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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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Listed on Skillselion
Installs68
repo stars1
Last updatedMarch 16, 2026
Repositorypixel-process-ug/superkit-agents

What it does

Helps with ai & agent building tasks.

Files

SKILL.mdMarkdownGitHub ↗

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

FactorMonolithModular MonolithMicroservicesServerless
Team size < 10PreferredStrong fitOverkillGood for bursty
Team size > 30ChallengingGoodPreferredDepends
Domain well-understoodGood fitGood fitNot needed yetGood fit
Domain evolving rapidlyFine to startGood fitToo earlyGood fit
Need independent deploymentNot possibleLimitedKey benefitBuilt-in
Operational maturity lowGood fitGood fitHigh riskManaged risk
Variable/bursty loadOver-provisionedOver-provisionedPossibleStrong 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

ImprovingMay Degrade
ConsistencyAvailability, Latency
PerformanceMaintainability, Cost
SecurityUsability, Performance
ScalabilitySimplicity, Cost
FlexibilityPerformance, Complexity
Time to MarketQuality, 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

LevelShowsWhen to Use
Level 1: System ContextUsers and external systemsAlways
Level 2: ContainerMajor technical building blocksAlways
Level 3: ComponentComponents within containersComplex services
Level 4: CodeClass-level detailCritical/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

PatternUse WhenImplementation
Load BalancingMultiple instances of same serviceRound-robin, least connections, IP hash
Stateless ServicesNeed to add/remove instances freelyJWT/external session store
Auto-scalingVariable load patternsCPU/memory/request-rate triggers
Read ReplicasRead-heavy workloadsRoute reads to replicas, writes to primary

Sharding Strategy Decision Table

StrategyHowGood For
Hash-basedConsistent hash of keyEven distribution
Range-basedDate range, ID rangeTime-series data
GeographicBy region/countryData locality
Tenant-basedPer customerMulti-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 severity

SLO/SLA/SLI Framework

TermDefinitionExample
SLIMeasurable metricRequest latency, error rate
SLOTarget value99.9% availability
SLAContractual commitment99.5% with penalty clause
Error Budget1 - SLO0.1% = 8.76h/year

---

Anti-Patterns / Common Mistakes

Anti-PatternWhy It Is WrongCorrect Approach
Resume-driven architectureComplexity without benefitChoose simplest solution that works
Distributed monolithAll downsides of bothEither true monolith or true microservices
Premature optimizationScaling for 1M users with 100Design for current + 10x, not 1000x
Golden hammerOne technology for everythingRight tool for each problem
Architecture without validationUntested assumptionsLoad test, failure test, validate
Big upfront design without iterationRequirements changeEvolutionary architecture with guardrails
Vague NFRs ("fast", "scalable")Cannot be validatedQuantified targets with measurement

---

Subagent Dispatch Opportunities

Task PatternDispatch ToWhen
Analyzing different architecture layersAgent tool with subagent_type="Explore" (one per layer)When reviewing frontend, backend, and infra independently
Security assessment of architectureAgent tool invoking security-review skillWhen architecture involves auth, data flow, or external APIs
Performance implications analysisAgent tool invoking performance-optimization skillWhen architecture decisions affect latency or throughput
Code quality review of existing patternsAgent tool dispatching code-reviewer agentWhen evaluating current codebase for refactoring

Follow the dispatching-parallel-agents skill protocol when dispatching.

---

Integration Points

SkillRelationship
senior-backendBackend implementation follows architecture decisions
senior-fullstackFull-stack architecture follows service boundaries
security-reviewSecurity is a cross-cutting architectural concern
performance-optimizationPerformance NFRs drive optimization targets
planningArchitecture decisions inform implementation planning
code-reviewReview validates architectural consistency
acceptance-testingNFRs 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.

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