MCP Marketplace Landscape: Comparing Skill Aggregators

The fragmented nature of the AI skill ecosystem presents both opportunity and challenge for developers. With over 34,000 skills scattered across a dozen platforms, the question of where to find, publish, and discover Claude Code capabilities has become critical.

This analysis provides a comprehensive comparison of the major skill aggregators and marketplaces, examining their approaches, strengths, and strategic positioning. Whether you're a skill creator deciding where to publish or a developer seeking the right capability, understanding this landscape is essential.

The Current State of Fragmentation

Before examining individual platforms, it's worth understanding why the market is so fragmented:

Organic growth patterns: Skills emerged from multiple sources—official Anthropic examples, community GitHub repos, third-party aggregators—each with their own discovery mechanisms.

Different platform philosophies: Some platforms prioritize quantity (aggregate everything), others prioritize quality (curate carefully). Neither approach has definitively won.

Evolving standards: The skill format has evolved rapidly, and different platforms support different versions and extensions of the core specification.

Distribution model uncertainty: Unlike mobile app stores with clear economic models, skill marketplaces are still experimenting with how value flows between creators, platforms, and users.

The result is a landscape where developers often check 3-5 different sources to find the right skill, and creators face decisions about where to publish for maximum reach.

Platform Comparison Matrix

Deep Dive: Major Platforms

SkillsMP.com

The aggregation giant

SkillsMP has pursued an aggressive aggregation strategy, crawling GitHub repositories to compile the largest collection of skills available. With 34,000+ listings across Claude, ChatGPT, and Codex, they've prioritized comprehensiveness over curation.

What they do well:

• Scale. Pure volume gives them the highest probability of having any given skill. If something exists, SkillsMP probably has it indexed.

• Multi-agent support. Unlike Claude-specific platforms, SkillsMP covers multiple AI assistants. Developers working across platforms find this valuable.

• AI-powered search. Their semantic search understands intent, not just keywords. Searching for something like "help me write tests" returns relevant results even if "test" isn't in the skill name.

• One-command installation. Their CLI tool reduces friction significantly: skillsmp install skill-name handles everything.

• Developer API. Programmatic access enables automation and integration with existing workflows.

Limitations:

• Quality variance. The 2+ star filter catches obvious duds, but many mediocre skills slip through. Discovery noise is high.

• No deep curation. You're on your own evaluating whether a skill is production-ready or abandoned.

• Claude dilution. Multi-agent focus means Claude-specific optimization isn't their priority.

• Attribution concerns. Some skill creators have raised questions about how their work is aggregated and represented.

Best for: Developers who want maximum choice and don't mind evaluating quality themselves. Power users who appreciate CLI installation.

Pricing: Free

Smithery.ai

The MCP infrastructure leader

Smithery occupies a related but distinct niche: MCP servers rather than Claude Code skills. The distinction matters—MCP servers provide external tool integrations (databases, APIs, file systems) while skills provide workflow automation and capabilities.

What they do well:

• Hosting infrastructure. Unlike other platforms, Smithery can host MCP servers, not just list them. This removes deployment friction entirely.

• Usage metrics. Real-time visibility into requests per minute, latency, and error rates helps developers assess reliability.

• Enterprise features. Production-grade infrastructure with SLAs, monitoring, and support.

• Developer experience. Comprehensive documentation, CLI tools, and API access.

Limitations:

• Different product category. If you need Claude Code skills (commands, agents, workflows), Smithery isn't the right platform. They solve a different problem.

• Complexity. MCP server deployment is more involved than skill installation. The target user is more technical.

• Pricing. Hosted servers incur costs. The freemium model means serious usage requires payment.

Best for: Developers building MCP integrations who want hosting infrastructure. Enterprise teams needing production-grade MCP deployment.

Pricing: Freemium (free local, paid hosted)

awesome-claude-skills (GitHub Collections)

The trusted open-source approach

Multiple GitHub repositories have emerged to curate Claude Code skills. The most notable include travisvn/awesome-claude-skills, ComposioHQ/awesome-claude-skills, and obra/superpowers.

