8 Best Skill Registry Platforms for AI Agents

The same deployment skill can exist in a dozen versions inside one company. One lives in a .cursor/skills folder, another was uploaded to Claude as a zip, and a third was copied from a public repo and edited by an engineer who has since changed teams. Each version tells an agent something slightly different about how production changes should happen, and none of them has an owner. Agent skills spread faster than any team can review them, and they shape agent behavior more directly than most code does. The eight platforms below each bring them under control in a different way.

Why Agent Skills Need a Skill Registry

An agent skill is a packaged set of instructions, often with scripts and reference files, that teaches an AI agent how an organization handles a specific task. When a developer asks an agent to triage an incident or prepare a release, the agent loads the matching skill and follows its steps instead of improvising. Agent Skills is an open format, so one skill can run in Claude Code, Cursor, Copilot and many other agents, and anyone can write, copy or install one in seconds.

That ease is also the problem. Without a registry, nobody knows which skills exist, which version is current, or whether a downloaded skill hides risky instructions. Duplicate skills confuse agents, security can't review what it can't see, and leaders can't tell which skills actually save time. A skill registry turns that sprawl into a managed library: skills are published through one path, checked for quality and safety, delivered from a single source, and measured by use and cost. The platforms below differ in how much of that lifecycle they cover, from full governance to open sharing.

The 8 Best Skill Registry Platforms for AI Agents

1. Port

Port is the best choice for organizations that want skills governed as part of the engineering platform rather than kept in a folder of prompts. In Port, skills are entities in the software catalog, and each has an owner, a group and role-based permissions. Port's MCP server delivers them to Cursor, Claude, VS Code and other MCP-compatible agents through one connection. Git ingestion keeps them in sync with GitHub and GitLab.

Publishing follows one golden path. A developer submits a skill, an AI step extracts its metadata, and Port opens a pull request that reviewers approve from the Skills Lifecycle Control dashboard. A similarity check flags near-duplicates before anything is published, and readiness scorecards rate each skill from Gold to Basic.

Port also ties every skill to cost and outcomes. Usage and spend data give a cost per run, and an ROI scorecard shows which skills to standardize or retire. Because the Skills registry sits beside Port's Agent and MCP registries, teams govern skills, agents and MCP servers from one place.

Key features

• Skills as catalog entities with owners and role-based permissions
• Golden-path publishing with auto PRs and approval dashboards
• Duplicate detection and readiness scorecards
• Cost per run, ROI scorecards and adoption analytics
• Distribution to any MCP-compatible agent
• Agent Plugins plus Agent and MCP registries

2. JFrog Skills Registry

JFrog's Skills Registry treats skills as software artifacts. Each skill is a versioned package stored in Artifactory, and every publish creates an immutable snapshot with a clear rollback path.

Security is JFrog's strength. Xray scans every skill and blocks suspicious or malicious ones from being installed, and project policies control which skills may run. Skills can be signed, and teams publish and install them through the JFrog CLI.

Key features

• Versioned, immutable skill packages
• Xray scanning before install
• Project policies and skill signing
• CLI support for Claude Code, Cursor and VS Code

3. Tessl Registry

The Tessl Registry gives teams one searchable index of skills across the org and their private workspaces, so developers can check for an existing skill before writing a new one.

Evaluation is at the center of the product. Teams run before-and-after evals on every skill change and benchmark models and agent harnesses against their own tasks. Every PR gets a security scan, and the registry shows security scores powered by Snyk.

Key features

• Org-wide searchable skill index
• Evals on every skill change
• Model and harness benchmarking
• Security scans and Snyk-powered scores

4. Google Gemini Enterprise Agent Platform Skill Registry

Google's Skill Registry is a private repository for agent skills within Gemini Enterprise Agent Platform. Each skill gets immutable revisions with a default version, and uploads are validated automatically.

Agents can search the registry and load the skills that match a user's request. The registry is still in Preview, so it fits teams already building on Google Cloud's agent stack.

