Essential AI Skills for Testers (With Download Links & Detailed Usage)#

1. What is this skill for?#

Webapp Testing Skill is an official Web application testing tool launched by Anthropic. With this skill, you only need to tell AI “test the login function” or “verify the form submission process”, and it will complete the testing automatically.

For example, if you want to test a locally developed e-commerce website, this skill will automatically launch Playwright, access your local service, simulate user operations, and then generate a test report. It can also take automatic screenshots to record the results of each step.

Its core capability is to control real browsers through Playwright to achieve automated testing of Web applications. It can complete page verification, user interaction simulation, screenshot checks and other operations without manually writing complex Playwright or Selenium scripts.

Its biggest advantage is “understanding testing intent”: it can automatically handle common challenges in Web testing such as dynamic rendering and asynchronous waiting, distinguish between static HTML and dynamic Web applications, select the optimal test path, and even automatically manage the lifecycle of development servers to form a complete closed loop of “write code → check effect → modify code”. It is especially suitable for front-end testing and full-stack testing scenarios.

2. Core Principles#

The working principle of Webapp Testing Skill is actually that Anthropic encapsulates the best practices of Playwright and common testing scenarios into the skill. It does not simply execute commands, but can understand testing intent, automatically select appropriate selector strategies, and handle dynamically loaded content.

This skill adopts a “Reconnaissance-Action” mode, with a two-step core process and support for a variety of practical scenarios:

• Reconnaissance Phase: Automatically launch a headless Chromium browser, navigate to the target page and wait for network idle (to ensure JS execution is complete), and grasp the current page state through screenshots, obtaining page content, discovering DOM elements, etc.
• Action Phase: Based on reconnaissance results, simulate user operations (click buttons, fill in forms, select options, etc.), verify operation results, and capture console logs for debugging JavaScript errors at the same time.
• Additional Features: Automatically manage development servers through the with_server.py script, supporting single-server and multi-server (front-end and back-end separated projects) scenarios. The server is automatically shut down after the script is executed, no manual start and stop required.

In short, this skill turns the UI testing experience of professional test engineers into knowledge that AI can understand and execute. For front-end developers, there is no need to spend time learning complex testing frameworks to quickly verify whether functions work properly.

3. Practical Usage#

Scenario 1: Quick verification of local applications

1
# Single-server scenario: Start the front-end service and run tests
2
python scripts/with_server.py \
3
  --server "npm run dev" \
4
  --port 5173 \
5
  -- python your_test.py

Scenario 2: Full-stack application testing (start front-end and back-end simultaneously)

1
# Multi-server scenario: Manage back-end API and front-end service at the same time
2
python scripts/with_server.py \
3
  --server "cd backend && python server.py" --port 3000 \
4
  --server "cd frontend && npm run dev" --port 5173 \
5
  -- python e2e_test.py

Scenario 3: Zero-code page check Users only need to say: “Test my XX website xxx.com”, and AI will automatically execute:

1. Launch the browser to access the page
2. Discover all interactive elements (22 buttons, 6 links)
3. Generate multi-device responsive screenshots
4. Check console errors (0 errors, 0 warnings)
5. Output SEO inspection report

Scenario 4: Reconnaissance-Action mode This skill emphasizes a test process of reconnaissance first, then action.

1
from playwright.sync_api import sync_playwright
2

3
with sync_playwright() as p:
4
    browser = p.chromium.launch(headless=True)
5
    page = browser.new_page()
6

7
    # 1. Reconnaissance Phase: Navigate and wait for network idle
8
    page.goto('http://localhost:5173')
9
    page.wait_for_load_state('networkidle')  # Key! Wait for JS execution to complete
10

11
    # 2. Identification Phase: Get page elements
12
    buttons = page.locator('button').all()
13
    print(f"Found {len(buttons)} buttons")
14

15
    # 3. Action Phase: Execute test operations
16
    page.click('text=Submit')
17
    page.screenshot(path='result.png')
18

19
    browser.close()

Pitfall Guide: For dynamic applications, be sure to wait for networkidle before checking the DOM, otherwise dynamically rendered elements may not be captured.

