Skills vs Coze vs Dify: A Hands-On Comparison — Which AI Tool Is Best for Auto-Generating Test Cases?

The Efficiency Revolution for Test Engineers: From Manual to AI

In the software testing world, an undeniable trend is accelerating — AI agents are reshaping how test engineers work. Traditional tasks like manually writing test cases, organizing requirements documents, and generating performance reports used to consume enormous amounts of time. With AI agents, these tasks can be compressed from "half a day" to "a few minutes."

So what exactly is an AI agent? In simple terms: you feed it a requirements document, and it automatically generates test cases; after a performance test run, it automatically produces an analysis report; when developers need to look up requirements, you set up a bot and let them ask it directly. This isn't hype — it's already being put into practice today.

From a technical perspective, an AI agent is an autonomous task execution system built on large language models (LLMs). Unlike traditional chatbots, AI agents have capabilities like goal decomposition, tool invocation, memory management, and autonomous decision-making. Their core architecture typically includes: a perception layer (receiving user input), a planning layer (breaking complex tasks into subtasks), an execution layer (calling external tools or APIs to complete specific operations), and a feedback layer (evaluating results and iterating). In the testing domain, AI agents can understand the semantics of requirements documents, automatically identify boundary conditions for testing, and generate structured output following established testing methodologies (such as equivalence partitioning and boundary value analysis).

Today, we'll take a deep dive into three mainstream AI agent building tools — Skills, Coze, and Dify — to see what scenarios each is best suited for and how to use them to build your own AI testing assistant.

Why Should Test Engineers Learn About AI Agents?

Before discussing specific tools, let's answer a fundamental question: what's the real benefit of learning this?

Efficiency: Time Is Productivity

Writing a complete set of test cases used to take half a day. Now, with AI agents, you just input the PRD (Product Requirements Document) and get structured test cases in minutes. The time saved can be redirected to more valuable exploratory testing or test strategy optimization.

The automatic conversion from PRD to test cases relies on the LLM's natural language understanding and structured output capabilities. The model first performs semantic parsing on the PRD, extracting key information such as functional points, business rules, and input/output constraints. It then automatically generates test scenarios based on test design methods (such as equivalence partitioning, boundary value analysis, decision tables, and state transition methods). Through Prompt Engineering techniques, the model can be guided to output standardized test case formats that include preconditions, test steps, and expected results. This means the AI isn't just "translating" requirements — it's performing test design reasoning based on an understanding of business logic.

Career: From "Click Monkey" to "AI Test Engineer"

Going from "manual test engineer" to "test engineer who can build AI agents" on your resume puts you in an entirely different league in an interviewer's eyes. In today's market, test engineers who understand AI testing and can build agents are still scarce talent. Getting ahead of the curve means stronger competitiveness and greater salary negotiation power.

From an industry trend perspective, Gartner predicts that by 2025, over 30% of testing activities will be AI-assisted. This means test engineers who don't master AI tools will face an ever-widening efficiency gap. Engineers who can build AI agents won't just boost their own productivity — they can build automated workflows for their entire team, evolving their role from "executor" to "enabler."

In-Depth Comparison of Three AI Testing Tools

Skills: A Purpose-Built Tool for Test Engineers

Skills is an AI tool designed specifically for test engineers, offering four core hands-on projects:

• Auto-Generate Test Cases from PRD: Input a product requirements document directly, and the AI automatically extracts test points and generates structured test cases
• Auto-Convert to XMind Mind Maps: One-click conversion of test cases into visual mind maps for easy review and communication
• One-Click Performance Analysis Reports: Automatic analysis of performance test data with professional report output
• Auto-Complete Requirements Checklist: Helps discover gaps in requirements documents

Skills' core advantage lies in its deep optimization for the testing domain. Unlike general-purpose AI tools, Skills has built-in testing expertise, including common test design methods, defect classification standards, and performance metric interpretation rules. This means the test cases it generates aren't simple requirement restatements but professional output guided by testing methodology. For example, when generating performance analysis reports, it can automatically identify anomalies in P95/P99 response time percentiles, throughput bottlenecks, error rate trends, and other key metrics, along with optimization recommendations.

Best for: Test engineers who want to get started quickly without fussing over configuration. Skills' strength is its out-of-the-box readiness with deep optimization for testing scenarios.

Coze: A No-Code Platform for Building AI Agents

Coze is an AI agent platform from ByteDance, and its biggest feature is that no coding is required. It offers seven hands-on projects covering all aspects of testing work:

• Requirements Document to XMind: Mind map generation similar to Skills
• Requirements Q&A Bot: Developers can ask the bot questions directly instead of repeatedly checking with testers
• Generate Excel Test Cases: Directly outputs usable test case documents in Excel format
• Natural Language Database Queries: Query databases using natural language descriptions instead of writing SQL
• AI Mock Interviewer: Helps prepare for testing position interviews
• Performance Results Analysis: Automated performance data interpretation
• API Requirements Analysis and Debugging: Intelligent assistance for API testing

Behind Coze's no-code capability is a visual orchestration engine where users define workflows by dragging and connecting nodes. The platform internally translates these visual operations into LLM prompt chains, API call sequences, and conditional branching logic. This approach essentially abstracts traditional programming concepts like function calls, conditionals, and loops into graphical components. The plugin system integrates with external services through standardized interface protocols (typically the OpenAPI specification), enabling non-technical users to build complex automated workflows. For example, you can chain together the entire flow of "receive requirements document → extract functional points → generate test cases → output Excel" through drag-and-drop, without writing a single line of code.

