SkillReg vs Git Repos for Managing AI Agent Skills
Git is the backbone of modern software development. It handles version control, collaboration, and code review better than any other tool. But when it comes to managing AI agent skills — structured instruction files like SKILL.md — Git repositories alone leave critical gaps. This page compares using Git repos (GitHub, GitLab, Bitbucket) to manage your team's AI skills versus using SkillReg, a purpose-built private registry for SKILL.md files.
What Git Does Well
Before diving into limitations, it's worth acknowledging where Git genuinely excels.
Version history. Git tracks every change to every file. You can see who modified a skill, when, and why. Blame, diff, and log are powerful tools for understanding how a skill evolved over time.
Collaboration via pull requests. PRs give teams a structured way to propose, review, and approve changes. For skills that affect production agent behavior, having a review gate is essential — and Git does this well.
Branching and experimentation. Want to test a new version of a deployment skill without affecting the main branch? Git branches make this trivial. You can iterate on a skill in isolation and merge when ready.
Ecosystem and integrations. Git integrates with CI/CD pipelines, code editors, and virtually every development tool. Most engineering teams already have Git workflows in place.
These are real strengths, and SkillReg doesn't try to replace them.
What Git Doesn't Do for Skills
Git was designed to version source code. AI agent skills are not ordinary source code — they are executable instructions that govern what an autonomous agent can and cannot do. This distinction creates gaps that Git alone cannot fill.
No Security Scanning of Skill Content
A SKILL.md file might instruct an agent to run shell commands, access APIs, or modify infrastructure. Git treats it as a text file. It won't flag a skill that contains rm -rf /, requests overly broad permissions, or references hardcoded secrets in its instructions. SkillReg scans skill content for security risks before the skill is available for installation.
No Usage Analytics
Who is using a specific skill? How often? Which version? Git has no concept of consumption. It tracks who committed changes, not who pulled and used a skill in production. SkillReg provides download counts, version adoption rates, and usage patterns per skill.
No Granular Access Control Per Skill
Git repos have repository-level permissions. Either you have access to the repo, or you don't. If your team stores 200 skills in a single repository, you cannot restrict an intern to only the code-review skill while giving senior engineers access to deploy-to-production. SkillReg supports scoped access control at the individual skill level.
No Discovery by Tags, Agent, or Use Case
Finding a skill in a Git repo means navigating directories, searching file names, or relying on a README that someone hopefully kept up to date. SkillReg lets you search and filter skills by tags, compatible agent (claude-code, codex, cursor), author, and use case — the way a package registry should work.
No Cross-Agent Install via CLI
To use a skill from a Git repo, you clone the repo, navigate to the right directory, and manually copy or symlink the file. SkillReg provides a single command: skillreg pull @org/skill-name installs the latest compatible version directly into your agent's workspace.
Feature Comparison
| Capability | Git Repos | SkillReg |
|---|---|---|
| Version history | Full git log and diff | Semantic versioning with immutable releases |
| Collaboration and review | Pull requests, code review | Git-based workflow upstream, registry as distribution |
| Branching | Native branching and merging | Not applicable (registry is the publish target) |
| Security scanning | None for skill content | Automated scan on every push |
| Access control | Repository-level | Per-skill and per-scope tokens |
| Usage analytics | Not available | Downloads, version adoption, usage trends |
| Discovery and search | File browser, grep | Tags, agent compatibility, full-text search |
| CLI install | Manual clone and copy | skillreg pull @org/skill-name |
| Dependency resolution | Not built-in | Automatic resolution on install |
| Audit log | Git log (commits only) | Full audit trail (publishes, pulls, access changes) |
| Offline access | Full local clone | Cached after first pull |
The Monolithic Skills Repo Problem
The most common pattern teams adopt is a single Git repository for all their skills: acme-corp/ai-skills. It starts simple — a handful of SKILL.md files in a flat directory. Then it grows.
At 50 skills, you need subdirectories. At 100, you need a naming convention. At 200, new team members spend more time searching for the right skill than using it. PRs become noisy because unrelated skills are reviewed in the same context.
Worse, access control becomes all-or-nothing. The repository contains both low-risk skills (formatting, linting) and high-risk skills (deployment, database migrations). Everyone with repo access can read and modify all of them.
A monorepo for skills creates the same problems that monorepos create for microservices — tight coupling, broad blast radius, and scaling friction — without the tooling (like Bazel or Nx) that makes code monorepos manageable.
SkillReg treats each skill as an independent, scoped package. Skills are published, versioned, and permissioned individually. Teams can own their skills without inheriting access to unrelated ones. You get the isolation of many repos with the convenience of a single registry.
SkillReg + Git: They're Complementary
SkillReg is not a replacement for Git. It is a distribution layer that sits downstream of your existing Git workflow. Git remains your version control system and collaboration tool. SkillReg becomes the place where reviewed, approved skills are published and consumed.
The recommended workflow looks like this:
git push origin feature/update-deploy-skill
|
v
Pull Request (review, approve, merge)
|
v
CI Pipeline triggers
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v
skillreg push ./skills/deploy-to-prod/SKILL.md
|
v
SkillReg validates, scans, and publishes
|
v
Team members run: skillreg pull @acme/deploy-to-prod
Step 1: Author in Git. Write and iterate on your SKILL.md files in a Git repository. Use branches, PRs, and code review exactly as you do for application code.
Step 2: Publish via CI. When a PR is merged, your CI pipeline runs skillreg push to publish the updated skill to SkillReg. The registry validates the SKILL.md format, runs security scans, and creates an immutable versioned release.
Step 3: Consume from SkillReg. Team members and agents pull skills from the registry using skillreg pull. They get the latest approved version with proper access control, without needing access to the source repository.
This mirrors how the software industry already works: source code lives in Git, but packages are distributed through registries (npm, PyPI, Docker Hub). SkillReg applies the same model to AI agent skills.
Why This Split Matters
Keeping authoring in Git and distribution in SkillReg gives you the best of both worlds:
- Review gates stay in Git. No skill reaches the registry without passing your PR review process.
- Security scanning happens at publish time. Even if a risky instruction slips through review, SkillReg catches it before distribution.
- Access control is decoupled from source access. A contractor can pull specific skills from SkillReg without having access to your Git repositories.
- Analytics track consumption, not just commits. You know which skills are actually being used, not just which ones were recently modified.
Ready to set up the workflow? Follow the getting started guide to configure your first skillreg push from CI. You can also see how SkillReg compares to managing skills in Notion and Google Docs.