Senior Architect
- 68 installs
- 1 repo stars
- Updated March 16, 2026
- pixel-process-ug/superkit-agents
Helps with ai & agent building tasks.
About
senior-architect is a Claude Code skill for ai & agent building. It helps solo builders move faster with AI-assisted development.
- senior-architect
- AI & Agent Building
- AI-coding skill
Senior Architect by the numbers
- 68 all-time installs (skills.sh)
- +1 installs in the week ending Aug 4, 2026 (Skillselion tracking)
- Ranked #5,858 of 16,546 AI & Agent Building skills by installs in the Skillselion catalog
- Data as of Aug 4, 2026 (Skillselion catalog sync)
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| Installs | 68 |
|---|---|
| repo stars | ★ 1 |
| Last updated | March 16, 2026 |
| Repository | pixel-process-ug/superkit-agents ↗ |
What it does
Helps with ai & agent building tasks.
Files
Senior Architect
Overview
Provide architecture-level guidance for system design decisions. This skill produces Architecture Decision Records (ADRs), trade-off analyses, scalability blueprints, and non-functional requirements specifications. Every recommendation includes explicit trade-offs and is grounded in proven patterns.
Announce at start: "I'm using the senior-architect skill for system design and architecture decisions."
---
Phase 1: Requirements Analysis
Goal: Capture all functional and non-functional requirements before designing.
Actions
1. Identify functional requirements (capabilities) 2. Define non-functional requirements (quality attributes) 3. Identify constraints (budget, team, timeline, compliance) 4. Map integration points with existing systems 5. Establish success criteria and SLOs
STOP — Do NOT proceed to Phase 2 until:
- [ ] Functional requirements are listed
- [ ] Non-functional requirements are quantified (not vague)
- [ ] Constraints are explicit
- [ ] Success criteria are measurable
---
Phase 2: Architecture Design
Goal: Evaluate options and select the approach with the best trade-off profile.
Actions
1. Evaluate architectural styles (monolith, microservices, event-driven) 2. Design component boundaries and interfaces 3. Define data architecture (storage, flow, consistency) 4. Plan infrastructure and deployment topology 5. Address cross-cutting concerns (auth, logging, monitoring)
Architecture Style Decision Table
| Factor | Monolith | Modular Monolith | Microservices | Serverless |
|---|---|---|---|---|
| Team size < 10 | Preferred | Strong fit | Overkill | Good for bursty |
| Team size > 30 | Challenging | Good | Preferred | Depends |
| Domain well-understood | Good fit | Good fit | Not needed yet | Good fit |
| Domain evolving rapidly | Fine to start | Good fit | Too early | Good fit |
| Need independent deployment | Not possible | Limited | Key benefit | Built-in |
| Operational maturity low | Good fit | Good fit | High risk | Managed risk |
| Variable/bursty load | Over-provisioned | Over-provisioned | Possible | Strong fit |
Default Recommendation
Start with Modular Monolith: clear module boundaries, single deployment. Extract to microservices only when you have proven need for independent scaling, deployment, or team autonomy.
Trade-Off Analysis: Common Pairs
| Improving | May Degrade |
|---|---|
| Consistency | Availability, Latency |
| Performance | Maintainability, Cost |
| Security | Usability, Performance |
| Scalability | Simplicity, Cost |
| Flexibility | Performance, Complexity |
| Time to Market | Quality, Scalability |
Decision Matrix Template
Weight each quality attribute (1-5), score each option (1-5), multiply and sum.
| Quality Attribute | Weight | Option A | Option B | Option C |
|--------------------|--------|----------|----------|----------|
| Performance | 4 | 4 (16) | 3 (12) | 5 (20) |
| Maintainability | 5 | 5 (25) | 4 (20) | 2 (10) |
| Scalability | 3 | 3 (9) | 5 (15) | 4 (12) |
| Cost | 4 | 4 (16) | 2 (8) | 3 (12) |
| Total | | 66 | 55 | 54 |STOP — Do NOT proceed to Phase 3 until:
- [ ] At least 2 architectural options have been evaluated
- [ ] Trade-offs are explicitly documented
- [ ] Decision matrix scores support the recommendation
- [ ] Data architecture is defined
---
Phase 3: Documentation and Validation
Goal: Record decisions and validate against requirements.
Actions
1. Write Architecture Decision Records for key decisions 2. Create system context and container diagrams (C4 model) 3. Validate against non-functional requirements 4. Identify risks and mitigation strategies 5. Define evolutionary architecture guardrails
ADR Format
# ADR-{number}: {Title}
## Status
Proposed | Accepted | Deprecated | Superseded by ADR-{number}
## Context
What is the issue motivating this decision?
## Decision
What change are we proposing?
