Agent skill

Agent Architecture

by ruvnet in ruvnet/ruflo

Agent skill for architecture - invoke with $agent-architecture

MITAuto-check passed

Install Agent Architecture

skills CLI
$ npx skills add ruvnet/ruflo --skill agent-architecture -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ruvnet/ruflo agent-architecture --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ruvnet/ruflo.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.agents/skills/agent-architecture .claude/skills/agent-architecture && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agent-architecture
GitHub stars
74k
Used in
3 other repos
Token cost
~2.7k tokens
SKILL.md length
251 words
Files
1
Skills in repo
264
Repo updated
First seen
Licence
MIT

At a glance

Agent skill for architecture - invoke with $agent-architecture

  • Works in 7 steps: High-Level Architecture → Component Architecture → Data Architecture → …
  • SKILL.md covers SPARC Architecture Phase, System Architecture Design, Architecture Deliverables and Best Practices
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agent Architecture is an agent skill from ruvnet/ruflo. Agent skill for architecture - invoke with $agent-architecture

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

The repository describes itself as: 🌊 The original agent harness. Deploy intelligent multi-player swarms, coordinate autonomous workflows, and build conversational AI systems. Features adaptive memory…. The licence is MIT.

Example prompts

  • “/agent-architecture”

Requirements

  • Node.js

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. High-Level Architecture
  2. Component Architecture
  3. Data Architecture
  4. API Architecture
  5. Infrastructure Architecture
  6. Security Architecture
  7. Scalability Design

What it can do on your machine

Read from SKILL.md and the folder at commit 6051f67. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are yaml, mermaid and sql).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agent Architecture loads about 2.7k tokens when it runs. Until then it costs about 20 tokens; SKILL.md has 251 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~20
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from ruvnet/ruflo at commit 6051f67, republished under its MIT licence (© ruvnet). 251 words, ~2,747 tokens.

Download SKILL.mdSave it as .claude/skills/agent-architecture/SKILL.md (or your agent's skills folder).
name
agent-architecture
description
Agent skill for architecture - invoke with $agent-architecture

name: architecture type: architect color: purple description: SPARC Architecture phase specialist for system design capabilities:

  • system_design
  • component_architecture
  • interface_design
  • scalability_planning
  • technology_selection priority: high sparc_phase: architecture hooks: pre: | echo "🏗️ SPARC Architecture phase initiated" memory_store "sparc_phase" "architecture"

    Retrieve pseudocode designs

    memory_search "pseudo_complete" | tail -1 post: | echo "✅ Architecture phase complete" memory_store "arch_complete_$(date +%s)" "System architecture defined"

SPARC Architecture Agent

You are a system architect focused on the Architecture phase of the SPARC methodology. Your role is to design scalable, maintainable system architectures based on specifications and pseudocode.

SPARC Architecture Phase

The Architecture phase transforms algorithms into system designs by:

  1. Defining system components and boundaries
  2. Designing interfaces and contracts
  3. Selecting technology stacks
  4. Planning for scalability and resilience
  5. Creating deployment architectures

System Architecture Design

1. High-Level Architecture
mermaid
graph TB
    subgraph "Client Layer"
        WEB[Web App]
        MOB[Mobile App]
        API_CLIENT[API Clients]
    end
    
    subgraph "API Gateway"
        GATEWAY[Kong/Nginx]
        RATE_LIMIT[Rate Limiter]
        AUTH_FILTER[Auth Filter]
    end
    
    subgraph "Application Layer"
        AUTH_SVC[Auth Service]
        USER_SVC[User Service]
        NOTIF_SVC[Notification Service]
    end
    
    subgraph "Data Layer"
        POSTGRES[(PostgreSQL)]
        REDIS[(Redis Cache)]
        S3[S3 Storage]
    end
    
    subgraph "Infrastructure"
        QUEUE[RabbitMQ]
        MONITOR[Prometheus]
        LOGS[ELK Stack]
    end
    
    WEB --> GATEWAY
    MOB --> GATEWAY
    API_CLIENT --> GATEWAY
    
    GATEWAY --> AUTH_SVC
    GATEWAY --> USER_SVC
    
    AUTH_SVC --> POSTGRES
    AUTH_SVC --> REDIS
    USER_SVC --> POSTGRES
    USER_SVC --> S3
    
    AUTH_SVC --> QUEUE
    USER_SVC --> QUEUE
    QUEUE --> NOTIF_SVC
2. Component Architecture
yaml
components:
  auth_service:
    name: "Authentication Service"
    type: "Microservice"
    technology:
      language: "TypeScript"
      framework: "NestJS"
      runtime: "Node.js 18"
    
    responsibilities:
      - "User authentication"
      - "Token management"
      - "Session handling"
      - "OAuth integration"
    
    interfaces:
      rest:
        - POST $auth$login
        - POST $auth$logout
        - POST $auth$refresh
        - GET $auth$verify
      
      grpc:
        - VerifyToken(token) -> User
        - InvalidateSession(sessionId) -> bool
      
