Anthropic Architect

Anthropic Architect

Expert architectural guidance for Anthropic-based projects. Analyze your requirements and receive tailored recommendations on the optimal architecture using Skills, Agents, Subagents, Prompts, and SDK primitives.

What This Skill Does

Helps you design the right Anthropic architecture for your project by:

• Analyzing project requirements - Understanding complexity, scope, and constraints
• Recommending architectures - Skills vs Agents vs Prompts vs SDK primitives
• Applying decision rubrics - Data-driven architectural choices
• Following best practices - 2025 Anthropic patterns and principles
• Progressive disclosure design - Efficient context management
• Security considerations - Safe, controllable AI systems

Why Architecture Matters

Without proper architecture:

• Inefficient context usage and high costs
• Poor performance and slow responses
• Security vulnerabilities and risks
• Difficult to maintain and scale
• Agents reading entire skill contexts unnecessarily
• Mixed concerns and unclear boundaries

With engineered architecture:

• Optimal context utilization
• Fast, focused responses
• Secure, controlled operations
• Easy to maintain and extend
• Progressive disclosure of information
• Clear separation of concerns
• Scalable and reusable components

Quick Start

Analyze Your Project

Using the anthropic-architect skill, help me determine the best
architecture for: [describe your project]

Requirements:
- [List your key requirements]
- [Complexity level]
- [Reusability needs]
- [Security constraints]

Get Architecture Recommendation

The skill will provide:

1. Recommended architecture - Specific primitives to use
2. Decision reasoning - Why this architecture fits
3. Implementation guidance - How to build it
4. Best practices - What to follow
5. Example patterns - Similar successful architectures

The Four Anthropic Primitives

1. Skills (Prompt-Based Meta-Tools)

What: Organized folders of instructions, scripts, and resources that agents can discover and load dynamically.

When to use:

• ✅ Specialized domain knowledge needed
• ✅ Reusable across multiple projects
• ✅ Complex, multi-step workflows
• ✅ Reference materials required
• ✅ Progressive disclosure beneficial

When NOT to use:

• ❌ Simple, one-off tasks
• ❌ Project-specific logic only
• ❌ No need for reusability

Example use cases:

• Prompt engineering expertise
• Design system generation
• Code review guidelines
• Domain-specific knowledge (finance, medical, legal)

2. Agents/Subagents (Autonomous Task Handlers)

What: Specialized agents with independent system prompts, dedicated context windows, and specific tool permissions.

When to use:

• ✅ Complex, multi-step autonomous tasks
• ✅ Need for isolated context
• ✅ Different tool permissions required
• ✅ Parallel task execution
• ✅ Specialized expertise per task type

When NOT to use:

• ❌ Simple queries or lookups
• ❌ Shared context required
• ❌ Sequential dependencies
• ❌ Resource-constrained environments

Example use cases:

• Code exploration and analysis
• Test generation and execution
• Documentation generation
• Security audits
• Performance optimization

3. Direct Prompts (Simple Instructions)

What: Clear, explicit instructions passed directly to Claude without additional structure.

When to use:

• ✅ Simple, straightforward tasks
• ✅ One-time operations
• ✅ Quick questions or clarifications
• ✅ No need for specialization
• ✅ Minimal context required

When NOT to use:

• ❌ Complex, multi-step processes
• ❌ Need for reusability
• ❌ Requires domain expertise
• ❌ Multiple related operations

Example use cases:

• Code explanations
• Quick refactoring
• Simple bug fixes
• Documentation updates
• Direct questions

4. SDK Primitives (Custom Workflows)

What: Low-level building blocks from the Claude Agent SDK to create custom agent workflows.

