popup-studio-ai/bkit-claude-code/zero-script-qa

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Zero Script QA — test without scripts using structured JSON logging and Docker monitoring. Triggers: zero-script-qa, log testing, docker logs, QA

Install

npx vskill@latest install popup-studio-ai/bkit-claude-code/zero-script-qa

SKILL.md

Zero Script QA Expert Knowledge

Overview

Zero Script QA is a methodology that verifies features through structured logs and real-time monitoring without writing test scripts.

Traditional: Write test code → Execute → Check results → Maintain
Zero Script: Build log infrastructure → Manual UX test → AI log analysis → Auto issue detection

Core Principles

1. Log Everything
  • All API calls (including 200 OK)
  • All errors
  • All important business events
  • Entire flow trackable via Request ID
2. Structured JSON Logs
  • Parseable JSON format
  • Consistent fields (timestamp, level, request_id, message, data)
  • Different log levels per environment
3. Real-time Monitoring
  • Docker log streaming
  • Claude Code analyzes in real-time
  • Immediate issue detection and documentation

Logging Architecture

JSON Log Format Standard
json
{
"timestamp": "2026-01-08T10:30:00.000Z",
"level": "INFO",
"service": "api",
"request_id": "req_abc123",
"message": "API Request completed",
"data": {
"method": "POST",
"path": "/api/users",
"status": 200,
"duration_ms": 45
}
}
Required Log Fields
FieldTypeDescription
timestampISO 8601Time of occurrence
levelstringDEBUG, INFO, WARNING, ERROR
servicestringService name (api, web, worker, etc.)
request_idstringRequest tracking ID
messagestringLog message
dataobjectAdditional data (optional)
Log Level Policy
EnvironmentMinimum LevelPurpose
LocalDEBUGDevelopment and QA
StagingDEBUGQA and integration testing
ProductionINFOOperations monitoring

Request ID Propagation

Concept
Client → API Gateway → Backend → Database
↓ ↓ ↓ ↓
req_abc req_abc req_abc req_abc
Trackable with same Request ID across all layers
Implementation Patterns
1. Request ID Generation (Entry Point)
typescript
// middleware.ts
import { v4 as uuidv4 } from 'uuid';
export function generateRequestId(): string {
return `req_${uuidv4().slice(0, 8)}`;
}
// Propagate via header
headers['X-Request-ID'] = requestId;
2. Request ID Extraction and Propagation
typescript
// API client
const requestId = headers['X-Request-ID'] || generateRequestId();
// Include in all logs
logger.info('Processing request', { request_id: requestId });
// Include in header when calling downstream services
await fetch(url, {
headers: { 'X-Request-ID': requestId }
});

Backend Logging (FastAPI)

Logging Middleware
python
# middleware/logging.py
import logging
import time
import uuid
import json
from fastapi import Request
class JsonFormatter(logging.Formatter):
def format(self, record):
log_record = {
"timestamp": self.formatTime(record),
"level": record.levelname,
"service": "api",
"request_id": getattr(record, 'request_id', 'N/A'),
"message": record.getMessage(),
}
if hasattr(record, 'data'):
log_record["data"] = record.data
return json.dumps(log_record)
class LoggingMiddleware:
async def __call__(self, request: Request, call_next):
request_id = request.headers.get('X-Request-ID', f'req_{uuid.uuid4().hex[:8]}')
request.state.request_id = request_id
start_time = time.time()
# Request logging
logger.info(
f"Request started",
extra={
'request_id': request_id,
'data': {
'method': request.method,
'path': request.url.path,
'query': str(request.query_params)
}
}
)
response = await call_next(request)
duration = (time.time() - start_time) * 1000
# Response logging (including 200 OK!)
logger.info(
f"Request completed",
extra={
'request_id': request_id,
'data': {
'status': response.status_code,
'duration_ms': round(duration, 2)
}
}
)
response.headers['X-Request-ID'] = request_id
return response
Business Logic Logging
python
# services/user_service.py
def create_user(data: dict, request_id: str):
logger.info("Creating user", extra={
'request_id': request_id,
'data': {'email': data['email']}
})
# Business logic
user = User(**data)
db.add(user)
db.commit()
logger.info("User created", extra={
'request_id': request_id,
'data': {'user_id': user.id}
})
return user

Frontend Logging (Next.js)

