SkillReg vs Notion & Google Docs for AI Skill Management
When teams start creating AI agent skills, the first instinct is to store them where everything else lives: Notion, Google Docs, or Confluence. It makes sense. These tools are already adopted, they support rich formatting, and everyone knows how to use them. But as the number of skills grows and multiple agents need to consume them reliably, document-based sharing introduces friction that a purpose-built registry eliminates.
This page compares the document-sharing approach to SkillReg, a private registry designed specifically for managing SKILL.md files across teams and agents.
How Teams Use Notion & Docs for AI Skills
The typical workflow looks like this: someone writes a useful prompt or instruction set for Claude Code or Cursor, pastes it into a Notion page or a Google Doc, and shares the link in Slack. Teammates duplicate the page, tweak it for their use case, and the cycle repeats.
Some teams go further. They create a dedicated Notion database with properties for agent type, category, and status. They use Google Docs folders organized by team or project. Confluence users embed skills inside runbook pages alongside deployment procedures.
This works. For a while. The problems only surface when the team scales, when compliance requirements appear, or when agents need to pull skills programmatically rather than through copy-paste.
Feature Comparison
| Capability | Notion / Google Docs / Confluence | SkillReg |
|---|---|---|
| Versioning | Manual (page history, no semver) | Automatic semantic versioning with immutable versions |
| Access control | Workspace-level or link sharing | Scope-based access per skill, team, or organization |
| Security scanning | None | Automated scanning on every publish for dangerous patterns |
| Analytics | Basic page views | Pull counts, version adoption, usage trends per skill |
| Cross-agent compatibility | No structure enforcement | SKILL.md format parsed by Claude Code, Codex, Cursor, Windsurf |
| Format standard | Free-form text | Standardized SKILL.md with frontmatter and structured body |
| CLI / CI-CD support | API exists but not skill-aware | Native CLI with push, pull, search commands |
| Search & discovery | Workspace search (text-based) | Skill-specific search with tags, scopes, agent filters |
Five Problems with Document-Based Skill Sharing
1. Version drift becomes invisible
Notion page history tracks edits, but there is no concept of a release. When someone improves a skill, there is no way to notify consumers, no way to pin a specific version, and no way to diff version 1.2 against version 1.3. Teams end up running different versions of the same skill without realizing it.
In practice: one developer updates the "deploy-to-staging" instructions in a Google Doc. Three teammates continue using the cached version in their agent configuration. The deployment skill now behaves differently depending on who runs it, and nobody knows why.
2. No programmatic access for agents
AI agents cannot browse Notion. They cannot open a Google Doc link and parse its contents. To feed a document-stored skill to an agent, someone must manually copy the text, paste it into a CLAUDE.md file or a prompt, and keep it synchronized by hand.
SkillReg solves this with skillreg pull @team/deploy-to-staging, which fetches the latest (or pinned) version directly into the agent's context.
3. Security is an afterthought
Documents have no concept of what is safe to execute. A skill stored in Notion might instruct an agent to curl an external endpoint, set environment variables, or modify system files. Nobody reviews these instructions with the same rigor applied to code.
SkillReg scans every published skill for dangerous patterns -- secret exfiltration, unrestricted file access, network calls to unknown hosts -- before it enters the registry.
4. Discovery does not scale
With 10 skills in a Notion workspace, search works fine. With 200 skills across six teams, finding the right skill for a specific agent and use case becomes a needle-in-a-haystack problem. Notion's search is text-based and workspace-scoped. It does not understand agent compatibility, skill categories, or usage popularity.
5. No governance trail
Regulated industries need to answer questions like: who published this skill? Who has access? When was it last reviewed? Which agents are using it? Document platforms track page edits but provide no governance model for executable instructions that affect production systems.
When Notion or Google Docs Is Enough
Document-based skill sharing is a reasonable approach in several situations:
- Small teams (2-5 people) where everyone sits in the same Slack channel and verbal coordination fills the gaps that tooling leaves open.
- Exploration phase where you are still figuring out what skills to create. Writing them in a doc is faster than setting up a registry, and iteration speed matters more than governance.
- Single-agent setups where only one type of agent (e.g., Claude Code) is used, and skills are stored directly in project-level CLAUDE.md files rather than shared across repos.
- Non-sensitive workloads where skill instructions do not touch production infrastructure, credentials, or customer data.
If your team matches these criteria, there is nothing wrong with using Notion or Google Docs. Start there, and migrate when the pain points described above start affecting your workflow.
When You Need a Dedicated Registry
The inflection point usually arrives when one or more of these conditions become true:
- Team size exceeds 5 engineers. Coordination overhead grows nonlinearly. You need a single source of truth that agents can pull from, not a wiki that humans must remember to update.
- Multiple agents are in use. When Claude Code, Codex, and Cursor all need the same skill, a standardized format and a central registry eliminate translation work. See how SkillReg compares to Git repos for the version-control angle.
- Compliance or security requirements exist. SOC 2, ISO 27001, or internal security policies demand audit trails, access controls, and automated scanning. Documents do not provide these out of the box.
- Skills affect production systems. If a skill can trigger deployments, modify databases, or interact with external APIs, you need the same rigor applied to skill management that you apply to code deployment.
- Skill reuse across projects. When the same skill is useful in multiple repositories, copy-pasting between docs leads to divergence. A registry with scoped packages keeps skills consistent.
Migrating from Docs to SkillReg
Moving from document-based skill sharing to SkillReg is straightforward because skills are already written in natural language. The main work is adding structure.
Step 1: Inventory your existing skills. Search your Notion workspace, Google Drive, or Confluence for pages that contain agent instructions. Tag each with its owner, the agents it targets, and how often it is used.
Step 2: Convert to SKILL.md format. For each skill, create a SKILL.md file with the standard frontmatter (name, version, description, agents) and move the instructions into the structured body. Most conversions take under 10 minutes per skill.
Step 3: Install the SkillReg CLI. Follow the getting started guide to install the CLI, authenticate, and set up your organization scope.
Step 4: Publish your skills. Run skillreg push for each skill. The registry validates the format, runs security scanning, and assigns the initial version.
Step 5: Update agent configurations. Replace hardcoded skill text in your CLAUDE.md or agent configs with skillreg pull commands. This ensures agents always fetch the latest approved version.
Step 6: Archive the docs. Once skills are live in the registry, archive the original Notion pages or Google Docs. Keep them as read-only references for the transition period, then remove them to avoid confusion.
Most teams complete the migration in a single afternoon for their first 10-20 skills. The time investment pays back immediately through reduced duplication and clearer ownership.