1. What is this skill for?#

This TDD Skill comes from the Superpowers skill library of obra, solving the problem that “AI writes code fast, but writes tests slowly (or skips them directly)”.

It is equivalent to a “strict TDD coach” that forces AI to follow the test-driven development process with iron rules. It can force developers to follow TDD best practices, including the Red-Green-Refactor cycle, YAGNI (You Aren’t Gonna Need It) and DRY (Don’t Repeat Yourself) principles. It helps developers develop the habit of test-first and small-step iteration, and is especially suitable for teams and individuals who want to learn TDD or struggle to stick to standardized processes.

AI tends to think that “if the code is written, it should work”, but this skill enforces the following rules:

2. Core Usage#

The core of this TDD Skill is to guide developers to complete the closed-loop process of “Red-Green-Refactor” with clear and implementable steps, no need to control the rhythm manually:

1. Confirm Requirements: First clarify the minimum functional unit to be implemented currently to avoid aimless coding.
2. Red: Automatically write failed test cases (no business code at this time, tests will inevitably fail) to clarify functional boundaries and expected behaviors.
3. Green: Guide the writing of minimal business code that only meets the requirements of the current test cases to ensure the test passes.
4. Refactor: On the premise that all test cases pass, guide the optimization of code structure, elimination of redundancy, and improvement of readability without changing code functions.
5. Iterative Cycle: Repeat the above steps until the complete function is implemented, following the principle of small-step iteration throughout, completing only one minimum functional unit each time to quickly discover and locate problems.

3. Practical Usage#

For example, a simple TDD example for a Python project:

1
# Step 1: Write a failing test (Red)
2
def test_calculator_add():
3
    calc = Calculator()
4
    assert calc.add(2, 3) == 5  # The Calculator class does not exist at this time, test fails
5

6
# Step 2: Run the test and confirm failure
7
# pytest calculator_test.py -> FAILED
8

9
# Step 3: Write minimal implementation (Green)
10
class Calculator:
11
    def add(self, a, b):
12
        return a + b  # Simplest implementation
13

14
# Step 4: Run the test and confirm pass
15
# pytest calculator_test.py -> PASSED
16

17
# Step 5: Refactor (keep green)
18
class Calculator:
19
    def add(self, a: int, b: int) -> int:
20
        """Return the sum of two numbers"""
21
        return a + b  # Add type annotations and documentation without changing behavior

4. Why Do We Need This Skill?#

Traditional TDD often encounters the following problems in practice:

• Naming Confusion: Inconsistent naming of test classes and methods, difficult to maintain.
• Complex Tool Chain: Tedious configuration of JUnit, Mockito, JaCoCo.
• Refactoring Fear: Worry about refactoring breaking existing functions.

This skill ensures the following through structural enforcement:

• The test class corresponds to the tested class in naming (e.g., Calculator corresponds to CalculatorTest).
• Test methods use Given/When/Then syntax (e.g., test_shouldReturnSum_whenTwoPositiveNumbersGiven).
• Verification is performed immediately after each refactoring to establish a quality gate.

1. What is this skill for?#

Test Fixing Skill is developed by mhattingpete, a skill specially used to diagnose and fix automated test errors. It is particularly suitable for solving the pain point of large-scale failures in CI/CD pipelines caused by front-end changes or test data invalidation.

Its core capability is to systematically identify and fix failed tests. Through intelligent error grouping strategies, it finds test cases with the same root cause, provides a unified repair plan, avoids repeated troubleshooting, and greatly improves the efficiency of test repair. It is especially suitable for teams maintaining large test suites.

