Agent skill

Zero Script QA

by ww-w-ai in ww-w-ai/bkit-claude-code

Zero Script QA — test without scripts using structured JSON logging and Docker monitoring.

Apache-2.0Auto-check: notesDevOps & Cloud

Install Zero Script QA

skills CLI
$ npx skills add ww-w-ai/bkit-claude-code --skill zero-script-qa -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ww-w-ai/bkit-claude-code zero-script-qa --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ww-w-ai/bkit-claude-code.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/zero-script-qa .claude/skills/zero-script-qa && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
zero-script-qa
GitHub stars
601
Token cost
~4.1k tokens
SKILL.md length
582 words
Files
1
Skills in repo
44
Repo updated
First seen
Licence
Apache-2.0

At a glance

Zero Script QA — test without scripts using structured JSON logging and Docker monitoring.

  • Works in 10 steps: Log Everything → Structured JSON Logs → Real-time Monitoring → …
  • Tasks that involve Containers
  • SKILL.md covers Overview, Core Principles, Logging Architecture and Request ID Propagation, plus 5 more sections
  • Calls docker, claude and curl

What it does

Zero Script QA is an agent skill from ww-w-ai/bkit-claude-code. Zero Script QA — test without scripts using structured JSON logging and Docker monitoring. Triggers: zero-script-qa, log testing, docker logs, QA

Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in DevOps & Cloud, covering Containers. It works with Docker. The repository describes itself as: bkit Vibecoding Kit - PDCA methodology + Claude Code mastery for AI-native development. The licence is Apache-2.0.

When your agent uses it

  • Tasks that involve Containers

Example prompts

  • “/zero-script-qa”

Requirements

  • Python 3
  • Docker
  • Pre-approved tools (allowed-tools): Read, Glob, Grep, Bash

Workflow steps

10 steps, taken from the step headings in SKILL.md.

  1. Log Everything
  2. Structured JSON Logs
  3. Real-time Monitoring
  4. Start Environment
  5. Manual UX Testing
  6. Claude Code Log Analysis
  7. Issue Documentation
  8. Slow Response Detection
  9. Consecutive Failure Detection
  10. Abnormal Status Codes

What it can do on your machine

Read from SKILL.md and the folder at commit 85b4913. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves these tools, so the agent can use them without asking each time:

    • Read
    • Glob
    • Grep
    • Bash

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • docker
    • claude
    • curl

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md. Its commands use docker and curl, which can reach the network depending on how they are called.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Zero Script QA loads about 4.1k tokens when it runs. Until then it costs about 40 tokens; SKILL.md has 582 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~40
When it runs · the whole SKILL.md, loaded when a task matches
~4.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check: notes

The automated check noted patterns worth knowing about, such as sudo or a known installer.

  • NotePre-approves every shell command (allowed-tools: Bash)SKILL.md
    allowed-tools: Read, Glob, Grep, Bash

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ww-w-ai/bkit-claude-code at commit 85b4913, republished under its Apache-2.0 licence (© ww-w-ai). 582 words, ~4,136 tokens.

Download SKILL.mdSave it as .claude/skills/zero-script-qa/SKILL.md (or your agent's skills folder).
name
zero-script-qa
description
Zero Script QA — test without scripts using structured JSON logging and Docker monitoring. Triggers: zero-script-qa, log testing, docker logs, QA
allowed-tools
Read, Glob, Grep, Bash
classification
workflow
classification-reason
Process automation persists regardless of model advancement
deprecation-risk
none
effort
high
context
fork
background
false
agent
bkit:qa-monitor
user-invocable
true

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
Show full SKILL.md (211 more words)Show less
Cycle Workflow
┌─────────────────────────────────────────────────────────────┐
│                   Iterative Test Cycle                        │
├─────────────────────────────────────────────────────────────┤
│                                                             │
│  Cycle N:                                                   │
│  1. Run test script (E2E or manual)                         │
│  2. Claude monitors logs in real-time                       │
│  3. Record pass/fail results                                │
│  4. Claude identifies root cause of failures                │
│  5. Fix code immediately (hot reload)                       │
│  6. Document: Cycle N → Bug → Fix                           │
│                                                             │
│  Repeat until acceptable pass rate (>85%)                   │
│                                                             │
└─────────────────────────────────────────────────────────────┘
E2E Test Script Template
bash
#!/bin/bash
# E2E Test Script Template

API_URL="http://localhost:8000"
TOKEN="your-test-token"

PASS_COUNT=0
FAIL_COUNT=0
SKIP_COUNT=0

GREEN='\033[0;32m'
RED='\033[0;31m'
YELLOW='\033[0;33m'
NC='\033[0m'

test_feature_action() {
    echo -n "Testing: Feature action... "

    response=$(curl -s -X POST "$API_URL/api/v1/feature/action" \
        -H "Authorization: Bearer $TOKEN" \
        -H "Content-Type: application/json" \
        -d '{"param": "value"}')

    if [[ "$response" == *"expected_result"* ]]; then
        echo -e "${GREEN}✅ PASS${NC}"
        ((PASS_COUNT++))
    else
        echo -e "${RED}❌ FAIL${NC}"
        echo "Response: $response"
        ((FAIL_COUNT++))
    fi
}

# Run all tests
test_feature_action
# ... more tests

# Summary
echo ""
echo "═══════════════════════════════════════"
echo "Test Results:"
echo -e "  ${GREEN}✅ PASS: $PASS_COUNT${NC}"
echo -e "  ${RED}❌ FAIL: $FAIL_COUNT${NC}"
echo -e "  ${YELLOW}⏭️  SKIP: $SKIP_COUNT${NC}"
echo "═══════════════════════════════════════"
Test Cycle Documentation Template
markdown
# Feature Test Results - Cycle N

