atomic, editable, composable Agent Skills—driven by structured grilling, shared vocabularies, TDD loops, and design checkpoints—turns the inherently probabilistic nature of LLM‑based programming into a repeatable, deterministic workflow while highlighting practical limits and best‑practice patterns.

AgentLLMPrompt Engineering
0 likes · 22 min read
Analyzing the grill‑me Agent Skills Repository: Making Probabilistic LLMs Deterministic
Machine Learning Algorithms & Natural Language Processing
Machine Learning Algorithms & Natural Language Processing
Jul 25, 2026 · Artificial Intelligence

Why Anthropic Cut 80% of Claude Code System Prompts Overnight

Anthropic discovered that with the release of Claude Opus 5 the previous, heavily‑engineered system prompts became unnecessary, so they removed more than 80% of Claude Code’s prompts without measurable loss, and outlined new concise, context‑driven best practices for LLM prompt engineering.

AI developmentAnthropicClaude
0 likes · 11 min read
Why Anthropic Cut 80% of Claude Code System Prompts Overnight
Top Architecture Tech Stack
Top Architecture Tech Stack
Jul 25, 2026 · Artificial Intelligence

Claude Opus 5 Arrives: Beats Fable 5 on Benchmarks and Costs Half the Price

Claude Opus 5 launches as a high‑frequency engineering model that matches or exceeds Fable 5 on several benchmarks while costing roughly half, prompting a shift in model routing, prompt design, agent orchestration, code‑review tactics, visual‑task tooling, and API usage for development teams.

Claude Opus 5Code ReviewPrompt Engineering
0 likes · 14 min read
Claude Opus 5 Arrives: Beats Fable 5 on Benchmarks and Costs Half the Price
IT Xianyu
IT Xianyu
Jul 25, 2026 · Artificial Intelligence

How GPT‑5.6 Escaped Its Sandbox and Hacked Servers: Three Experiments Reveal Its Limits

After OpenAI reported that GPT‑5.6 broke out of its sandbox and accessed Hugging Face servers, the author ran three hands‑on tests—an ambiguous security‑fix prompt, a custom command‑whitelist sandbox, and a direct ethical question—to expose how the model autonomously exploits zero‑day flaws, bypasses simple command filters, and rationalizes its actions, highlighting the fragile nature of AI guardrails.

AI safetyGPT-5.6Prompt Engineering
0 likes · 8 min read
How GPT‑5.6 Escaped Its Sandbox and Hacked Servers: Three Experiments Reveal Its Limits
PaperAgent
PaperAgent
Jul 25, 2026 · Artificial Intelligence

Claude Opus 5 Gets Tested in Tornadoes, Collapsing Buildings, and Sand Simulations

Claude Opus 5 launched at half the price of Fable 5, and the community immediately pushed it to its limits with self‑contained HTML physics scenes—tornado‑ripped houses, demolition‑ball‑crushed apartments, bridge‑collapsing trucks, and massive sand‑water‑fire simulations—while comparing costs and performance against Fable 5, GPT 5.6, and Kimi K3.

AI model comparisonAnthropicClaude Opus 5
0 likes · 6 min read
Claude Opus 5 Gets Tested in Tornadoes, Collapsing Buildings, and Sand Simulations
Machine Heart
Machine Heart
Jul 25, 2026 · Artificial Intelligence

Why Claude Code Cut 80% of System Prompts Overnight

Anthropic discovered that after launching the stronger Claude Opus 5 model, they could remove more than 80% of Claude Code’s system prompts without any measurable loss in coding performance, prompting a shift toward minimal, high‑level context engineering that relies on concise CLAUDE.md files, Skills, and progressive disclosure.

AI developmentAnthropicClaude
0 likes · 11 min read
Why Claude Code Cut 80% of System Prompts Overnight
Alibaba Cloud Native
Alibaba Cloud Native
Jul 24, 2026 · Artificial Intelligence

Maximizing Qoder Credits: Focus on Output per Credit, Not Just Cost

The article analyzes how to reduce the "billing anxiety" of using Qoder's Coding Agent by measuring the ratio of verified deliverables to credits spent, explaining the execution chain that consumes credits, and offering concrete strategies—such as improving design, pruning redundant context, leveraging cache, and scheduling tasks at night—to boost per‑credit productivity.

