This marketplace follows industry best practices with a focus on granularity, composability, and minimal token usage.
- Each plugin does one thing well (Unix philosophy)
- Clear, focused purposes (describable in 5-10 words)
- Average plugin size: 3.4 components (follows Anthropic's 2-8 pattern)
- Zero bloated plugins - all plugins focused and purposeful
- Mix and match plugins based on needs
- Workflow orchestrators compose focused plugins
- No forced feature bundling
- Clear boundaries between plugins
- Smaller tools = faster processing
- Better fit in LLM context windows
- More accurate, focused responses
- Install only what you need
- Single-purpose = easier updates
- Clear boundaries = isolated changes
- Less duplication = simpler maintenance
- Isolated dependencies
- 67 focused plugins optimized for specific use cases
- 23 clear categories with 1-6 plugins each for easy discovery
- Organized by domain:
- Development: 4 plugins (debugging, backend, frontend, multi-platform)
- Security: 4 plugins (scanning, compliance, backend-api, frontend-mobile)
- Operations: 4 plugins (incident, diagnostics, distributed, observability)
- Languages: 7 plugins (Python, JS/TS, systems, JVM, scripting, functional, embedded)
- Infrastructure: 5 plugins (deployment, validation, K8s, cloud, CI/CD)
- And 18 more specialized categories
99 Specialized Agents
- Domain experts with deep knowledge
- Organized across architecture, languages, infrastructure, quality, data/AI, documentation, business, and SEO
- Model-optimized with three-tier strategy (Opus, Sonnet, Haiku) for performance and cost
15 Workflow Orchestrators
- Multi-agent coordination systems
- Complex operations like full-stack development, security hardening, ML pipelines, incident response
- Pre-configured agent workflows
71 Development Tools
- Optimized utilities including:
- Project scaffolding (Python, TypeScript, Rust)
- Security scanning (SAST, dependency audit, XSS)
- Test generation (pytest, Jest)
- Component scaffolding (React, React Native)
- Infrastructure setup (Terraform, Kubernetes)
107 Agent Skills
- Modular knowledge packages
- Progressive disclosure architecture
- Domain-specific expertise across 18 plugins
- Spec-compliant (Anthropic Agent Skills Specification)
claude-agents/
├── .claude-plugin/
│ └── marketplace.json # Marketplace catalog (67 plugins)
├── plugins/ # Isolated plugin directories
│ ├── python-development/
│ │ ├── agents/ # Python language agents
│ │ │ ├── python-pro.md
│ │ │ ├── django-pro.md
│ │ │ └── fastapi-pro.md
│ │ ├── commands/ # Python tooling
│ │ │ └── python-scaffold.md
│ │ └── skills/ # Python skills (5 total)
│ │ ├── async-python-patterns/
│ │ ├── python-testing-patterns/
│ │ ├── python-packaging/
│ │ ├── python-performance-optimization/
│ │ └── uv-package-manager/
│ ├── backend-development/
│ │ ├── agents/
│ │ │ ├── backend-architect.md
│ │ │ ├── graphql-architect.md
│ │ │ └── tdd-orchestrator.md
│ │ ├── commands/
│ │ │ └── feature-development.md
│ │ └── skills/ # Backend skills (3 total)
│ │ ├── api-design-principles/
│ │ ├── architecture-patterns/
│ │ └── microservices-patterns/
│ ├── security-scanning/
│ │ ├── agents/
│ │ │ └── security-auditor.md
│ │ ├── commands/
│ │ │ ├── security-hardening.md
│ │ │ ├── security-sast.md
│ │ │ └── security-dependencies.md
│ │ └── skills/ # Security skills (1 total)
│ │ └── sast-configuration/
│ ├── c4-architecture/
│ │ ├── agents/ # C4 architecture agents
│ │ │ ├── c4-code.md
│ │ │ ├── c4-component.md
│ │ │ ├── c4-container.md
│ │ │ └── c4-context.md
│ │ └── commands/
│ │ └── c4-architecture.md
│ └── ... (62 more isolated plugins)
├── docs/ # Documentation
│ ├── agent-skills.md # Agent Skills guide
│ ├── agents.md # Agent reference
│ ├── plugins.md # Plugin catalog
│ ├── usage.md # Usage guide
│ └── architecture.md # This file
└── README.md # Quick start
Each plugin contains:
- agents/ - Specialized agents for that domain (optional)
- commands/ - Tools and workflows specific to that plugin (optional)
- skills/ - Modular knowledge packages with progressive disclosure (optional)
- At least one agent OR one command
- Clear, focused purpose
- Proper frontmatter in all files
- Entry in marketplace.json
plugins/kubernetes-operations/
├── agents/
│ └── kubernetes-architect.md # K8s architecture and design
├── commands/
│ └── k8s-deploy.md # Deployment automation
└── skills/
├── k8s-manifest-generator/ # Manifest creation skill
├── helm-chart-scaffolding/ # Helm chart skill
├── gitops-workflow/ # GitOps automation skill
└── k8s-security-policies/ # Security policy skill
Skills use a three-tier architecture for token efficiency:
- Metadata (Frontmatter): Name and activation criteria (always loaded)
- Instructions: Core guidance and patterns (loaded when activated)
- Resources: Examples and templates (loaded on demand)
All skills follow the Agent Skills Specification:
---
name: skill-name # Required: hyphen-case
description: What the skill does. Use when [trigger]. # Required: < 1024 chars
---
# Skill content with progressive disclosure- Token Efficiency: Load only relevant knowledge when needed
- Specialized Expertise: Deep domain knowledge without bloat
- Clear Activation: Explicit triggers prevent unwanted invocation
- Composability: Mix and match skills across workflows
- Maintainability: Isolated updates don't affect other skills
See Agent Skills for complete details on the 107 skills.