What they do well:

• Developer trust. GitHub is familiar territory. Developers can inspect source code, review commit history, and fork freely.

• No platform lock-in. Skills are just files in repositories. No vendor dependency, no platform risk.

• Community maintenance. Popular repos have active contributors improving curation and adding new skills.

• Quality signals. Star counts, fork counts, and recent activity provide quality indicators native to the platform.

Limitations:

• Fragmentation. Skills are scattered across 10+ repositories. No single source of truth exists.

• Discovery friction. GitHub search is powerful but optimized for code, not skill discovery. Finding the right skill requires knowing where to look.

• No installation tooling. Manual copy-paste from repositories. No unified CLI or installation tracking.

• Maintenance uncertainty. Repository maintainers may lose interest. No platform ensuring ongoing curation.

Major repositories:

Best for: Developers who prefer Git workflows, want maximum transparency, and don't mind manual installation.

Pricing: Free

claudecodemarketplace.com

The professional plugin focus

This platform focuses on production-ready, professional-grade plugins rather than broad aggregation. Quality over quantity defines their approach.

What they do well:

• Production readiness. Skills are vetted for enterprise use cases. Security and compliance receive attention.

• Specialized agents. 91 security detection agents demonstrate deep expertise in specific domains.

• Enterprise focus. GDPR compliance, security review, and professional templates serve regulated industries.

Limitations:

• Limited scale. 30+ commands versus thousands elsewhere. If they don't have what you need, you're out of luck.

• Discovery gaps. Less developed search and filtering compared to larger platforms.

• Update frequency. Less clear how often new skills are added or existing ones updated.

Best for: Enterprise users prioritizing security and compliance. Organizations in regulated industries.

Pricing: Unknown (appears free)

Emerging Platforms

claude-plugins.dev

A newer entrant offering cross-platform compatibility (Claude, Cursor, Codex, VS Code). Their emphasis on "Agent Skills" that activate automatically based on context rather than explicit commands represents an interesting evolution.

claudecodeplugin.com

Another recent addition to the landscape. Less information available, but appears to offer curation and installation guides.

Official MCP Registry (registry.modelcontextprotocol.io)

Anthropic's official MCP server catalog. Focused on verified, official integrations rather than community contributions.

Market Dynamics and Trends

Quantity vs. Quality Trade-off

The fundamental tension in skill marketplaces is between comprehensive aggregation and quality curation:

Aggregation advantages:

• Higher probability of having any given skill
• Lower barrier to contribution
• Faster catalog growth
• API-friendly for automation

Curation advantages:

• Lower discovery noise
• Higher average skill quality
• Trust signals (verification, reviews)
• Better user experience

The market hasn't clearly decided which approach wins. Both models have active users and growing catalogs.

The Attribution Question

As aggregators scrape and repackage skills from GitHub, questions emerge about appropriate attribution:

• Should aggregators link to original sources?
• Do original creators benefit from aggregator traffic?
• Who owns the relationship with users?
• How should revenue (if any) be shared?

These questions remain largely unresolved. The open-source nature of most skills means legal restrictions are minimal, but community norms are still forming.

Installation Friction

The installation experience varies dramatically across platforms:

CLI-based (SkillsMP, Smithery): One-command installation reduces friction but requires CLI comfort.

Manual (GitHub, most marketplaces): Copy-paste from web interface. Higher friction but no tool dependencies.

Browser extension (proposed): Zero-friction installation from web. No implementations have gained traction yet.

Installation friction significantly impacts adoption. Skills requiring less friction see higher install counts, all else equal.

Discovery Challenges

Finding the right skill remains difficult:

Keyword search limitations: Skills with different names but similar functions are hard to connect.

Category pollution: Broad categories contain too many unrelated skills.

Quality signal gaps: Stars, installs, and reviews are inconsistent across platforms.

Freshness uncertainty: Distinguishing actively maintained skills from abandoned ones is difficult.

AI-powered semantic search (SkillsMP) addresses some issues but isn't universally available.

Comparative Analysis by Use Case

For Skill Creators

Maximize reach: Publish to GitHub and ensure major aggregators can index. SkillsMP will pick up public repos automatically.