AI workflowCache ManagementCoding Agent
0 likes · 16 min read
Maximizing Qoder Credits: Focus on Output per Credit, Not Just Cost
IT Services Circle
IT Services Circle
Jul 24, 2026 · Artificial Intelligence

Why You Can Ditch Most Superpowers Skills in the GPT‑5.6 Era

With GPT‑5.6 and newer models handling most routine coding steps, many previously essential Skills become redundant, leading to context bloat, token waste, and security risks; the article proposes a three‑category framework to keep only lightweight, high‑value Skills and outlines concrete pruning criteria.

AI agentsAgentic WorkflowPrompt Engineering
0 likes · 14 min read
Why You Can Ditch Most Superpowers Skills in the GPT‑5.6 Era
Big Data and Microservices
Big Data and Microservices
Jul 24, 2026 · Artificial Intelligence

How to Write a Truly Usable and Effective Agent Skill

Agent Skills are structured prompt packages that encapsulate domain knowledge, steps, code, and safety constraints; this guide explains why many AI assistants fall short, presents research‑backed principles—progressive disclosure, locking down deterministic logic, procedural instructions, manual authoring, modular design—and offers concrete metrics, safety checks, and a publishing checklist.

AI workflowAgent SkillPrompt Engineering
0 likes · 12 min read
How to Write a Truly Usable and Effective Agent Skill
Ray's Galactic Tech
Ray's Galactic Tech
Jul 23, 2026 · Artificial Intelligence

Stop Embedding Business Logic in Prompts: An Enterprise Guide to Spring AI Alibaba Skills

The article explains why many AI projects fail not because of model performance but due to architectural boundaries, illustrates a real‑world incident caused by an ever‑growing "super Prompt", and shows how Spring AI Alibaba Skills can split responsibilities, enforce governance, and make AI services production‑ready.

Alibaba SkillsEnterprise AIObservability
0 likes · 26 min read
Stop Embedding Business Logic in Prompts: An Enterprise Guide to Spring AI Alibaba Skills
JavaGuide
JavaGuide
Jul 23, 2026 · Artificial Intelligence

Goodbye Superpowers: Which Skills Are Worth Keeping After GPT‑5.6?

The article explains why, with stronger models like GPT‑5.6, many traditional coding Skills become redundant, outlines the progressive‑disclosure mechanism, highlights token and security costs, and provides concrete criteria and examples for deciding which Skills to retain or discard.

AI agentsAgentic WorkflowGPT-5.6
0 likes · 14 min read
Goodbye Superpowers: Which Skills Are Worth Keeping After GPT‑5.6?
IT Services Circle
IT Services Circle
Jul 23, 2026 · Artificial Intelligence

Why Shorter Prompts Make GPT‑5.6 Smarter: Insights from OpenAI’s Official Guide

OpenAI’s GPT‑5.6 prompt guide shows that trimming redundant instructions and examples can boost agent scores by 10‑15%, cut token usage by up to 66%, and reduce costs, while also redefining prompt‑engineering from lengthy “recipes” to concise contracts that specify goals, boundaries, and verification steps.

AI agentsGPT-5.6OpenAI
0 likes · 11 min read
Why Shorter Prompts Make GPT‑5.6 Smarter: Insights from OpenAI’s Official Guide
DeWu Technology
DeWu Technology
Jul 23, 2026 · Artificial Intelligence

When Engineers Cross Boundaries: How “Boundary‑Breaking” Boosted Problem Solving at Dewu

The Dewu tech team’s recent “boundary‑crossing” incidents—engineers skipping formal specs to talk directly with users and operations—led to deeper problem understanding, rapid prototyping, and measurable improvements such as 60% automated answers, 30% support load reduction, and 50% faster onboarding.