Key features

• Private, low-latency skill repository
• Immutable revisions
• Automatic package validation
• Intent-based discovery for agents

5. Microsoft Foundry Private Skill Catalogs

Microsoft Foundry's private skill catalogs, in public preview since August 2026, register skills in Azure API Center so developers in Foundry projects can find them. Git sync keeps the catalog in line with source code.

Admins register skills and define allowed tools, which limit the APIs and MCP servers each skill can reach. Developers browse the catalog with read-only access. Because the feature is in preview, it suits Azure teams testing skill governance.

Key features

• Skill registration in Azure API Center
• Git sync with source repos
• Allowed tools lists
• Optional AI assessments

6. Anthropic Agent Skills for Claude

Anthropic created the open Agent Skills format. Its Skills API distributes custom skills across a Claude workspace, and version pinning keeps production behavior stable.

Claude Enterprise admins can turn on skill and plugin security scanning, which blocks skills that fail. Custom skills don't sync between Claude's API, claude.ai and Claude Code, so teams working across several of these need their own registry.

Key features

• Creator of the Agent Skills format
• Workspace-wide distribution via the Skills API
• Version pinning
• Enterprise security scanning

7. GitHub Copilot Agent Skills

With GitHub Copilot, teams keep skills in their own repositories, for example in .github/skills, and in personal folders. Skills work across Copilot cloud agent, code review, CLI and IDE agent mode.

Developers find and install skills with gh skill, and every change goes through normal pull request review. GitHub has no central registry, approval flow or usage analytics, so larger orgs often pair it with a dedicated registry.

Key features

• Repository and personal skills
• Support across Copilot surfaces
• gh skill install command
• Git-based review and history

8. Vercel Skills and skills.sh

Vercel's skills CLI installs skills with a single npx skills add command. It works with more than 15 coding agents, including Claude Code, Cursor, Codex and Copilot.

skills.sh is a public directory and leaderboard where developers can browse skills by popularity. It's built for open sharing, not enterprise control, so teams usually vet skills they find there before adopting them.

Key features

• One-command installs
• Support for 15+ coding agents
• Public directory and rankings
• Install counts across the ecosystem

Skill Registry Platforms Compared

Signs Your Team Has Outgrown Ad Hoc Skill Management

Nobody sets out to buy a skill registry. The need usually shows up after an agent confidently follows a rollback process that was retired in the spring.

If any of these sound familiar, you’re probably already there:

• Two engineers ask the same agent the same question and get different answers
• Nobody knows who owns the skill that handles production deploys
• New skills spread through Slack as zip files and land on five laptops by Friday
• Security finds out about a skill only after it has run
• Half the skills in your repos might be in use, or might be dead weight, and no one can tell

At that point, skills aren’t side projects anymore. They’re shared infrastructure, and they need an owner, a review step and usage data like everything else your agents depend on.

Frequently Asked Questions

Can a skill registry track AI spend?

Some can. When usage and token data are linked to each skill, teams can see what every skill costs to run and compare that against the time it saves. Without that link, AI spend shows up as one large bill with no clear view of which skills are worth keeping.

How do agent skills differ from MCP servers?

Skills tell an agent how to carry out a task, through instructions, steps and references. MCP servers give the agent access to tools and data, such as a ticketing system or a database. Most workflows use both: the skill describes the process, and MCP servers supply the systems the agent works with.

Should companies use public skills from open directories?

They can, but only after review. Public skills are a quick way to start, but they may contain hidden instructions, scripts or network calls. Treat them like open-source dependencies. Read every file, test them in isolation, and publish an approved copy to your internal registry instead of installing straight from the internet.

How often should agent skills be reviewed?

Review a skill whenever the process, tool or system it describes changes, and audit the whole library at least quarterly. Skills that rarely trigger, overlap with others, or point to outdated runbooks should be updated, merged or retired so agents don't act on stale guidance.

Who should approve new skills for the organization?

The team that owns the underlying process should approve a skill's content, such as the SRE team for an incident skill or platform engineering for a deployment skill. Security should review any skill that runs scripts, makes network calls or references external tools. The author should never be the only approver.