Vendor-agnostic alternative to Claude skills and the GPT Store (2026)

If your team has built up real know-how for getting an AI agent to do a job well, you eventually want to share it: hand it to a teammate, reuse it across projects, run it again next quarter without rebuilding it from scratch. Two of the most visible ways to do that today are Anthropic's Claude skills and OpenAI's GPT Store. Both are good at what they do. Both also share one hard limit: the thing you build lives inside one vendor's product and runs on one vendor's models. This guide lays out what each one is, where the single-vendor line sits, and what a vendor-agnostic, verified alternative looks like.

What Claude skills and the GPT Store actually are

Claude skills: packaged expertise for Claude

A Claude skill is a folder with a SKILL.md file at its root: YAML frontmatter (a name and a description) plus markdown instructions, and optionally scripts and reference files the agent loads only when it needs them. Anthropic calls the loading pattern progressive disclosure, and it is genuinely well-designed. The agent reads the lightweight description first and pulls in the full body only when a task matches, so you can install many skills without burning context. Skills work across Anthropic's surfaces: claude.ai, Claude Code, the Claude Agent SDK, and the Claude Developer Platform. In December 2025 Anthropic published the SKILL.md format as an open standard, so the packaging itself is documented for anyone to adopt.

What a skill does well is give Claude reliable, reusable context for a task. What it does not do is run on another company's model, or check its own result. A skill is instructions. It also stays close to Anthropic in practice: per Anthropic's own documentation, custom skills do not even sync across its surfaces, so a skill uploaded to claude.ai is separate from one uploaded through the API, which is separate again from the filesystem skills in Claude Code.

The GPT Store: custom GPTs inside ChatGPT

A custom GPT is a configured version of ChatGPT. You give it instructions, knowledge files, and a set of capabilities, no code required, and you can publish it to the GPT Store for other people to find and use. OpenAI rolled the store out in January 2024 to ChatGPT Plus, Team, and Enterprise users, and within two months of announcing GPTs, people had built more than three million of them. There is a real creator side too: a verified builder profile, a review process, and a usage-based earnings program. For reach and ease of use, it is hard to beat.

The catch is in the name. A GPT is a version of ChatGPT. It runs inside ChatGPT, on OpenAI's models. You cannot take a GPT and run it on Claude, point it at a different model when a better one ships, or drop it into your own pipeline. The work and the runtime are the same thing.

The single-vendor boundary

Here is the line both options share. A Claude skill is built for Claude and triggered by Claude across Anthropic's products. A custom GPT is built for ChatGPT and runs on OpenAI's models. Anthropic publishing the SKILL.md format as an open standard softens this for skills, since the file format is portable on paper, but the runtime that loads a skill, decides when to fire it, and ships the prebuilt skills is still Anthropic's, and a skill is a set of instructions either way.

Neither option checks whether the output actually met your standard. That is the part that bites when you share work with other people. A teammate runs your skill or your GPT, it does something plausible, and nobody knows whether it cleared the bar you cared about. The instructions traveled. The standard did not.

How to share AI agent skills across different models like Claude and ChatGPT

The fix is to stop treating the shared thing as a vendor feature and start treating it as an artifact you own. Author the work once, in a form any agent can run, and attach a check that travels with it.

That is what a Goodeye workflow is. A workflow is a markdown runbook (the instructions an agent follows for a task) paired with verifiers (checks that score the agent's output). It reaches the agent over three peer surfaces: a CLI, an MCP server, and a REST API. The same workflow runs whether your agent sits on Claude, on ChatGPT's models, or anywhere else, because Goodeye does not own the model. It owns the runbook and the checks. There is no GUI to log into; the workflow runs wherever your agent already runs.

A vendor-agnostic, verified alternative to Claude skills and the GPT Store

Portable is half of it. Verified is the half the stores skip.

A verifier is a check you author. It takes the agent's output and judges it against a criterion you wrote, the thing that actually defines a finished result for the task: the right tone, the correct figures, a format that validates, a claim that matches the source. It runs inside the agent's loop at generation time, so when the output misses, the agent revises and re-runs before you ever see it. You are not grading work after the fact and sending it back. The agent corrects itself against your standard first.

That changes what sharing means. When you share a workflow as a public template, the verifier travels with it, so whoever forks and runs your template gets work held to the same bar you set. Fork it into a private copy, tighten the verifier to your house rules, and the next run is measured against your standard automatically. Read how verifiers work, or how a workflow is structured, if you want to author your own.

This is the real difference between a store entry and a verified workflow. A Claude skill or a custom GPT tells an agent what to do. A workflow tells the agent what to do and checks that it did it, on whatever model you run.

Where this fits

Pick the single-vendor stores when their strengths match your need. If your team lives in ChatGPT and wants a no-code way to publish an assistant to a large audience, the GPT Store is built for exactly that. If you work inside Anthropic's products and want low-overhead, well-structured context for Claude, skills are a clean way to do it.

Reach for a vendor-agnostic workflow when you need the work to outlast a single model or vendor, to run the same way across the models your team actually uses, and to arrive already held to a standard instead of just described. For the full picture on sharing verified agent work across models and teams, start with the hub guide on sharing verified AI agent skills, then browse the public templates to fork one and make it meet your standard. If you are new to it, the getting started guide walks you through your first workflow.