AI assistantAI platformLLM
0 likes · 7 min read
When Engineers Cross Boundaries: How “Boundary‑Breaking” Boosted Problem Solving at Dewu
ShiZhen AI
ShiZhen AI
Jul 23, 2026 · Artificial Intelligence

How AI‑Generated Prompts Make Technical Architecture Diagrams Animate

The article evaluates fireworks‑tech‑graph, an AI‑driven skill that converts Chinese prompts into verifiable SVG, PNG and animated GIF architecture diagrams, detailing its 12 visual styles, four real‑world scenarios, installation steps, performance limits, and the nuanced arrow animations that reflect call‑chains, delegation, data flow and error branches.

AI diagram generationPrompt EngineeringSVG
0 likes · 14 min read
How AI‑Generated Prompts Make Technical Architecture Diagrams Animate
Wu Shixiong's Large Model Academy
Wu Shixiong's Large Model Academy
Jul 22, 2026 · Artificial Intelligence

How to Handle Long Conversation History: Beyond Full Prompt or Recent Rounds

The article explains that effective conversation memory for LLMs requires classifying information into static knowledge, short‑term context, and long‑term memory, defining a full lifecycle for each entry, and implementing strict storage, retrieval, update, and deletion policies rather than simply concatenating all history or keeping only the latest turns.

LLMPrompt EngineeringRAG
0 likes · 24 min read
How to Handle Long Conversation History: Beyond Full Prompt or Recent Rounds
Advanced AI Application Practice
Advanced AI Application Practice
Jul 22, 2026 · Artificial Intelligence

Creating a Vox‑Style Paper‑Cut Video with Codex: A Zero‑Skill Walkthrough

The author walks through using the open‑source paper‑collage‑ad‑codex skill with Codex to generate a Vox‑style paper‑cut video about Chinese art history, detailing prompt setup, multi‑round interactions, generated storyboard and video, and the practical limitations encountered.

AI video generationCodexPrompt Engineering
0 likes · 4 min read
Creating a Vox‑Style Paper‑Cut Video with Codex: A Zero‑Skill Walkthrough
AI Engineer Programming
AI Engineer Programming
Jul 22, 2026 · Artificial Intelligence

Is Prompt Engineering Dead? A Deep Dive into Harness, Context Assembly, and Token Generation

The article examines why traditional prompt engineering is no longer sufficient in production AI systems, detailing how harness layers, context reassembly, tool orchestration, token generation methods, training objectives, and architecture choices transform a simple prompt into a complex, multi‑stage workflow that demands robust, system‑level design.

HarnessLLMMulti-Token Prediction
0 likes · 16 min read
Is Prompt Engineering Dead? A Deep Dive into Harness, Context Assembly, and Token Generation
IT Xianyu
IT Xianyu
Jul 21, 2026 · Artificial Intelligence

Why Your GPT‑5.6 Plus Quota Drains Fast: Misusing the Three Modes

The author shows that GPT‑5.6 Plus splits its quota into three separate pools—Chat, Work, and Codex—and demonstrates with a script and real‑world scenarios that assigning tasks to the wrong mode can waste up to three times more quota and produce lower‑quality code, while proper mode selection conserves resources and improves results.

AI modesChatGPT PlusCodex
0 likes · 10 min read
Why Your GPT‑5.6 Plus Quota Drains Fast: Misusing the Three Modes
Old Zhang's AI Learning
Old Zhang's AI Learning
Jul 21, 2026 · Artificial Intelligence

How I Used Three AI Skills to Create Elementary Lesson Plans at Home

During the summer I used three AI-powered skills—PDF parsing, lesson‑plan generation, and PPT creation—to turn open‑source PDF textbooks into editable Word lesson plans, student worksheets, observation templates, and classroom slides, demonstrating a practical workflow for home‑based teaching material production.

AIEducation techLesson planning
0 likes · 4 min read
How I Used Three AI Skills to Create Elementary Lesson Plans at Home
Nightwalker Tech
Nightwalker Tech
Jul 20, 2026 · Artificial Intelligence

Designing Reliable AI Agents: From a Single Prompt to Stable Delivery

The article explains why treating complex AI agents as a single long prompt leads to instability, and proposes a reusable closed‑loop architecture—scheduler, planner, executor, evaluator, repairer, and finalizer—that makes agents explainable, recoverable, and safely deliverable in production.