Build reputation: Focus on curated platforms (AI Skill Market, claudecodemarketplace) where verification and attribution are emphasized.

Enterprise focus: Target platforms with enterprise users and compliance features.

Future monetization: Position on platforms planning paid marketplaces (AI Skill Market has announced plans).

For Individual Developers

Maximum choice: SkillsMP's 34,000+ skills offers the broadest selection.

Quality assurance: Curated platforms reduce discovery noise at the cost of breadth.

Easy installation: CLI-equipped platforms (SkillsMP, Smithery) minimize friction.

Transparency preference: GitHub repos offer full source visibility.

For Enterprise Teams

Compliance needs: claudecodemarketplace's enterprise focus may fit regulated environments.

Private deployment: Consider platforms offering private skill repositories (emerging feature).

Integration requirements: API access (SkillsMP, Smithery) enables workflow integration.

Support expectations: Evaluate support offerings before committing to a platform.

Strategic Implications

Platform Strategy Recommendations

For new entrants:

• Differentiate on quality rather than quantity (SkillsMP already won that race)
• Focus on specific verticals underserved by generalists
• Build installation tooling that reduces friction
• Establish clear value proposition for creators

For existing platforms:

• Invest in discovery experience (search, recommendations, curation)
• Build creator tools and attribution
• Develop enterprise features for monetization
• Consider strategic partnerships over pure competition

For Anthropic:

• The absence of an official first-party marketplace creates opportunity for third parties
• MCP Registry focuses on servers, not skills—a gap remains
• Future official marketplace would significantly disrupt current landscape
• Current ecosystem growth benefits Anthropic regardless of who captures value

User Strategy Recommendations

Developers should:

• Check multiple sources for important skills (fragmentation means no single source has everything)
• Evaluate skill quality before production use (stars and installs aren't sufficient)
• Consider installation friction in platform choice
• Track where high-quality skills tend to appear

Teams should:

• Standardize on preferred platforms to reduce cognitive load
• Build internal curation layers on top of public marketplaces
• Evaluate enterprise features before scaling adoption
• Plan for potential platform changes and lock-in risks

Future Landscape Predictions

Near-Term (2025)

1. Consolidation begins. Smaller platforms merge or are acquired. 2-3 major players emerge from the current fragmentation.

2. Monetization experiments. First paid skill marketplaces launch. Creator economics become clearer.

3. Quality signals improve. User reviews, usage metrics, and verification badges become standard.

4. Installation standardization. Common installation protocols reduce friction across platforms.

Medium-Term (2026)

1. Vertical specialization. Domain-specific marketplaces (legal, healthcare, finance) emerge with deep curation.

2. Enterprise tier differentiation. Clear free vs. paid tiers with team management and compliance features.

3. Creator tools mature. Analytics, monetization, and promotion tools for skill creators.

4. Anthropic official marketplace. High probability of official first-party offering, reshaping competitive dynamics.

Long-Term (2027+)

1. Winner-take-most dynamics. Network effects concentrate market around 1-2 dominant platforms.

2. Skills as credentials. Skill development portfolios become hiring signals.

3. Automated curation. AI-powered quality assessment replaces manual curation at scale.

4. Embedded distribution. Skills distributed through agent interfaces rather than standalone marketplaces.

Conclusion

The MCP marketplace landscape in 2025 is characterized by productive fragmentation. Multiple approaches—aggregation, curation, infrastructure provision—coexist because the market is large enough and still forming.

For developers, this fragmentation means checking multiple sources and accepting some discovery friction. For creators, it means strategic decisions about where to publish and how to build reputation.

The next 18-24 months will likely see consolidation and clearer winners emerge. Platforms that solve the quality-at-scale problem—maintaining curation standards while achieving broad coverage—will be best positioned.

Until then, the skill marketplace landscape remains a fascinating case study in how ecosystems form around new technological paradigms. The parallels to early mobile app stores, browser extension markets, and package manager registries are instructive—those markets eventually consolidated around clear leaders, and this one likely will too.

The question is which platforms will be the iOS and Android of AI skills, and which will be the Windows Phone and BlackBerry.

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