# System Planning Assistant

> High-level system architecture planning with component diagrams, scalability analysis, technology trade-offs, cost estimation, migration strategies, and technical specification documents Use when this capability is needed.

- Skill: `tomevault-io/system-planning-assistant` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/system-planning-assistant`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/system-planning-assistant/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Web & Frontend
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/system-planning-assistant

---


# Purpose

Assist with strategic system design decisions, analyzing trade-offs, defining architectures, and creating technical specifications for complex systems.

## When to Use

Use this skill when the task involves:

- Designing new system architecture from requirements
- Evaluating technology choices and trade-offs
- Planning system migrations or refactoring
- Creating technical specification documents
- Estimating infrastructure costs
- Analyzing scalability and reliability requirements

## Constraints

- Follow **Dependency Rule** — dependencies point inward to domain
- Keep **domain logic independent** of frameworks and infrastructure
- Treat **microservices as deployment strategy**, not architecture
- Apply **Domain-Driven Design** for complex business domains
- Document **explicit trade-offs** for every major decision
- Include **non-functional requirements** (latency, throughput, availability)

## Execution Steps

1. **Gather requirements** — Functional and non-functional (SLAs, scale targets)
2. **Identify domains** — Bounded contexts and their relationships
3. **Design components** — Services, databases, messaging, caching layers
4. **Create diagrams** — C4 model (context, container, component)
5. **Analyze trade-offs** — Document pros/cons of key decisions
6. **Estimate resources** — Team size, timeline, infrastructure costs
7. **Define milestones** — Phased delivery plan with risk mitigation

## Examples

### Input

> Design architecture for a real-time bidding platform handling 100k requests/second

### Output

````markdown
# Real-Time Bidding Platform Architecture

## Requirements

- **Throughput**: 100,000 requests/second peak
- **Latency**: P99 < 50ms for bid decisions
- **Availability**: 99.95% SLA

## High-Level Architecture

```mermaid
graph TB
    subgraph Edge
        CDN[CloudFront CDN]
        LB[Application Load Balancer]
    end

    subgraph Compute
        BidService[Bid Service<br/>Auto-scaled EKS]
        AuctionEngine[Auction Engine<br/>In-memory processing]
    end

    subgraph Data
        Redis[(Redis Cluster<br/>Campaign cache)]
        Kafka[Kafka<br/>Event streaming]
        TimescaleDB[(TimescaleDB<br/>Analytics)]
    end

    CDN --> LB --> BidService
    BidService --> Redis
    BidService --> AuctionEngine
    AuctionEngine --> Kafka
    Kafka --> TimescaleDB
```
````

## Key Decisions

| Decision  | Choice        | Trade-off                               |
| --------- | ------------- | --------------------------------------- |
| Caching   | Redis Cluster | +Low latency, -Eventual consistency     |
| Database  | TimescaleDB   | +Time-series optimized, -Less ecosystem |
| Messaging | Kafka         | +Durability, -Operational complexity    |

## Cost Estimate (Monthly)

- EKS (50 nodes): ~$15,000
- Redis Cluster: ~$3,000
- Kafka (MSK): ~$2,500
- Data transfer: ~$5,000
- **Total**: ~$25,500/month

```

## Related Skills

- `terraform-aws-builder` — Implement infrastructure
- `database-schema-designer` — Design data models
- `kafka-stream-designer` — Event streaming design
```

---
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