2. Core Usage#

No need to analyze failed tests one by one manually; the skill will automatically complete the entire process of “Analysis-Grouping-Repair”:

1. Batch Import Failed Test Logs: Support test failure reports exported in the CI/CD process, and automatically parse all failure information.
2. Intelligent Error Grouping: Identify the correlation between failed tests and group test cases with the same root cause (such as API interface changes, dependency package updates, code logic adjustments).
3. Generate Repair Plan: Analyze the root cause for each group of failed tests and provide specific repair suggestions, including code modifications and test case adjustments.
4. Verify Repair Effect: After the repair is completed, it can guide the verification of whether all test cases pass to ensure the root cause is completely solved and avoid secondary failures.

Usage Notes: After downloading, it can be directly integrated into the CI/CD process, or used independently for repairing local test failures, supporting the parsing of failure logs from a variety of test frameworks.

3. Practical Usage#

Scenario: Batch failure repair in CI/CD pipeline Traditional Method:

1
See 15 test failures -> Check logs one by one -> Find that the login button selector has changed for all
2
-> Manually modify 15 files -> Rerun CI -> May still have omissions
3
Estimated time: 2-3 hours

Using Test Fixing Skill:

1
AI analyzes error logs -> Automatic grouping: All 15 failures are due to #login-btn being changed to #sign-in-button
2
-> Provide a unified repair plan -> Apply to all affected files with one click
3
Time required: only 5 minutes

Workflow:

1
# 1. Collect failure information
2
failed_tests = [
3
    "test_login: Element not found #login-btn",
4
    "test_logout: Element not found #login-btn",
5
    "test_profile: Element not found #login-btn",
6
    # ... more similar errors
7
]
8

9
# 2. AI pattern analysis
10
analysis = test_fixing_skill.analyze(failed_tests)
11
# Output: Found a common pattern - 15 tests failed due to selector change
12

13
# 3. Provide repair plan
14
fix_plan = analysis.suggest_fix()
15
# Output: Replace #login-btn with #sign-in-button
16

17
# 4. Apply repair in batches
18
test_fixing_skill.apply_fix(fix_plan, dry_run=True)  # Preview first
19
test_fixing_skill.apply_fix(fix_plan, dry_run=False) # Apply after confirmation

Core Value:

• From one hour of troubleshooting + one minute of coding → one minute of troubleshooting + one minute of coding
• Avoid wasting time on repeated and similar error troubleshooting
• Especially suitable for teams maintaining large test suites

1. What is this skill for?#

Systematic Debugging Skill also comes from the Superpowers skill library of obra, positioned as a systematic debugging skill that solves the problem of blind attempts in AI debugging such as “see error → change randomly → check if it works → change again if not”.

Its core is to structure the thinking mode of professional debugging engineers and provide a four-phase root cause analysis process to guide users to systematically analyze problems, locate root causes, and verify repairs instead of directly giving answers. It can greatly shorten debugging time and improve the success rate of first repair, and is applicable to all types of bug debugging (especially occasional and deep-seated bugs).

2. Core Usage#

Following the core principle of “find the root cause first, then make repairs”, complete debugging in four phases (must be completed in order):

1
┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐    ┌─────────────────┐
2
│  Phase 1: Root  │ -> │  Phase 2: Pattern│ -> │  Phase 3: Hypo- │ -> │  Phase 4: Imple-│
3
│  Cause Investi- │    │  Analysis       │    │  thesis & Test  │    │  mentation      │
4
│  gation         │    │                 │    │                 │    │                 │
5
└─────────────────┘    └─────────────────┘    └─────────────────┘    └─────────────────┘

It also provides a variety of auxiliary skills:

• Phase 1: Root Cause Investigation: Collect error information and context, stably reproduce the problem, check recent code changes, collect evidence from multi-component systems, and avoid blind repairs.
• Phase 2: Pattern Analysis: Find similar working examples in the code base, compare reference implementations, and identify differences between problematic code and normal code.
• Phase 3: Hypothesis and Testing: Form a single hypothesis (clearly state “what the root cause is and why it occurs”), verify the hypothesis through minimal testing, and change only one variable at a time to avoid confusion.
• Phase 4: Implementation: Create failed test cases, implement a single repair plan, verify the repair effect, and ensure the root cause is completely solved.