Logger Module
typescript
// lib/logger.ts
type LogLevel = 'DEBUG' | 'INFO' | 'WARNING' | 'ERROR';
interface LogData {
request_id?: string;
[key: string]: any;
}
const LOG_LEVELS: Record<LogLevel, number> = {
DEBUG: 0,
INFO: 1,
WARNING: 2,
ERROR: 3,
};
const MIN_LEVEL = process.env.NODE_ENV === 'production' ? 'INFO' : 'DEBUG';
function log(level: LogLevel, message: string, data?: LogData) {
if (LOG_LEVELS[level] < LOG_LEVELS[MIN_LEVEL]) return;
const logEntry = {
timestamp: new Date().toISOString(),
level,
service: 'web',
request_id: data?.request_id || 'N/A',
message,
data: data ? { ...data, request_id: undefined } : undefined,
};
console.log(JSON.stringify(logEntry));
}
export const logger = {
debug: (msg: string, data?: LogData) => log('DEBUG', msg, data),
info: (msg: string, data?: LogData) => log('INFO', msg, data),
warning: (msg: string, data?: LogData) => log('WARNING', msg, data),
error: (msg: string, data?: LogData) => log('ERROR', msg, data),
};
API Client Integration
typescript
// lib/api-client.ts
import { logger } from './logger';
import { v4 as uuidv4 } from 'uuid';
export async function apiClient<T>(
endpoint: string,
options: RequestInit = {}
): Promise<T> {
const requestId = `req_${uuidv4().slice(0, 8)}`;
const startTime = Date.now();
logger.info('API Request started', {
request_id: requestId,
method: options.method || 'GET',
endpoint,
});
try {
const response = await fetch(`/api${endpoint}`, {
...options,
headers: {
'Content-Type': 'application/json',
'X-Request-ID': requestId,
...options.headers,
},
});
const duration = Date.now() - startTime;
const data = await response.json();
// Log 200 OK too!
logger.info('API Request completed', {
request_id: requestId,
status: response.status,
duration_ms: duration,
});
if (!response.ok) {
logger.error('API Request failed', {
request_id: requestId,
status: response.status,
error: data.error,
});
throw new ApiError(data.error);
}
return data;
} catch (error) {
logger.error('API Request error', {
request_id: requestId,
error: error instanceof Error ? error.message : 'Unknown error',
});
throw error;
}
}

Nginx JSON Logging

nginx.conf Configuration
nginx
http {
log_format json_combined escape=json '{'
'"timestamp":"$time_iso8601",'
'"level":"INFO",'
'"service":"nginx",'
'"request_id":"$http_x_request_id",'
'"message":"HTTP Request",'
'"data":{'
'"remote_addr":"$remote_addr",'
'"method":"$request_method",'
'"uri":"$request_uri",'
'"status":$status,'
'"body_bytes_sent":$body_bytes_sent,'
'"request_time":$request_time,'
'"upstream_response_time":"$upstream_response_time",'
'"http_referer":"$http_referer",'
'"http_user_agent":"$http_user_agent"'
'}'
'}';
access_log /var/log/nginx/access.log json_combined;
}

Docker-Based QA Workflow

docker-compose.yml Configuration
yaml
version: '3.8'
services:
api:
build: ./backend
environment:
- LOG_LEVEL=DEBUG
- LOG_FORMAT=json
logging:
driver: json-file
options:
max-size: "10m"
max-file: "3"
web:
build: ./frontend
environment:
- NODE_ENV=development
depends_on:
- api
nginx:
image: nginx:alpine
volumes:
- ./nginx/nginx.conf:/etc/nginx/nginx.conf
ports:
- "80:80"
depends_on:
- api
- web
Real-time Log Monitoring
bash
# Stream all service logs
docker compose logs -f
# Specific service only
docker compose logs -f api
# Filter errors only
docker compose logs -f | grep '"level":"ERROR"'
# Track specific Request ID
docker compose logs -f | grep 'req_abc123'

QA Automation Workflow

1. Start Environment
bash
# Start development environment
docker compose up -d
# Start log monitoring (Claude Code monitors)
docker compose logs -f
2. Manual UX Testing
User tests actual features in browser:
1. Sign up attempt
2. Login attempt
3. Use core features
4. Test edge cases
3. Claude Code Log Analysis
Claude Code in real-time:
1. Monitor log stream
2. Detect error patterns
3. Detect abnormal response times
4. Track entire flow via Request ID
5. Auto-document issues
4. Issue Documentation
markdown
# QA Issue Report
## Issues Found
### ISSUE-001: Insufficient error handling on login failure
-**Request ID**: req_abc123
-**Severity**: Medium
-**Reproduction path**: Login → Wrong password
-**Log**:
```json
{"level":"ERROR","message":"Login failed","data":{"error":"Invalid credentials"}}
```
-**Problem**: Error message not user-friendly
-**Recommended fix**: Add error code to message mapping

Issue Detection Patterns

1. Error Detection
json
{"level":"ERROR","message":"..."}

→ Report immediately

2. Slow Response Detection
json
{"data":{"duration_ms":3000}}

→ Warning when exceeding 1000ms

3. Consecutive Failure Detection
3+ consecutive failures on same endpoint

→ Report potential system issue

4. Abnormal Status Codes
json
{"data":{"status":500}}

→ Report 5xx errors immediately


Phase Integration

PhaseZero Script QA Integration
Phase 4 (API)API response logging verification
Phase 6 (UI)Frontend logging verification
Phase 7 (Security)Security event logging verification
Phase 8 (Review)Log quality review
Phase 9 (Deployment)Production log level configuration

Iterative Test Cycle Pattern

Based on bkamp.ai notification feature development:

Example: 8-Cycle Test Process
CyclePass RateBug FoundFix Applied
1st30%DB schema mismatchSchema migration
2nd45%NULL handling missingAdd null checks
3rd55%Routing errorFix deeplinks
4th65%Type mismatchFix enum types
5th70%Calculation errorFix count logic
6th75%Event missingAdd event triggers
7th82%Cache sync issueFix cache invalidation
8th89%StableFinal polish
Cycle Workflow

This is the start of the file. Read the full SKILL.md on GitHub.

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Source

Popularity

Repository stars
583
Repository forks
148
7-day trend score
32

Stars and forks are for the whole GitHub repository, not this skill alone. Refreshed 17 Mar 2026, 7 months ago.

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