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zero-script-qa

Detect application issues from structured JSON logs and Docker log streams.

Updated Mar 20, 2026
Search Tags:#logging#qa#docker#ai-analysis#json-logs#real-time-monitoring#request-id
One-click install
npx skills add https://github.com/hendrax5/ironman --skill zero-script-qa-hendrax5
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: zero-script-qa
Source: https://github.com/hendrax5/ironman/tree/main/skills/zero-script-qa
Command: npx skills add https://github.com/hendrax5/ironman --skill zero-script-qa-hendrax5

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Zero Script QA eliminates the need to write and maintain E2E test scripts by using structured JSON logs and real-time monitoring to detect functional regressions, performance anomalies, and error patterns during development and staging.

Core Features & Use Cases

  • Structured JSON logging: Enforces consistent log shape (timestamp, level, service, request_id, message, data) for reliable parsing and analysis.
  • Request ID propagation: Tracks a single request across gateway, backend, and frontend to trace failures and performance bottlenecks.
  • Real-time Docker log streaming: Uses docker compose log streams to enable live monitoring and immediate detection of errors and slow responses.
  • AI-assisted analysis and documentation: Integrates an AI monitor to analyze log streams, detect patterns (errors, slow responses, consecutive failures), and auto-generate QA issue reports.
  • Use case: Run manual UX tests while the system auto-detects backend/frontend errors, documents request IDs, and creates actionable bug reports.

Quick Start

Start your Docker compose environment, stream service logs, and let the AI monitor analyze logs in real time to detect and document issues.

Frequently Asked Questions about zero-script-qa

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I detect application errors without writing E2E test scripts?▼

You can detect application errors without test scripts by streaming structured JSON logs from your Docker compose environment. An AI monitor analyzes the log streams in real time to identify functional regressions and auto-generate issue reports.

What is the best way to trace request failures across backend and frontend services in Docker?▼

Tracing request failures across services requires propagating a request ID through your system's structured JSON logs. This connects gateway, backend, and frontend logs to pinpoint exact failure locations and performance bottlenecks.

Does zero-script QA work with non-JSON log formats?▼

No, this approach requires JSON-formatted logs to function correctly. Structured JSON logging enforces a consistent shape including timestamp, level, service, and request ID, which is necessary for reliable AI-assisted parsing and pattern detection.

How do I monitor Docker logs in real time for functional regressions?▼

Monitor Docker logs for regressions by streaming your docker compose log output to an AI-based log monitor. The monitor analyzes live streams to detect error patterns, slow responses, and consecutive failures during manual UX testing.

Can I use this for automated issue detection in staging environments?▼

Yes, this method supports automated issue detection during development and staging. By running manual UX tests against your Docker services, the system auto-detects backend and frontend errors while documenting request IDs for actionable bug reports.

Why do I need request ID propagation for AI log analysis?▼

Request ID propagation is needed to track a single request across gateway, backend, and frontend services. This tracing capability allows the AI analysis engine to correlate related log entries and accurately identify systemic failures and bottlenecks.

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