Strangler Fig Mainframe Cobol

---
id: SKL-strangler-STRANGLERFIGMAINFRAMECOBOL
name: Strangler Fig Mainframe Cobol
description: This skill covers the modernization of legacy systems using the Strangler
  Fig Pattern, interfacing with mainframe systems (COBOL, AS/400), and gradual migration
  strategies. It enables replacing legacy
version: 1.0.0
status: active
owner: '@cerebra-team'
last_updated: '2026-02-22'
category: Backend
tags:
- api
- backend
- server
- database
stack:
- Python
- Node.js
- REST API
- GraphQL
difficulty: Intermediate
---

# Strangler Fig Mainframe Cobol

## Skill Profile
*(Select at least one profile to enable specific modules)*
- [ ] **DevOps**
- [x] **Backend**
- [ ] **Frontend**
- [ ] **AI-RAG**
- [ ] **Security Critical**

## Overview
This skill covers the modernization of legacy systems using the Strangler Fig Pattern, interfacing with mainframe systems (COBOL, AS/400), and gradual migration strategies. It enables replacing legacy systems incrementally while maintaining business continuity.

## Why This Matters
- **Risk Reduction**: Incremental migration reduces risk of complete system failure
- **Business Continuity**: Maintain operations during modernization
- **Modern Stack**: Leverage modern technologies and practices

---

## Core Concepts & Rules

### 1. Core Principles
- Follow established patterns and conventions
- Maintain consistency across codebase
- Document decisions and trade-offs

### 2. Implementation Guidelines
- Start with the simplest viable solution
- Iterate based on feedback and requirements
- Test thoroughly before deployment


## Inputs / Outputs / Contracts
* **Inputs**:
  - Legacy system documentation and code
  - Business requirements and use cases
  - Integration specifications
  - Migration roadmap and priorities
* **Entry Conditions**:
  - Access to legacy system (mainframe, AS/400)
  - Understanding of legacy technologies (COBOL, RPG)
  - Development environment for modern stack
  - Integration tools (IBM MQ, terminal emulators)
* **Outputs**:
  - API facade layer
  - Modern microservices
  - Integration adapters
  - Migration documentation
* **Artifacts Required (Deliverables)**:
  - Strangler Fig architecture design
  - API specifications (OpenAPI)
  - Integration adapters (MQ, REST)
  - Migration progress reports
* **Acceptance Evidence**:
  - API facade successfully exposes legacy functionality
  - New microservices replace legacy functionality
  - Data migration completes successfully
  - Business continuity maintained
* **Success Criteria**:
  - Migration progress > 80% of planned scope
  - API availability > 99.9%
  - Data accuracy > 99.99%
  - Zero downtime during migration

## Skill Composition
* **Depends on**: [legacy-migration-playbook](../../59-release-engineering/legacy-migration-playbook/SKILL.md), [api-design-contracts](../../71-infrastructure-patterns/api-design-contracts/SKILL.md)
* **Compatible with**: [multi-tenancy-saas](../../62-scale-operations/multi-tenancy-saas/SKILL.md), [data-quality-checks](../../53-data-engineering/data-quality-checks/SKILL.md)
* **Conflicts with**: None
* **Related Skills**: [service-standards-blueprint](../../64-meta-standards/service-standards-blueprint/SKILL.md)

---

## Quick Start / Implementation Example

1. Review requirements and constraints
2. Set up development environment
3. Implement core functionality following patterns
4. Write tests for critical paths
5. Run tests and fix issues
6. Document any deviations or decisions

```python
# Example implementation following best practices
def example_function():
    # Your implementation here
    pass
```


## Assumptions / Constraints / Non-goals

* **Assumptions**:
  - Development environment is properly configured
  - Required dependencies are available
  - Team has basic understanding of domain
* **Constraints**:
  - Must follow existing codebase conventions
  - Time and resource limitations
  - Compatibility requirements
* **Non-goals**:
  - This skill does not cover edge cases outside scope
  - Not a replacement for formal training