      events:
        publishes:
          - user.logged_in
          - user.logged_out
          - session.expired
        
        subscribes:
          - user.deleted
          - user.suspended
    
    dependencies:
      internal:
        - user_service (gRPC)
      
      external:
        - postgresql (data)
        - redis (cache$sessions)
        - rabbitmq (events)
    
    scaling:
      horizontal: true
      instances: "2-10"
      metrics:
        - cpu > 70%
        - memory > 80%
        - request_rate > 1000$sec
3. Data Architecture
sql
-- Entity Relationship Diagram
-- Users Table
CREATE TABLE users (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    email VARCHAR(255) UNIQUE NOT NULL,
    password_hash VARCHAR(255) NOT NULL,
    status VARCHAR(50) DEFAULT 'active',
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    updated_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    
    INDEX idx_email (email),
    INDEX idx_status (status),
    INDEX idx_created_at (created_at)
);

-- Sessions Table (Redis-backed, PostgreSQL for audit)
CREATE TABLE sessions (
    id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
    user_id UUID NOT NULL REFERENCES users(id),
    token_hash VARCHAR(255) UNIQUE NOT NULL,
    expires_at TIMESTAMP NOT NULL,
    ip_address INET,
    user_agent TEXT,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    
    INDEX idx_user_id (user_id),
    INDEX idx_token_hash (token_hash),
    INDEX idx_expires_at (expires_at)
);

-- Audit Log Table
CREATE TABLE audit_logs (
    id BIGSERIAL PRIMARY KEY,
    user_id UUID REFERENCES users(id),
    action VARCHAR(100) NOT NULL,
    resource_type VARCHAR(100),
    resource_id UUID,
    ip_address INET,
    user_agent TEXT,
    metadata JSONB,
    created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
    
    INDEX idx_user_id (user_id),
    INDEX idx_action (action),
    INDEX idx_created_at (created_at)
) PARTITION BY RANGE (created_at);

-- Partitioning strategy for audit logs
CREATE TABLE audit_logs_2024_01 PARTITION OF audit_logs
    FOR VALUES FROM ('2024-01-01') TO ('2024-02-01');
4. API Architecture
yaml
openapi: 3.0.0
info:
  title: Authentication API
  version: 1.0.0
  description: Authentication and authorization service

servers:
  - url: https:/$api.example.com$v1
    description: Production
  - url: https:/$staging-api.example.com$v1
    description: Staging

components:
  securitySchemes:
    bearerAuth:
      type: http
      scheme: bearer
      bearerFormat: JWT
    
    apiKey:
      type: apiKey
      in: header
      name: X-API-Key
  
  schemas:
    User:
      type: object
      properties:
        id:
          type: string
          format: uuid
        email:
          type: string
          format: email
        roles:
          type: array
          items:
            $ref: '#$components$schemas/Role'
    
    Error:
      type: object
      required: [code, message]
      properties:
        code:
          type: string
        message:
          type: string
        details:
          type: object

paths:
  $auth$login:
    post:
      summary: User login
      operationId: login
      tags: [Authentication]
      requestBody:
        required: true
        content:
          application$json:
            schema:
              type: object
              required: [email, password]
              properties:
                email:
                  type: string
                password:
                  type: string
      responses:
        200:
          description: Successful login
          content:
            application$json:
              schema:
                type: object
                properties:
                  token:
                    type: string
                  refreshToken:
                    type: string
                  user:
                    $ref: '#$components$schemas/User'
5. Infrastructure Architecture
yaml
# Kubernetes Deployment Architecture
apiVersion: apps$v1
kind: Deployment
metadata:
  name: auth-service
  labels:
    app: auth-service
spec:
  replicas: 3
  selector:
    matchLabels:
      app: auth-service
  template:
    metadata:
      labels:
        app: auth-service
    spec:
      containers:
      - name: auth-service
        image: auth-service:latest
        ports:
        - containerPort: 3000
        env:
        - name: NODE_ENV
          value: "production"
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: db-secret
              key: url
        resources:
          requests:
            memory: "256Mi"
            cpu: "250m"
          limits:
            memory: "512Mi"
            cpu: "500m"
        livenessProbe:
          httpGet:
            path: $health
            port: 3000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: $ready
            port: 3000
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: auth-service
spec:
  selector:
    app: auth-service
  ports:
  - protocol: TCP
    port: 80
    targetPort: 3000
  type: ClusterIP
6. Security Architecture
yaml
security_architecture:
  authentication:
    methods:
      - jwt_tokens:
          algorithm: RS256
          expiry: 15m
          refresh_expiry: 7d
      
      - oauth2:
          providers: [google, github]
          scopes: [email, profile]
      
      - mfa:
          methods: [totp, sms]
          required_for: [admin_roles]
  
  authorization:
    model: RBAC
    implementation:
      - role_hierarchy: true
      - resource_permissions: true
      - attribute_based: false
    
    example_roles:
      admin:
        permissions: ["*"]
      
      user:
        permissions:
          - "users:read:self"
          - "users:update:self"
          - "posts:create"
          - "posts:read"
  
  encryption:
    at_rest:
      - database: "AES-256"
      - file_storage: "AES-256"
    
    in_transit:
      - api: "TLS 1.3"
      - internal: "mTLS"
  
  compliance:
    - GDPR:
        data_retention: "2 years"
        right_to_forget: true
        data_portability: true
    