## Summary
- **Date**: YYYY-MM-DD
- **Feature**: {feature name}
- **Pass Rate**: N%
- **Tests**: X passed / Y total

## Results

| Test Case | Status | Notes |
|-----------|--------|-------|
| Test 1 | ✅ | |
| Test 2 | ❌ | {error description} |
| Test 3 | ⏭️ | {skip reason} |

## Bugs Found

### BUG-001: {Title}
- **Root Cause**: {description}
- **Fix**: {what was changed}
- **Files**: `path/to/file.py:123`

## Next Cycle Plan
- {what to test next}

Checklist

Logging Infrastructure
  • JSON log format applied
  • Request ID generation and propagation
  • Log level settings per environment
  • Docker logging configuration
Backend Logging
  • Logging middleware implemented
  • All API calls logged (including 200 OK)
  • Business logic logging
  • Detailed error logging
Frontend Logging
  • Logger module implemented
  • API client integration
  • Error boundary logging
QA Workflow
  • Docker Compose configured
  • Real-time monitoring ready
  • Issue documentation template ready

Auto-Apply Rules

When Building Logging Infrastructure

When implementing API/Backend:

  1. Suggest logging middleware creation
  2. Suggest JSON format logger setup
  3. Add Request ID generation/propagation logic

When implementing Frontend:

  1. Suggest Logger module creation
  2. Suggest logging integration with API client
  3. Suggest including Request ID header
When Performing QA

On test request:

  1. Guide to run docker compose logs -f
  2. Request manual UX testing from user
  3. Real-time log monitoring
  4. Document issues immediately when detected
  5. Provide fix suggestions
Issue Detection Thresholds
SeverityConditionAction
Criticallevel: ERROR or status: 5xxImmediate report
Criticalduration_ms > 3000Immediate report
Critical3+ consecutive failuresImmediate report
Warningstatus: 401, 403Warning report
Warningduration_ms > 1000Warning report
InfoMissing log fieldsNote for improvement
InfoRequest ID not propagatedNote for improvement
Required Logging Locations
Backend (FastAPI/Express)
✅ Request start (method, path, params)
✅ Request complete (status, duration_ms)
✅ Major business logic steps
✅ Detailed info on errors
✅ Before/after external API calls
✅ DB queries (in development)
Frontend (Next.js/React)
✅ API call start
✅ API response received (status, duration)
✅ Detailed info on errors
✅ Important user actions

© ww-w-ai, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/zero-script-qa of ww-w-ai/bkit-claude-code.

Open the folder on GitHubat commit 85b4913

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Works with

Categories

Questions about Zero Script QA

What does Zero Script QA do?

Zero Script QA — test without scripts using structured JSON logging and Docker monitoring. Zero Script QA is an agent skill from ww-w-ai/bkit-claude-code. Zero Script QA — test without scripts using structured JSON logging and Docker monitoring.

When should I use Zero Script QA?

Zero Script QA fits situations like: tasks that involve Containers.

How do I install Zero Script QA in Claude Code?

Run `npx skills add ww-w-ai/bkit-claude-code --skill zero-script-qa -a claude-code`. Or copy the skill folder (skills/zero-script-qa in ww-w-ai/bkit-claude-code) into .claude/skills/zero-script-qa in your project. Claude Code loads it when a task matches its description.

How do I install Zero Script QA in Codex?

Run `npx skills add ww-w-ai/bkit-claude-code --skill zero-script-qa -a codex`. Or copy the skill folder (skills/zero-script-qa in ww-w-ai/bkit-claude-code) into .agents/skills/zero-script-qa in your project. Codex loads it when a task matches its description.

Can I use Zero Script QA in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ww-w-ai/bkit-claude-code --skill zero-script-qa -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/zero-script-qa, .gemini/skills/zero-script-qa, .github/skills/zero-script-qa and .opencode/skills/zero-script-qa in your project.

What does Zero Script QA need to run?

Going by SKILL.md and its folder, Zero Script QA needs the command-line tools its instructions call (docker, claude and curl). Our summary lists: Python 3; Docker. Its frontmatter pre-approves these tools: Read, Glob, Grep, Bash.

Does Zero Script QA access the network?

SKILL.md contains no URLs. Its commands use docker and curl, which can reach the network depending on how they are called. This is read from the text; nothing was executed.

Is Zero Script QA safe to install?

Our automated static check of SKILL.md found notes only (pre-approves every shell command (allowed-tools: bash)), nothing it rates as a warning. It is not a guarantee. Review the folder before installing.

What licence does Zero Script QA use?

Zero Script QA is published under the Apache-2.0 licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Zero Script QA use?

About 4.1k tokens (SKILL.md is roughly 17k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Zero Script QA?

Skills that share tags, products or a category with Zero Script QA: Iron Proxy Gateway for NanoClaw (nanocoai/nanoclaw, 31k stars), GreptimeDB Dev Docker Image (GreptimeTeam/greptimedb, 6.7k stars), Senior DevOps Toolkit (maslennikov-ig/claude-code-orchestrator-kit, 260 stars) and LangBot Deployment Guide (langbot-app/LangBot, 18k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Zero Script QA?

ww-w-ai (a GitHub organization) maintains it in ww-w-ai/bkit-claude-code, which has 601 GitHub stars. The repository holds 44 skills in this directory. The repository was last updated on September 27, 2026.

Source: ww-w-ai/bkit-claude-code on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.