dtn-leo-connectivity

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This skill covers the implementation and management of network protocols for space-based communications, specifically focusing on Low Earth Orbit (LEO) satellite constellations and Disruption-Tolerant

AmnadTaowsoam By AmnadTaowsoam schedule Updated 2/22/2026

id: SKL-dtn-DTNLEOCONNECTIVITY name: Dtn Leo Connectivity description: This skill covers the implementation and management of network protocols for space-based communications, specifically focusing on Low Earth Orbit (LEO) satellite constellations and Disruption-Tolerant 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

Dtn Leo Connectivity

Skill Profile

(Select at least one profile to enable specific modules)

  • DevOps
  • Backend
  • Frontend
  • AI-RAG
  • Security Critical

Overview

This skill covers the implementation and management of network protocols for space-based communications, specifically focusing on Low Earth Orbit (LEO) satellite constellations and Disruption-Tolerant Networking (DTN). It enables building applications that can operate in high-latency, intermittent connectivity environments typical of space communications.

Why This Matters

  • Global Connectivity: Enables internet access in remote and underserved areas
  • Disruption Tolerance: Applications continue functioning during connectivity interruptions
  • Space Communication: Essential for satellite constellations, deep space missions, and aerospace applications

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:
    • Satellite constellation configuration (orbital parameters, beam patterns)
    • DTN bundle routing tables and custody policies
    • Ground station locations and capabilities
    • Application data bundles with priority and TTL
  • Entry Conditions:
    • DTN2 or ION (Interplanetary Overlay Network) installed
    • Ground station connectivity established
    • Satellite terminal credentials and API access
    • Time synchronization with UTC (critical for orbital calculations)
  • Outputs:
    • DTN bundle delivery confirmations and custody receipts
    • Satellite link quality metrics (SNR, latency, throughput)
    • Ground station handover events
    • Application data delivery status
  • Artifacts Required (Deliverables):
    • DTN configuration files (bp.conf, ipnfw.conf)
    • Bundle routing policies and custody transfer rules
    • Satellite terminal integration code
    • Monitoring dashboards for constellation status
  • Acceptance Evidence:
    • Successful bundle delivery through multi-hop DTN network
    • Handover between satellites without connection loss
    • Application data survives connectivity interruptions
    • Link quality metrics meet SLA requirements
  • Success Criteria:
    • Bundle delivery rate > 95% for high-priority traffic
    • Handover latency < 500ms
    • Application continues functioning during 30-minute outages
    • End-to-end latency < 150ms for LEO constellation (best case)

Skill Composition


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
# 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
Install via CLI
npx skills add https://github.com/AmnadTaowsoam/CerebraSkills --skill dtn-leo-connectivity
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