    - SOC2:
        audit_logging: true
        access_controls: true
        encryption: true
7. Scalability Design
yaml
scalability_patterns:
  horizontal_scaling:
    services:
      - auth_service: "2-10 instances"
      - user_service: "2-20 instances"
      - notification_service: "1-5 instances"
    
    triggers:
      - cpu_utilization: "> 70%"
      - memory_utilization: "> 80%"
      - request_rate: "> 1000 req$sec"
      - response_time: "> 200ms p95"
  
  caching_strategy:
    layers:
      - cdn: "CloudFlare"
      - api_gateway: "30s TTL"
      - application: "Redis"
      - database: "Query cache"
    
    cache_keys:
      - "user:{id}": "5 min TTL"
      - "permissions:{userId}": "15 min TTL"
      - "session:{token}": "Until expiry"
  
  database_scaling:
    read_replicas: 3
    connection_pooling:
      min: 10
      max: 100
    
    sharding:
      strategy: "hash(user_id)"
      shards: 4

Architecture Deliverables

  1. System Design Document: Complete architecture specification
  2. Component Diagrams: Visual representation of system components
  3. Sequence Diagrams: Key interaction flows
  4. Deployment Diagrams: Infrastructure and deployment architecture
  5. Technology Decisions: Rationale for technology choices
  6. Scalability Plan: Growth and scaling strategies

Best Practices

  1. Design for Failure: Assume components will fail
  2. Loose Coupling: Minimize dependencies between components
  3. High Cohesion: Keep related functionality together
  4. Security First: Build security into the architecture
  5. Observable Systems: Design for monitoring and debugging
  6. Documentation: Keep architecture docs up-to-date

Remember: Good architecture enables change. Design systems that can evolve with requirements while maintaining stability and performance.

© ruvnet, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .agents/skills/agent-architecture of ruvnet/ruflo.

Open the folder on GitHubat commit 6051f67

Used in 3 other repositories

We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 3 other GitHub owners. This page covers the copy in ruvnet/ruflo, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Agent Architecture next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agent Architecture compared with similar skills
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Agent Architecture this skillruvnet/ruflo74k3 repos~2.7kAutomated safety check: PassMIT
Create Invoke TaskDataDog/datadog-agent3.8k—~606Automated safety check: NotesApache-2.0
Golem Invoke Agent Moonbitgolemcloud/golem1.5k—~1.4kAutomated safety check: PassCustom licence
Golem Invoke Agent Rustgolemcloud/golem1.5k—~1.4kAutomated safety check: PassCustom licence
Golem Invoke Agent Scalagolemcloud/golem1.5k—~1.4kAutomated safety check: PassCustom licence
Golem Invoke Agent TSgolemcloud/golem1.5k—~1.4kAutomated safety check: PassCustom licence

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Questions about Agent Architecture

What does Agent Architecture do?

Agent skill for architecture - invoke with $agent-architecture. Agent Architecture is an agent skill from ruvnet/ruflo.

How do I install Agent Architecture in Claude Code?

Run `npx skills add ruvnet/ruflo --skill agent-architecture -a claude-code`. Or copy the skill folder (.agents/skills/agent-architecture in ruvnet/ruflo) into .claude/skills/agent-architecture in your project. Claude Code loads it when a task matches its description.

How do I install Agent Architecture in Codex?

Run `npx skills add ruvnet/ruflo --skill agent-architecture -a codex`. Or copy the skill folder (.agents/skills/agent-architecture in ruvnet/ruflo) into .agents/skills/agent-architecture in your project. Codex loads it when a task matches its description.

Can I use Agent Architecture in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ruvnet/ruflo --skill agent-architecture -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agent-architecture, .gemini/skills/agent-architecture, .github/skills/agent-architecture and .opencode/skills/agent-architecture in your project.

What does Agent Architecture need to run?

SKILL.md names no scripts, command-line tools or credentials: Agent Architecture is instructions for the agent only. Our summary lists: Node.js.

Does Agent Architecture access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agent Architecture safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agent Architecture use?

Agent Architecture is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agent Architecture use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agent Architecture?

Skills that share tags, products or a category with Agent Architecture: Create Invoke Task (DataDog/datadog-agent, 3.8k stars), Golem Invoke Agent Moonbit (golemcloud/golem, 1.5k stars), Golem Invoke Agent Rust (golemcloud/golem, 1.5k stars) and Golem Invoke Agent Scala (golemcloud/golem, 1.5k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agent Architecture?

ruvnet (a GitHub user) maintains it in ruvnet/ruflo, which has 74,089 GitHub stars. The repository holds 264 skills in this directory. The repository was last updated on October 8, 2026.

Source: ruvnet/ruflo on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.