## Consequences
### Positive
- [Benefit 1]
- [Benefit 2]
### Negative
- [Trade-off 1]
- [Trade-off 2]
### Risks
- [Risk and mitigation]
## Alternatives Considered
| Option | Pros | Cons | Verdict |
|--------|------|------|---------|
| Option A | ... | ... | Chosen |
| Option B | ... | ... | Rejected because... |C4 Model Levels
| Level | Shows | When to Use |
|---|---|---|
| Level 1: System Context | Users and external systems | Always |
| Level 2: Container | Major technical building blocks | Always |
| Level 3: Component | Components within containers | Complex services |
| Level 4: Code | Class-level detail | Critical/complex areas only |
STOP — Documentation complete when:
- [ ] ADRs written for all key decisions
- [ ] System context diagram created
- [ ] NFRs validated against design
- [ ] Risks documented with mitigations
---
Scalability Patterns
Horizontal Scaling Decision Table
| Pattern | Use When | Implementation |
|---|---|---|
| Load Balancing | Multiple instances of same service | Round-robin, least connections, IP hash |
| Stateless Services | Need to add/remove instances freely | JWT/external session store |
| Auto-scaling | Variable load patterns | CPU/memory/request-rate triggers |
| Read Replicas | Read-heavy workloads | Route reads to replicas, writes to primary |
Sharding Strategy Decision Table
| Strategy | How | Good For |
|---|---|---|
| Hash-based | Consistent hash of key | Even distribution |
| Range-based | Date range, ID range | Time-series data |
| Geographic | By region/country | Data locality |
| Tenant-based | Per customer | Multi-tenant SaaS |
Caching Layers
Client Cache (browser) -> CDN Cache -> API Gateway Cache ->
Application Cache (Redis) -> Database Query Cache -> Database---
Non-Functional Requirements Template
## Performance
- Response time: p95 < 200ms, p99 < 500ms for API calls
- Throughput: 1000 RPS sustained, 5000 RPS peak
- Batch processing: 1M records/hour
## Availability
- Target: 99.9% (8.76h downtime/year)
- RTO (Recovery Time Objective): < 15 minutes
- RPO (Recovery Point Objective): < 5 minutes
## Scalability
- Current: 10K DAU
- 12-month target: 100K DAU
- Scale dimension: users, data volume, request rate
## Security
- Authentication: OAuth 2.0 / OIDC
- Authorization: RBAC with resource-level permissions
- Data encryption: at rest (AES-256) and in transit (TLS 1.3)
## Observability
- Logging: structured JSON, 30-day retention
- Metrics: RED method, custom business metrics
- Tracing: distributed tracing across all services
- Alerting: PagerDuty integration, tiered severitySLO/SLA/SLI Framework
| Term | Definition | Example |
|---|---|---|
| SLI | Measurable metric | Request latency, error rate |
| SLO | Target value | 99.9% availability |
| SLA | Contractual commitment | 99.5% with penalty clause |
| Error Budget | 1 - SLO | 0.1% = 8.76h/year |
---
Anti-Patterns / Common Mistakes
| Anti-Pattern | Why It Is Wrong | Correct Approach |
|---|---|---|
| Resume-driven architecture | Complexity without benefit | Choose simplest solution that works |
| Distributed monolith | All downsides of both | Either true monolith or true microservices |
| Premature optimization | Scaling for 1M users with 100 | Design for current + 10x, not 1000x |
| Golden hammer | One technology for everything | Right tool for each problem |
| Architecture without validation | Untested assumptions | Load test, failure test, validate |
| Big upfront design without iteration | Requirements change | Evolutionary architecture with guardrails |
| Vague NFRs ("fast", "scalable") | Cannot be validated | Quantified targets with measurement |
---
Subagent Dispatch Opportunities
| Task Pattern | Dispatch To | When |
|---|---|---|
| Analyzing different architecture layers | Agent tool with subagent_type="Explore" (one per layer) | When reviewing frontend, backend, and infra independently |
| Security assessment of architecture | Agent tool invoking security-review skill | When architecture involves auth, data flow, or external APIs |
| Performance implications analysis | Agent tool invoking performance-optimization skill | When architecture decisions affect latency or throughput |
| Code quality review of existing patterns | Agent tool dispatching code-reviewer agent | When evaluating current codebase for refactoring |
Follow the dispatching-parallel-agents skill protocol when dispatching.
---
Integration Points
| Skill | Relationship |
|---|---|
senior-backend | Backend implementation follows architecture decisions |
senior-fullstack | Full-stack architecture follows service boundaries |
security-review | Security is a cross-cutting architectural concern |
performance-optimization | Performance NFRs drive optimization targets |
planning | Architecture decisions inform implementation planning |
code-review | Review validates architectural consistency |
acceptance-testing | NFRs become acceptance criteria |
---
Key Principles
- Start with the simplest architecture that could work
- Make decisions reversible when possible
- Design for failure (everything will fail eventually)
- Optimize for team cognitive load, not technical elegance
- Document decisions, not just outcomes
- Prefer boring technology for critical paths
- Every architectural decision has a cost — make it explicit
---
Skill Type
FLEXIBLE — Adapt architecture recommendations to the specific context. ADRs are strongly recommended for all significant decisions. Trade-off analysis is mandatory. NFRs must be quantified, not described vaguely.