The system uses Claude Opus and Sonnet models strategically:
| Model | Count | Use Case |
|---|---|---|
| Opus | 42 agents | Critical architecture, security, code review |
| Sonnet | 39 agents | Complex tasks, support with intelligence |
| Haiku | 18 agents | Fast operational tasks |
Haiku - Fast Execution & Deterministic Tasks
- Generating code from well-defined specifications
- Creating tests following established patterns
- Writing documentation with clear templates
- Executing infrastructure operations
- Performing database query optimization
- Handling customer support responses
- Processing SEO optimization tasks
- Managing deployment pipelines
Sonnet - Complex Reasoning & Architecture
- Designing system architecture
- Making technology selection decisions
- Performing security audits
- Reviewing code for architectural patterns
- Creating complex AI/ML pipelines
- Providing language-specific expertise
- Orchestrating multi-agent workflows
- Handling business-critical legal/HR matters
Combine models for optimal performance and cost:
Planning Phase (Sonnet) → Execution Phase (Haiku) → Review Phase (Sonnet)
Example:
backend-architect (Sonnet) designs API
↓
Generate endpoints (Haiku) implements spec
↓
test-automator (Haiku) creates tests
↓
code-reviewer (Sonnet) validates architecture
- Isolated plugins load only what you need
- Granular architecture reduces unnecessary context
- Progressive disclosure (skills) loads knowledge on demand
- Clear boundaries prevent context pollution
- 100% agent coverage - all plugins include at least one agent
- 100% component availability - all 99 agents accessible across plugins
- Efficient distribution - 3.4 components per plugin average
- Clear plugin names convey purpose immediately
- Logical categorization with 23 well-defined categories
- Searchable documentation with cross-references
- Easy to find the right tool for the job
Each plugin focuses on one domain:
python-development/
├── agents/ # Python language experts
├── commands/ # Python project scaffolding
└── skills/ # Python-specific knowledge
Benefits:
- Clear responsibility
- Easy to maintain
- Minimal token usage
- Composable with other plugins
Orchestrator plugins coordinate multiple agents:
full-stack-orchestration/
└── commands/
└── full-stack-feature.md # Coordinates 7+ agents
Orchestration:
- backend-architect (design API)
- database-architect (design schema)
- frontend-developer (build UI)
- test-automator (create tests)
- security-auditor (security review)
- deployment-engineer (CI/CD)
- observability-engineer (monitoring)
Agents provide reasoning, skills provide knowledge:
User: "Build FastAPI project with async patterns"
↓
fastapi-pro agent (orchestrates)
↓
fastapi-templates skill (provides patterns)
↓
python-scaffold command (generates project)
Complex workflows use multiple plugins:
Feature Development Workflow:
1. backend-development:feature-development
2. security-scanning:security-hardening
3. unit-testing:test-generate
4. code-review-ai:ai-review
5. cicd-automation:workflow-automate
6. observability-monitoring:monitor-setup
- Marketplace catalog in
.claude-plugin/marketplace.json - Semantic versioning for plugins
- Backward compatibility maintained
- Clear migration guides for breaking changes
- Individual plugin updates don't affect others
- Skills can be updated independently
- Agents can be added/removed without breaking workflows
- Commands maintain stable interfaces
- Create plugin directory:
plugins/{plugin-name}/ - Add agents and/or commands
- Optionally add skills
- Update marketplace.json
- Document in appropriate category
- Create
plugins/{plugin-name}/agents/{agent-name}.md - Add frontmatter (name, description, model)
- Write comprehensive system prompt
- Update plugin definition
- Create
plugins/{plugin-name}/skills/{skill-name}/SKILL.md - Add YAML frontmatter (name, description with "Use when")
- Write skill content with progressive disclosure
- Add to plugin's skills array in marketplace.json
- Clear naming - Hyphen-case, descriptive
- Focused scope - Single responsibility
- Complete documentation - What, when, how
- Tested functionality - Verify before committing
- Spec compliance - Follow Anthropic guidelines
- Agent Skills - Modular knowledge packages
- Agent Reference - Complete agent catalog
- Plugin Reference - All 67 plugins
- Usage Guide - Commands and workflows