code-testing-agent

name	code-testing-agent
description	Generates and writes new unit tests for any programming language — scaffolds .NET test projects, pytest suites, Vitest/Jest suites, Go test files, and JUnit suites, and configures coverage tooling (coverlet, pytest-cov, @vitest/coverage-v8) as part of test generation. Use when asked to generate tests, generate pytest tests, generate Vitest tests, write unit tests, add tests, improve coverage, comprehensive tests, or scaffold a new test project or suite for an app, service, library, REST API, blueprint, or package — including project-wide, multi-file test generation across services, repositories, routes, and modules. Supports C#/.NET, Python (pytest, Flask/Django), TypeScript/JavaScript (Vitest, Jest, Mocha), Go, Rust, Java (JUnit). Runs a research, planning, and implementation pipeline so tests compile and pass. DO NOT USE FOR: running existing tests (use run-tests); analyzing existing coverage reports (use coverage-analysis or crap-score); MSTest modernization (use writing-mstest-tests).
license	MIT

Code Testing Generation Skill

An AI-powered skill that generates comprehensive, workable unit tests for any programming language using a coordinated multi-agent pipeline.

When to Use This Skill

Use this skill when you need to:

• Generate unit tests for an entire project or specific files
• Improve test coverage for existing codebases
• Create test files that follow project conventions
• Write tests that actually compile and pass
• Add tests for new features or untested code

When Not to Use

• Running or executing existing tests (use the run-tests skill)
• Migrating between test frameworks (use migration skills)
• Writing tests specifically for MSTest patterns (use writing-mstest-tests)
• Debugging failing test logic

How It Works

This skill coordinates multiple specialized agents in a Research → Plan → Implement pipeline:

Pipeline Overview

┌─────────────────────────────────────────────────────────────┐
│                     TEST GENERATOR                          │
│  Coordinates the full pipeline and manages state            │
└─────────────────────┬───────────────────────────────────────┘
                      │
        ┌─────────────┼─────────────┐
        ▼             ▼             ▼
┌───────────┐  ┌───────────┐  ┌───────────────┐
│ RESEARCHER│  │  PLANNER  │  │  IMPLEMENTER  │
│           │  │           │  │               │
│ Analyzes  │  │ Creates   │  │ Writes tests  │
│ codebase  │→ │ phased    │→ │ per phase     │
│           │  │ plan      │  │               │
└───────────┘  └───────────┘  └───────┬───────┘
                                      │
                    ┌─────────┬───────┼───────────┐
                    ▼         ▼       ▼           ▼
              ┌─────────┐ ┌───────┐ ┌───────┐ ┌───────┐
              │ BUILDER │ │TESTER │ │ FIXER │ │LINTER │
              │         │ │       │ │       │ │       │
              │ Compiles│ │ Runs  │ │ Fixes │ │Formats│
              │ code    │ │ tests │ │ errors│ │ code  │
              └─────────┘ └───────┘ └───────┘ └───────┘

Step-by-Step Instructions

Step 1: Determine the user request

Make sure you understand what user is asking and for what scope. When the user does not express strong requirements for test style, coverage goals, or conventions, source the guidelines from unit-test-generation.prompt.md. This prompt provides best practices for discovering conventions, parameterization strategies, coverage goals (aim for 80%), and language-specific patterns.

Step 2: Invoke the Test Generator

Start by calling the code-testing-generator agent with your test generation request:

Generate unit tests for [path or description of what to test], following the [unit-test-generation.prompt.md](unit-test-generation.prompt.md) guidelines

The Test Generator will manage the entire pipeline automatically.

Step 3: Research Phase (Automatic)

The code-testing-researcher agent analyzes your codebase to understand:

• Language & Framework: Detects C#, TypeScript, Python, Go, Rust, Java, etc.
• Testing Framework: Identifies MSTest, xUnit, Jest, pytest, go test, etc.
• Project Structure: Maps source files, existing tests, and dependencies
• Build Commands: Discovers how to build and test the project

For C# / .NET repos with a multi-file scope, the researcher should prefer the find-untested-sources skill (when available) over manual find/grep/glob walks to build the source-to-test pairing map. It is a parse-only Roslyn analyzer (no build, no coverage — seconds on multi-thousand-file repos) that emits a deterministic JSON list of untested files ordered by API surface, plus a suggested_test_path derived from <ProjectReference> edges.