When to use:

• ✅ Unique workflow requirements
• ✅ Custom tool integration needed
• ✅ Specific feedback loops required
• ✅ Integration with existing systems
• ✅ Fine-grained control needed

When NOT to use:

• ❌ Standard use cases covered by Skills/Agents
• ❌ Limited development resources
• ❌ Maintenance burden concern
• ❌ Faster time-to-market priority

Example use cases:

• Custom CI/CD integration
• Specialized code analysis pipelines
• Domain-specific automation
• Integration with proprietary systems

Decision Rubric

Use this rubric to determine the right architecture:

Task Complexity Analysis

Low Complexity → Direct Prompts

• Single operation
• Clear input/output
• No dependencies
• < 5 steps

Medium Complexity → Skills

• Multiple related operations
• Reusable patterns
• Reference materials helpful
• 5-20 steps

High Complexity → Agents/Subagents

• Multi-step autonomous workflow
• Needs isolated context
• Different tool permissions

• 20 steps or parallel tasks

Custom Complexity → SDK Primitives

• Unique workflows
• System integration required
• Custom tools needed
• Specific feedback loops

Reusability Assessment

Single Use → Direct Prompts

• One-time task
• Project-specific
• No future reuse

Team Reuse → Skills

• Multiple team members benefit
• Common workflows
• Shareable knowledge

Organization Reuse → Skills + Marketplace

• Cross-team benefit
• Standard patterns
• Company-wide knowledge

Product Feature → SDK Primitives

• End-user facing
• Production deployment
• Custom integration

Context Management Needs

Minimal Context → Direct Prompts

• Self-contained task
• No external references
• Simple instructions

Structured Context → Skills

• Progressive disclosure needed
• Reference materials required
• Organized information

Isolated Context → Agents/Subagents

• Separate concerns
• Avoid context pollution
• Parallel execution

Custom Context → SDK Primitives

• Specific context handling
• Integration requirements
• Fine-grained control

Security & Control Requirements

Basic Safety → Direct Prompts + Skills

• Standard guardrails
• No sensitive operations
• Read-only or low-risk

Controlled Access → Agents with Tool Restrictions

• Specific tool permissions
• Allowlist approach
• Confirmation required

High Security → SDK Primitives + Custom Controls

• Deny-all default
• Explicit confirmations
• Audit logging
• Custom security layers

Architecture Patterns

Pattern 1: Skills-First Architecture

Use when: Building reusable expertise and workflows

Structure:

Project
├── skills/
│   ├── domain-expert/
│   │   ├── SKILL.md
│   │   └── references/
│   │       ├── patterns.md
│   │       ├── best_practices.md
│   │       └── examples.md
│   └── workflow-automation/
│       ├── SKILL.md
│       └── scripts/
│           └── automate.sh
└── .claude/
    └── config

Benefits:

• Reusable across projects
• Progressive disclosure
• Easy to share and maintain
• Clear documentation

Pattern 2: Agent-Based Architecture

Use when: Complex autonomous tasks with isolated concerns

Structure:

Main Agent (orchestrator)
├── Explore Agent (codebase analysis)
├── Plan Agent (task planning)
├── Code Agent (implementation)
└── Review Agent (validation)

Benefits:

• Parallel execution
• Isolated contexts
• Specialized expertise
• Clear responsibilities

Pattern 3: Hybrid Architecture

Use when: Complex projects with varied requirements

Structure:

Main Conversation
├── Direct Prompts (simple tasks)
├── Skills (reusable expertise)
│   ├── code-review-skill
│   └── testing-skill
└── Subagents (complex workflows)
    ├── Explore Agent
    └── Plan Agent

Benefits:

• Right tool for each task
• Optimal resource usage
• Flexible and scalable
• Best of all approaches

Pattern 4: SDK Custom Architecture

Use when: Unique requirements or product features

Structure:

Custom Agent SDK Implementation
├── Custom Tools
├── Specialized Feedback Loops
├── System Integrations
└── Domain-Specific Workflows

Benefits:

• Full control
• Custom integration
• Unique workflows
• Production-ready

Key Principles (2025)

1. Progressive Disclosure

What: Show only what's needed, when it's needed.

Why: Avoids context limits, reduces costs, improves performance.

How: Organize skills with task-based navigation, provide query tools, structure information hierarchically.

2. Context as Resource

What: Treat context window as precious, limited resource.

Why: Every token counts toward limits and costs.

How: Use progressive disclosure, prefer retrieval over dumping, compress aggressively, reset periodically.