Auxiliary Skills: Provide defense-in-depth strategies (add verification at each level of data transmission), root cause tracing technology (trace back to the original trigger through the call chain), and remind danger signals in debugging to avoid falling into debugging misunderstandings.

3. Detailed Explanation of the Four Phases#

Phase 1: Root Cause Investigation

1
# 1. Read error information carefully (not just the first line)
2
# Error: Vitest output includes file paths and line numbers
3
# Error: TypeScript errors show the full type mismatch
4

5
# 2. Stably reproduce the problem
6
pnpm test src/features/workout/__tests__/specific.test.ts
7
pnpm test --reporter=verbose
8

9
# 3. Check recent changes
10
git diff HEAD~5 --stat
11
git log --oneline -10
12

13
# 4. Add diagnostics for multi-component systems
14
# Add console.error to track data flow at each layer:
15
# Component -> Composable -> Repository -> Database

Phase 2: Pattern Analysis

1. Find examples of similar working code
2. Compare test patterns in the testing-conventions skill
3. Identify differences: Missing await? Missing resetDatabase()?

Phase 3: Hypothesis and Testing

1
# Form a single hypothesis: "I believe X is the root cause because Y"
2
# Change only one place at a time!
3
# Do not proceed to the next modification before verification
4

5
# Red warning signals (must stop):
6
# - "Fix it quickly now and investigate later"
7
# - "Just try changing X to see if it works"

If three repair attempts fail in a row, it indicates:

• The root cause hypothesis is wrong
• Need to re-examine the architectural design
• Return to Phase 1 for re-investigation

Phase 4: Implementation Based on the understanding of the root cause, implement targeted repairs instead of symptom-based repairs.

1. Selection by Scenario#

2. Combined Usage Process#

Scenario: Full process of new feature development

1. Requirement Analysis
2. TDD Skill: Write failed tests
3. Development and Implementation
4. Webapp Testing: Automated verification
5. If bugs are found → Systematic Debugging: Root cause analysis
6. If tests fail → Test Fixing: Batch repair
7. Continuous Integration

A more concise process:

1
flowchart LR
2
    A[Requirement Analysis] --> B[TDD Skill<br/>(Write failed tests)]
3
    B --> C[Development and Implementation]
4
    C --> D[Webapp Testing<br/>(Automated verification)]
5
    D --> E{If bugs found?}
6
    E -->|Yes| F[Systematic Debugging<br/>(Root cause analysis)]
7
    E -->|No| G{If tests fail?}
8
    F --> G
9
    G -->|Yes| H[Test Fixing<br/>(Batch repair)]
10
    G -->|No| I[Continuous Integration]
11
    H --> I

Sequence Diagram Version (showing the collaboration of each skill):

1
sequenceDiagram
2
    participant Developer
3
    participant TDD
4
    participant Implementation
5
    participant Testing
6
    participant Debugging
7
    participant Fixing
8
    participant CI
9

10
    Developer->>TDD: Input requirements
11
    TDD->>TDD: Write failed tests
12
    TDD->>Implementation: Drive minimal implementation
13
    Implementation->>Testing: Submit verification
14
    Testing->>Testing: Automated test execution
15
    alt Bugs found
16
        Testing->>Debugging: Four-phase debugging
17
        Debugging->>Fixing: Batch failures located
18
    else Tests fail
19
        Testing->>Fixing: Test failures
20
    end
21
    Fixing->>Testing: Regression verification
22
    Testing->>CI: Test passed, enter pipeline
23
    CI->>Developer: Deployment completion notification