The "natural language database query" feature deserves special mention. This technology (also known as Text-to-SQL or NL2SQL) works as follows: the LLM receives a user's natural language query (e.g., "Show me the number of new bugs from last week grouped by module"), combines it with pre-injected database schema information (table structures, field definitions, table relationships), and automatically generates and executes the corresponding SQL query. The challenge lies in accuracy for complex queries — scenarios involving multi-table joins, nested subqueries, and aggregate functions require Few-shot examples and schema description optimization to improve generation quality. For test engineers, this means you can quickly retrieve test data and defect statistics without mastering complex SQL syntax.

Best for: Test engineers who don't want to touch code and prefer building agents through visual drag-and-drop. Coze has a mature ecosystem with rich plugins and a low barrier to entry.

Dify: An Open-Source, Self-Hostable Option for Deep Customization

Dify is an open-source platform suited for users with some technical background who want deep customization. It also offers seven hands-on projects:

• Voice Interviewer: Mock interviews with voice interaction support
• Knowledge Base Application: Build a team-specific testing knowledge base
• Product Q&A Bot: Intelligent Q&A based on product documentation
• Requirements to Test Cases: Core test case generation capability
• Natural Language Database Queries: Natural language data querying similar to Coze

Dify's open-source nature means its source code is fully public (under the Apache 2.0 license), and enterprises can deploy the entire system on their own servers or private cloud. This is especially important for industries with strict data compliance requirements, such as finance, healthcare, and government, since all data (including requirements documents, test cases, performance data, etc.) never leaves the enterprise's network boundary. Private deployment also allows enterprises to fine-tune models using internally accumulated testing assets (such as historical test cases, defect databases, and best practice documents) to train custom models, resulting in output quality that better fits their business scenarios.

Additionally, Dify supports integration with multiple LLMs (such as GPT-4, Claude, open-source Llama, Qwen, etc.), allowing enterprises to flexibly choose the underlying model based on cost, performance, and data security needs. Its RAG (Retrieval-Augmented Generation) engine supports knowledge base construction from multiple document formats, which is particularly valuable for building team testing knowledge bases — you can import all of your team's accumulated testing standards, historical defect cases, and product architecture documents, enabling the AI agent to reference this internal knowledge when generating test cases, significantly improving output accuracy and business relevance.

Best for: The technically inclined who want to tinker and deeply customize. Dify's open-source nature means you have full control over data and deployment, making it ideal for enterprise scenarios with data security requirements.

Skills, Coze, or Dify — How to Choose? A Quick Comparison Table

Dimension	Skills	Coze	Dify
Learning Curve	⭐⭐	⭐	⭐⭐⭐
Customization Flexibility	Medium	Medium	High
Coding Required?	Mostly not	Not at all	Partially
Best Scenario	Testing-specific	General purpose	Enterprise customization
Data Security	Platform-hosted	Platform-hosted	Self-hostable
Model Selection	Platform built-in	Platform built-in	Multiple models available
Knowledge Base Capability	Limited	Moderate	Powerful (RAG)
Community Ecosystem	Testing-focused community	ByteDance ecosystem	Open-source community

Quick Summary:

• If you're new to testing and want to quickly experience AI-powered efficiency → Choose Coze
• If you're a test engineer looking for professional testing-specific tools → Choose Skills
• If you have a technical background and want deep customization or on-premise deployment → Choose Dify

Practical Advice: Three Steps from Getting Started to Going Live

Step 1: Start with One Scenario

Don't try to do everything at once. Pick your biggest pain point first. For example, "PRD to test cases" — this is something nearly every test engineer does daily, and the efficiency gains are immediately visible.

In practice, start by testing with a real PRD document and compare the AI-generated cases against manually written ones. Pay special attention to whether the AI covers boundary conditions, edge cases, and non-functional requirements. Initially, AI-generated cases may need manual review and supplementation, but as you refine your prompts and build out your knowledge base, the output quality will continuously improve.

Step 2: Gradually Expand Your Scope

Once you're comfortable with one scenario, gradually expand to performance analysis reports, requirements Q&A bots, and other use cases. Each new agent application you master takes your work efficiency up another notch.

Expand in order of "high frequency, low complexity → low frequency, high complexity." For example: start with test case generation (used daily), then move to performance report analysis (used weekly), and finally build a requirements Q&A bot (which requires knowledge base construction). This way, you get positive feedback at each stage and avoid getting discouraged by tackling high-difficulty scenarios right away.

Step 3: Build a Team Collaboration Workflow

The ultimate goal is to integrate AI agents into your team's daily workflow. For example, developers use the requirements Q&A bot for self-service queries, product managers use natural language database queries to pull data, and test engineers focus on higher-value test strategy and quality assurance work.

When rolling out at the team level, keep a few key points in mind: first, establish a quality review mechanism for AI output to ensure accuracy; second, continuously maintain and update the knowledge base so the AI agent's output stays current; third, collect team feedback and continuously optimize prompts and workflow configurations. Successful AI adoption isn't a one-time tool deployment — it's a process of continuous iteration.

Final Thoughts

The impact of AI agents on the testing industry isn't a question of "should I learn this" — it's a question of "when." Evolving from a manual executor to a master of AI tools is an inevitable step in the career progression of test engineers.

Each of the three tools has its strengths. The key is to take action. Pick the tool that suits you best, start with one hands-on project, and you'll find that the world of AI-powered testing is easier to enter than you might think. It's worth noting that AI agents aren't here to replace test engineers — they're here to automate repetitive documentation work, freeing test engineers to invest more energy in test strategy design, exploratory testing, user experience evaluation, and other high-value work that demands human judgment and creativity.