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The Intelligence Router implements the "Twin-Engine" Intelligence Architecture that governs how skills are routed, composed, and validated within the CerebraSkills ecosystem. Layer A provides determin
Generate a standardized shift-left engineering documentation set (Blueprint, Requirements, Verification, Architecture, Contract, Specs, Operation, and Agentic Work Ticket) under a target project docs folder using canonical 1:1 heading templates.
Architectural Reviews is a critical process for evaluating system designs before implementation, helping to reduce risks in large-scale systems where incorrect early decisions can cause millions of do
Architecture Decision Records (ADRs) are lightweight documents that capture important architectural decisions and their rationale, providing a historical record that helps teams understand the "why" b
Problem framing is the critical first step before any implementation, teaching agents to detect vague or incomplete requirements, pause execution, and ask clarifying questions. This skill reduces hall
Risk assessment is a systematic process of identifying, analyzing, and evaluating potential risks that could affect project success, system stability, or business operations. This skill provides frame
Self-Validation CI/CD provides an automated pipeline for testing AI-generated code and documentation. It uses a dummy project to verify that AI-generated skills work correctly before they are committe
| id | SKL-intelligence-INTELLIGENCEROUTER |
| name | Intelligence Router |
| description | The Intelligence Router implements the "Twin-Engine" Intelligence Architecture that governs how skills are routed, composed, and validated within the CerebraSkills ecosystem. Layer A provides determin |
| 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 |
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The Intelligence Router implements the "Twin-Engine" Intelligence Architecture that governs how skills are routed, composed, and validated within the CerebraSkills ecosystem. Layer A provides deterministic, predictable routing through metadata validation and rule-based matching, while Layer B enhances with AI-assisted semantic search, intent mapping, and quality improvement suggestions. This dual approach ensures reliability while enabling intelligent, context-aware skill discovery.
skill-generatorskill-router, swarm-collaboration# Example implementation following best practices
def example_function():
# Your implementation here
pass
.env.example keys: API_KEY, DATABASE_URL (no values)| 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) |
request_iderror_rate, latency, queue_depth(ข้อกำหนดสำหรับ AI Agent ในการคิดและแก้ปัญหาเมื่อเกิดข้อผิดพลาด)