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| name | characterization-test-generator |
| description | Generate characterization tests to capture and verify existing behavior before migration |
| allowed-tools | ["Bash","Read","Write","Grep","Glob","Edit"] |
| graph | {"domains":["domain:software-engineering"],"specializations":["specialization:code-migration-modernization"],"skillAreas":["skill-area:regression-testing","skill-area:unit-testing"],"roles":["role:qa-engineer","role:backend-engineer"],"workflows":["workflow:technical-debt-reduction"],"topics":["topic:refactoring"]} |
Generates characterization tests (also known as golden master tests or approval tests) to capture existing system behavior before migration, ensuring functional equivalence after changes.
Enable behavior preservation during migration through:
This skill can leverage the following external tools when available:
| Tool | Purpose | Integration Method |
|---|---|---|
| ApprovalTests | Approval testing framework | Library |
| Jest snapshots | JavaScript snapshot testing | Framework |
| pytest-snapshot | Python snapshot testing | Plugin |
| TextTest | Golden master testing | CLI |
| Verify | .NET approval testing | Library |
| Scientist | Safe refactoring library | Library |
| AI Testing MCP | AI-powered test generation | MCP Server |
# Invoke skill for characterization test generation
# The skill will analyze code and generate tests
# Expected inputs:
# - targetPath: Path to code to characterize
# - testFramework: 'jest' | 'pytest' | 'junit' | 'nunit' | 'auto'
# - outputDir: Directory for generated tests
# - captureMode: 'snapshot' | 'approval' | 'recording'
Analysis Phase
Input Discovery Phase
Capture Phase
Test Generation Phase
{
"generationId": "string",
"timestamp": "ISO8601",
"target": {
"path": "string",
"language": "string",
"framework": "string",
"unitsAnalyzed": "number"
},
"testsGenerated": {
"total": "number",
"byType": {
"snapshot": "number",
"approval": "number",
"recording": "number"
},
"coverage": {
"functions": "number"
This skill integrates with the following Code Migration/Modernization processes:
Create .characterization-tests.json in the project root:
{
"testFramework": "auto",
"outputDir": "./tests/characterization",
"captureMode": "snapshot",
"goldenMasterDir": "./tests/golden-masters",
"inputGeneration": {
"boundary": true,
"combinatorial": true,
"maxCombinations": 100,
"includeNulls": true,
"includeEmpty": true
},
"outputCapture": {
"format": "json",
"normalizeWhitespace": true,
"ignorePaths"
When AI Testing MCP Server is available:
// Example AI-powered test generation
{
"tool": "ai_testing_generate",
"arguments": {
"target": "./src/services/user.ts",
"framework": "jest",
"style": "characterization"
}
}
// Generated characterization test
describe('UserService', () => {
describe('calculateDiscount', () => {
it('should match snapshot for standard customer', () => {
const result = userService.calculateDiscount({
customerId: 'C001',
purchaseAmount: 100,
loyaltyPoints: 500
});
expect(result).toMatchSnapshot();
});
it('should match snapshot for premium customer', () => {
const result = userService.calculateDiscount({
customerId: 'C002',
purchaseAmount: 100,
loyaltyPoints: 5000,
isPremium: true
});
expect(result).toMatchSnapshot();
});
// Edge cases
it('should match snapshot for zero amount', () => {
const result = userService.calculateDiscount({
customerId: 'C001',
purchaseAmount: 0,
loyaltyPoints: 0
});
(result).();
});
});
});
// Generated approval test
public class UserServiceCharacterizationTest {
@Test
public void calculateDiscount_standardCustomer() {
UserService service = new UserService();
DiscountResult result = service.calculateDiscount(
new DiscountRequest("C001", 100.0, 500)
);
Approvals.verify(result);
}
@Test
public void calculateDiscount_boundaryValues() {
UserService service = new UserService();
// Boundary: minimum values
Approvals.verify(service.calculateDiscount(
new DiscountRequest("C001", 0.01, 0)
), "minimum");
// Boundary: maximum values
Approvals.verify(service.calculateDiscount(
new DiscountRequest("C001", 999999.99, 999999)
), "maximum");
}
}
# Generated recording-based test
import pytest
from tests.recordings import PlaybackRecorder
class TestUserServiceCharacterization:
@pytest.fixture
def recorder(self):
return PlaybackRecorder('tests/recordings/user_service')
def test_get_user_profile_recorded(self, recorder):
"""Replay recorded external API interactions"""
with recorder.playback('get_user_profile_c001'):
service = UserService()
result = service.get_user_profile('C001')
assert result == recorder.expected_output()
def test_update_user_settings_recorded(self, recorder):
"""Verify database interactions match recording"""
with recorder.playback('update_settings_c001'):
service = UserService()
result = service.update_settings('C001', {'theme': 'dark'})
recorder.verify_database_calls()
assert result == recorder.expected_output()
test-coverage-analyzer: Analyze coverage gapsmigration-validator: Validate migration resultsstatic-code-analyzer: Identify testable code pathsmigration-testing-strategist: Uses this skill for test strategyregression-detector: Uses this skill for regression detectionparallel-run-validator: Uses this skill for comparison testingReference for querying the Atlas knowledge graph through its MCP tools — the SECONDARY enrichment/comparison layer that adds best-practice context to systems you have ALREADY scanned from your real sources (`az`, repos, dirs). Use when you need to look up nodes, edges, kinds, clusters, stats, or wiki pages in Atlas to compare against your real inventory. (atlas graph, query atlas, atlas mcp, search the graph, graph neighbors, atlas record, atlas kinds, enrichment layer)
Atlas turns your STATED NEED into a real systems atlas by SCANNING your actual sources (Azure via `az`, git repos, local dirs) and process/data mining them, THEN enriching against the Atlas knowledge graph. Use this skill when asked to inventory/map your real systems, scan your cloud + repos + directories, mine the real processes or data they contain, or collect their real constraints/gotchas. (atlas, scan my systems, inventory our azure account, map my repos, real systems atlas, process mining, data mining, collect nuances, system discovery)
This skill should be used when the user asks to "find skills in the wild", "assimilate popular workflows", "discover SKILL.md files in repos", "research external skills", "find workflow patterns", "survey the skill landscape", "what skills exist out there", or wants to investigate public repositories for extractable processes, babysitter plugins, and reusable procedural insights. Searches GitHub for SKILL.md files, classifies repos by archetype, and maintains structured research under docs/reference-repos/.