Output: .testagent/research.md

Step 4: Planning Phase (Automatic)

The code-testing-planner agent creates a structured implementation plan:

• Groups files into logical phases (2-5 phases typical)
• Prioritizes by complexity and dependencies
• Specifies test cases for each file
• Defines success criteria per phase

Output: .testagent/plan.md

Step 5: Implementation Phase (Automatic)

The code-testing-implementer agent executes each phase sequentially:

1. Read source files to understand the API
2. Write test files following project patterns
3. Build using the code-testing-builder sub-agent to verify compilation
4. Test using the code-testing-tester sub-agent to verify tests pass
5. Fix using the code-testing-fixer sub-agent if errors occur
6. Lint using the code-testing-linter sub-agent for code formatting

Each phase completes before the next begins, ensuring incremental progress.

Coverage Types

• Happy path: Valid inputs produce expected outputs
• Edge cases: Empty values, boundaries, special characters
• Error cases: Invalid inputs, null handling, exceptions

State Management

All pipeline state is stored in .testagent/ folder:

File	Purpose
.testagent/research.md	Codebase analysis results
.testagent/plan.md	Phased implementation plan
.testagent/status.md	Progress tracking (optional)

Examples

Strategy Selection

The generator picks a strategy based on request scope:

User Request	Strategy	Why
"Generate tests for src/services/UserService.ts"	Direct	Single file, small scope — write tests immediately, skip sub-agents
"Add unit tests for my billing project"	Single pass	Moderate scope — one Research → Plan → Implement cycle covers it
"Achieve 80% coverage across the entire solution"	Iterative	Large scope — multiple R→P→I cycles, each narrowing remaining gaps

Pipeline Walkthrough

Given a request like "Generate unit tests for my InvoiceService", the pipeline produces:

1. Research → .testagent/research.md containing detected language/framework, build commands, files to test ranked by priority, and existing test inventory
2. Plan → .testagent/plan.md containing phased approach with specific methods and test scenarios (happy path, edge cases, error cases) for each file
3. Implement → Test files written, built, and verified per phase. Fix cycle runs automatically if build/test errors occur
4. Validate → Full workspace build + full test run to catch cross-project issues
5. Report → Summary of tests created, pass/fail counts, coverage notes, and next steps

Language-Specific Examples

The code-testing-extensions skill provides concrete, filled-in examples for each pipeline phase showing real source code, real research output, real plans, and real generated tests. Call the code-testing-extensions skill to discover available extension files, then read:

• dotnet-examples.md — MSTest example with InvoiceService: research output, plan output, generated test file, fix cycle walkthrough, and final report
• python-examples.md — pytest example with the same InvoiceService scenario: research, plan, generated test file (parametrized, unittest.mock), fix cycles (ModuleNotFoundError, patch target, Mock(spec=...)), and final report
• typescript-examples.md — Vitest example (also applicable to Jest) showing it.each parameterization, async tests, fake timers, and ESM/CJS fix cycles
• go-examples.md — Standard testing package example with table-driven subtests, hand-written fake repository, injected clock, and -run regex fix cycle
• java-examples.md — JUnit 5 + Mockito example on Maven showing @ExtendWith(MockitoExtension.class), @ParameterizedTest + @CsvSource, Clock.fixed(...) for time, and Surefire fix cycles

For languages without a dedicated examples file (Rust, Ruby, Swift, Kotlin, C++, PowerShell), use the base extension file (<language>.md) plus the example file for the closest paradigm — the pipeline shape (research → plan → generate → fix) and the categories of decisions (test layout, mocking strategy, fixed clock for time-dependent code, parameterization style) translate directly.

Agent Reference

Agent	Purpose
code-testing-generator	Coordinates pipeline
code-testing-researcher	Analyzes codebase
code-testing-planner	Creates test plan
code-testing-implementer	Writes test files
code-testing-builder	Compiles code
code-testing-tester	Runs tests
code-testing-fixer	Fixes errors
code-testing-linter	Formats code

Requirements

• Project must have a build/test system configured
• Testing framework should be installed (or installable)
• VS Code with GitHub Copilot extension

Troubleshooting

Tests don't compile

The code-testing-fixer agent will attempt to resolve compilation errors. Check .testagent/plan.md for the expected test structure. Call the code-testing-extensions skill and read the language-specific extension file for error code references (e.g., dotnet.md for .NET).

Tests fail

Most failures in generated tests are caused by wrong expected values in assertions, not production code bugs:

1. Read the actual test output
2. Read the production code to understand correct behavior
3. Fix the assertion, not the production code
4. Never mark tests [Ignore] or [Skip] just to make them pass

Wrong testing framework detected

Specify your preferred framework in the initial request: "Generate Jest tests for..."

Environment-dependent tests fail

Tests that depend on external services, network endpoints, specific ports, or precise timing will fail in CI environments. Focus on unit tests with mocked dependencies instead.

Build fails on full solution

During phase implementation, build only the specific test project for speed. After all phases, run a full non-incremental workspace build to catch cross-project errors.