SkillReg vs Public AI Skill Marketplaces
The ecosystem for AI agent skills is splitting into two distinct models. Public marketplaces like Smithery and Glama make it easy to discover and share skills openly. Private registries like SkillReg give teams full control over which skills their agents can use, who can publish them, and how they're governed. Both models serve different needs, and understanding where each fits is essential before your team builds its skill management stack.
This guide compares the two approaches and explains when you need one, the other, or both.
What Are Public Skill Marketplaces?
Public AI skill marketplaces are open platforms where developers publish and discover skills, MCP servers, and agent tooling. The most prominent examples include:
- Smithery -- A public registry focused on MCP server discovery. Developers publish servers and other users can browse, install, and use them immediately.
- Glama -- An aggregation platform that indexes publicly available MCP servers and AI tools, making discovery easier across fragmented sources.
- Other directories -- A growing number of community-maintained lists and registries that catalog open-source agent tooling.
These platforms provide real value. They lower the barrier to entry, accelerate experimentation, and let the community collectively build a shared library of agent capabilities. For individual developers and small teams exploring what AI agents can do, public marketplaces are an excellent starting point.
However, public marketplaces are designed for open discovery, not governed usage. Every skill published is visible to everyone. There are no approval workflows, no access scoping, and limited (if any) security review. That trade-off is acceptable for open-source tooling but becomes a problem when organizations need to control what their agents execute.
What Is a Private Skill Registry?
A private skill registry is an internal platform where teams publish, version, scan, and govern AI agent skills within their organization. SkillReg is built specifically for this purpose.
In a private registry:
- Skills are private by default. Nothing is visible outside your organization unless you explicitly share it.
- Every version is scanned for dangerous patterns, secret exposure, and shell injection before it becomes available for installation.
- Access is scoped. Teams control who can publish, who can install, and which skills are approved for production use.
- Every action is audited. Publishes, installs, permission changes, and scan results are logged for compliance and incident response.
Where public marketplaces answer "what skills exist?", a private registry answers "what skills are our agents allowed to use, and who approved them?"
Feature Comparison
| Feature | Public Marketplaces (Smithery, Glama) | SkillReg (Private Registry) |
|---|---|---|
| Access control | Open to all; anyone can publish or install | Scope-based permissions (owner, maintainer, reader) |
| Security scanning | None or minimal community review | Automated scanning on every push (dangerous patterns, secrets, network calls) |
| Versioning | Varies; some support versions, others show latest only | Immutable semver versions with pinning support |
| Audit logs | Not available | Full audit trail (publishes, installs, permission changes) |
| Usage analytics | Basic download counts | Per-skill, per-team install and usage tracking |
| SSO / Identity | GitHub OAuth at most | SSO integration with your identity provider |
| Approval workflows | None; publish and it's live | Configurable review and approval before skills go live |
| Pricing model | Free (ad-supported or community-funded) | Subscription per seat; free tier available |
| Skill visibility | Public by default | Private by default |
| Compliance support | None | SOC 2 and ISO 27001 audit trail support |
Neither model is objectively better. They serve different segments of the adoption curve. The comparison above should make it clear where each model fits.
Why Enterprises Cannot Rely on Public Marketplaces Alone
For organizations building production AI agent workflows, public marketplaces introduce three categories of risk that are difficult to mitigate externally.
Intellectual Property Leakage
Enterprise skills often encode proprietary logic -- deployment procedures, internal API orchestration, domain-specific reasoning patterns. Publishing these to a public marketplace exposes that logic to competitors and the broader internet. Even "unlisted" skills on public platforms are typically accessible via direct URL or API.
A private registry keeps proprietary skills within your organization's boundary. Skills are invisible to anyone outside your access scopes.
No Governance Layer
Public marketplaces have no mechanism to enforce which skills your team can or cannot use. Any developer can install any skill from the marketplace and wire it into an agent. This creates a shadow-IT problem specific to AI agents: you don't know what capabilities your agents have, who gave them those capabilities, or when it happened.
SkillReg provides approval workflows that ensure every skill used in production has been reviewed and authorized. Teams can enforce policies like "only skills from the @platform-team scope are allowed in CI/CD agents" without relying on individual developer discipline.
Compliance Gaps
Regulated industries require audit trails for any system that touches customer data or production infrastructure. AI agents executing skills fall squarely into this category. Public marketplaces provide no audit logging, no access records, and no way to demonstrate compliance to auditors.
SkillReg logs every publish, install, and permission change with timestamps and user identity. This audit trail maps directly to SOC 2 and ISO 27001 control requirements.
The Hybrid Model: Use Both
The strongest approach for most teams is not choosing one model over the other. It is using both, with clear boundaries between discovery and production use.
How the Hybrid Model Works
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Discover skills on public marketplaces. Use Smithery, Glama, and community directories to find skills that solve the problem you're facing. Evaluate them in a sandbox environment.
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Fork and adapt internally. When you find a skill worth using, fork it into your private SkillReg instance. Adapt it to your organization's conventions, remove unnecessary capabilities, and add guardrails specific to your environment.
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Scan and review. SkillReg automatically scans the forked skill on push. Your team reviews the scan results and approves the skill for production use through your normal approval workflow.
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Pin and deploy. Install the skill from SkillReg with version pinning. Your agents now use a reviewed, scanned, governed copy of the skill rather than pulling directly from a public source.
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Track upstream changes. When the original public skill updates, your team can review the diff and decide whether to merge changes into your private fork.
This model gives you the best of both worlds: the breadth of public discovery combined with the governance of private deployment. Your developers still benefit from the community's work, but your production agents never execute unreviewed code.
When to Skip the Hybrid Model
- Early-stage experimentation. If you're a small team exploring AI agents and haven't reached production use, public marketplaces alone may be sufficient. You can adopt a private registry later as your usage matures.
- Fully custom skills only. If your organization builds all skills internally and never uses community-developed ones, you don't need the discovery layer. SkillReg alone covers your needs.
Choosing the Right Approach for Your Team
The decision depends on where your organization sits on the AI agent adoption curve:
- Exploring and prototyping -- Public marketplaces are a great starting point. Low friction, broad selection, zero cost.
- Moving to production -- You need governance. A private registry like SkillReg ensures your agents only execute approved, scanned skills.
- Scaling across teams -- The hybrid model lets you leverage community innovation while maintaining enterprise-grade controls.
For a broader comparison of all the tools in this space, see Best AI Agent Skill Management Tools in 2026. To understand how SkillReg's security model works in detail, read How SkillReg Secures Your AI Agent Workflows.
SkillReg is not a replacement for public marketplaces. It is the governance layer that makes it safe to use skills in production -- whether those skills come from public sources or from your own team. Get started with SkillReg and bring your AI agent skills under control.