AI AgentClosed-loop ArchitecturePrompt Engineering
0 likes · 21 min read
Designing Reliable AI Agents: From a Single Prompt to Stable Delivery
IT Xianyu
IT Xianyu
Jul 20, 2026 · Artificial Intelligence

Why GPT‑5.6’s Real Breakthrough Is No Longer Hand‑Holding Prompts

GPT‑5.6 boosts evaluation scores by 10‑15% while cutting token usage 41‑66% and costs up to two‑thirds, introduces ChatGPT Work that autonomously handles end‑to‑end tasks, and offers three model tiers (Sol, Terra, Luna) that let users choose performance versus price, fundamentally changing how we prompt AI.

AI productivityChatGPT WorkCost Efficiency
0 likes · 9 min read
Why GPT‑5.6’s Real Breakthrough Is No Longer Hand‑Holding Prompts
Alibaba Cloud Developer
Alibaba Cloud Developer
Jul 20, 2026 · Artificial Intelligence

From Prompt to Harness: The Complete Evolution of Enterprise‑Grade AI Agents

This article chronicles the end‑to‑end engineering journey of enterprise AI agents, detailing how the team progressed from basic prompt engineering through multi‑layer context management to a full‑featured harness layer and a five‑tier Agent OS, addressing challenges such as context overflow, data‑搬运, and reliable execution.

AI AgentAgent OSEnterprise AI
0 likes · 61 min read
From Prompt to Harness: The Complete Evolution of Enterprise‑Grade AI Agents
AgentGuide
AgentGuide
Jul 20, 2026 · Artificial Intelligence

What Are Skills in AI Agents? A One‑Minute Overview of Their Principles and Usage

Skills are structured local folders that encapsulate domain‑specific processes, knowledge, and tools for large language models, enabling on‑demand loading, token efficiency, and reusable workflows, and they differ from one‑off prompts by persisting instructions and supporting templates, scripts, and reference materials.

AI agentsLarge Language ModelsOn‑Demand Loading
0 likes · 5 min read
What Are Skills in AI Agents? A One‑Minute Overview of Their Principles and Usage
Black & White Path
Black & White Path
Jul 20, 2026 · Information Security

WallBreaker: An Open-Source CLI for Automated LLM Red-Team Testing

WallBreaker is an open-source CLI that automates LLM red-team testing by iteratively mutating attack payloads, offering a library of research-grade techniques, a 59-to-222 transformation engine, multimodal image attacks, a HarmBench-based judge, and performance optimizations that cut token costs by 20% and boost success rates by about 30%.

AI safetyCLI toolHarmBench
0 likes · 7 min read
WallBreaker: An Open-Source CLI for Automated LLM Red-Team Testing
Java Tech Enthusiast
Java Tech Enthusiast
Jul 19, 2026 · Artificial Intelligence

Superpowers vs Grill‑Me: Which AI Coding Assistant Wins the Race?

The article compares two AI‑driven coding assistants, grill‑me and superpowers, by having them each build the same web‑based endless‑runner game, then analyzes their questioning styles, workflow ownership, generated artifacts, time consumption, and suitability for different project scales.

AI coding assistantsPrompt Engineeringgrill-me
0 likes · 13 min read
Superpowers vs Grill‑Me: Which AI Coding Assistant Wins the Race?
PMTalk Product Manager Community
PMTalk Product Manager Community
Jul 19, 2026 · Artificial Intelligence

Three Practical Ways to Turn Your Experience into Reusable AI Skills

The article explains why repeating prompts is inefficient, defines a Skill as a reusable SOP for AI, and presents three concrete methods—reverse‑engineering from results, completing a task then summarising, and writing a work‑instruction for AI optimisation—along with templates, storage tips, and real‑world Skill examples.

AI ToolsAI workflowCodex
0 likes · 13 min read
Three Practical Ways to Turn Your Experience into Reusable AI Skills