## Compatibility & Prerequisites

* **Supported Versions**:
  - Python 3.8+
  - Node.js 16+
  - Modern browsers (Chrome, Firefox, Safari, Edge)
* **Required AI Tools**:
  - Code editor (VS Code recommended)
  - Testing framework appropriate for language
  - Version control (Git)
* **Dependencies**:
  - Language-specific package manager
  - Build tools
  - Testing libraries
* **Environment Setup**:
  - `.env.example` keys: `API_KEY`, `DATABASE_URL` (no values)


## Test Scenario Matrix (QA Strategy)

| Type | Focus Area | Required Scenarios / Mocks |
| :--- | :--- | :--- |
| **Unit** | Core Logic | Must cover primary logic and at least 3 edge/error cases. Target minimum 80% coverage |
| **Integration** | DB / API | All external API calls or database connections must be mocked during unit tests |
| **E2E** | User Journey | Critical user flows to test |
| **Performance** | Latency / Load | Benchmark requirements |
| **Security** | Vuln / Auth | SAST/DAST or dependency audit |
| **Frontend** | UX / A11y | Accessibility checklist (WCAG), Performance Budget (Lighthouse score) |


## Technical Guardrails & Security Threat Model

### 1. Security & Privacy (Threat Model)
* **Top Threats**: Injection attacks, authentication bypass, data exposure
- [ ] **Data Handling**: Sanitize all user inputs to prevent Injection attacks. Never log raw PII
- [ ] **Secrets Management**: No hardcoded API keys. Use Env Vars/Secrets Manager
- [ ] **Authorization**: Validate user permissions before state changes

### 2. Performance & Resources
- [ ] **Execution Efficiency**: Consider time complexity for algorithms
- [ ] **Memory Management**: Use streams/pagination for large data
- [ ] **Resource Cleanup**: Close DB connections/file handlers in finally blocks

### 3. Architecture & Scalability
- [ ] **Design Pattern**: Follow SOLID principles, use Dependency Injection
- [ ] **Modularity**: Decouple logic from UI/Frameworks

### 4. Observability & Reliability
- [ ] **Logging Standards**: Structured JSON, include trace IDs `request_id`
- [ ] **Metrics**: Track `error_rate`, `latency`, `queue_depth`
- [ ] **Error Handling**: Standardized error codes, no bare except
- [ ] **Observability Artifacts**:
    - **Log Fields**: timestamp, level, message, request_id
    - **Metrics**: request_count, error_count, response_time
    - **Dashboards/Alerts**: High Error Rate > 5%


## Agent Directives & Error Recovery
*(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)*

- **Thinking Process**: Analyze root cause before fixing. Do not brute-force.
- **Fallback Strategy**: Stop after 3 failed test attempts. Output root cause and ask for human intervention/clarification.
- **Self-Review**: Check against Guardrails & Anti-patterns before finalizing.
- **Output Constraints**: Output ONLY the modified code block. Do not explain unless asked.


## Definition of Done (DoD) Checklist

- [ ] Tests passed + coverage met
- [ ] Lint/Typecheck passed
- [ ] Logging/Metrics/Trace implemented
- [ ] Security checks passed
- [ ] Documentation/Changelog updated
- [ ] Accessibility/Performance requirements met (if frontend)


## Anti-patterns / Pitfalls

* ⛔ **Don't**: Log PII, catch-all exception, N+1 queries
* ⚠️ **Watch out for**: Common symptoms and quick fixes
* 💡 **Instead**: Use proper error handling, pagination, and logging


## Reference Links & Examples

* Internal documentation and examples
* Official documentation and best practices
* Community resources and discussions


## Versioning & Changelog

* **Version**: 1.0.0
* **Changelog**:
  - 2026-02-22: Initial version with complete template structure