Monitoring (Grade A)Claude AI의 개발 기술을 위한 안정성 테스트 - A 등급. 서비스 모니터링 및 알림을 위한 워크플로우. 서비스 모니터링을 설정하거나 개선해야 하는 경우 사용하세요.

Why You Should Use This?

| Audience | Key Benefits | | -------------------- | ------------------------------------------------------------------------------------------------- | | Individual | 30%+ token savings, stop repeating standards, security guardrails, instant scaffolding | | Teams | Consistent AI behavior, day‑one onboarding, codified patterns, compounding cost savings | | Organizations | Always‑on compliance, same standards across 1,000 projects, central governance, measurable ROI | ---

Asked by Sami Virtanen · Oct 3, 2025

What You Can Customize?

| Section | What It Controls | Example | | ------------- | -------------------------- | ---------------------------------------------- | | project | Project identity | Name, description, repo URL | | techStack | Language, framework, tools | TypeScript + Express or Python + FastAPI | | paths | Directory structure | Where handlers, services, and common code live | | domain | Business entities | Order, Product, Customer + lifecycle states | | patterns | Code patterns | 7‑step handler flow, error handling strategy | | testing | Quality gates | 90% coverage, test/lint/type‑check commands | | database | DB conventions | Soft delete field, timestamp columns, naming | | packages | Internal packages | @your‑org scope, registry URL | | conventions | Git and workflow | Branch prefixes, commit format, PR templates | ---

Asked by Nils Johansson · Sep 5, 2025

What it detects?

| Category | Signals | | ------------------ | ------------------------------------------------------------------------------------------ | | Language | tsconfig.json, go.mod, Cargo.toml, requirements.txt, pom.xml, file extensions | | Framework | Dependencies in package.json / requirements.txt (React, Express, Django, Spring, etc.) | | Database | ORM configs (prisma/, sequelize, typeorm), .sql files, migration folders | | Testing | jest.config., vitest, pytest, cypress/, playwright.config. | | Infrastructure | Dockerfile, terraform/, cdk.json, serverless.yml, cloud SDK deps | | CI/CD | .github/workflows/, .gitlab-ci.yml, Jenkinsfile | ---

Asked by Gunnar Eriksson · Sep 3, 2025

What's Inside?

| Layer | Count | What It Does | How It's Triggered | | ------------- | ----- | ------------------------------------------------- | ----------------------------------- | | Rules | 47 | Enforces coding standards on every AI interaction | Automatically — always on | | Agents | 62 | Specialized assistants for complex tasks | On demand — /agent-name | | Skills | 50 | Step‑by‑step guided workflows with checklists | Contextually — when patterns match | | Commands | 37 | Lightweight, token‑efficient quick actions | On demand — /command | | Hooks | 12 | Automation scripts in the AI loop | Event‑driven — before/after actions | | Templates | 9 | Scaffolding for handlers, components, tests, etc. | Referenced by skills and agents | ---

Asked by Idris Suleiman · Aug 24, 2025

How It Works?

Layer 1 — Pre-Processing: Hooks inject project context and block dangerous commands before your prompt reaches the AI. Layer 2 — Rules Engine: 47 always‑on rules enforce token efficiency, security, architecture, code standards, database conventions, and testing thresholds. Layer 3 — Specialized Processing: The right component activates — an agent, skill, or command — based on your prompt. Layer 4 — Post-Processing: Hooks validate output, auto‑format, scan for secrets, and verify coverage.

Asked by Grace Okafor · Jul 8, 2025