How to share AI prompts across your team: every option compared

There are six realistic ways to share AI prompts across a team: a shared document, a vendor prompt gallery, a shared assistant, a developer prompt platform, admin-provisioned skills or a distribution layer that spans all of them. They differ on versioning, on approval before a prompt spreads and on reach beyond a single vendor. This guide compares each one, and shows where it stops working.

Sharing prompts is worth doing properly, because the prompt is one of the largest levers on output quality a company controls. One 2026 study found the choice of prompt explained 36.4% of the variance in output quality, nearly as much as the choice of model at 40.9%. Formatting choices alone can swing task accuracy by up to 76 percentage points. Yet in most companies the best prompts never travel. Ethan Mollick calls the people holding them secret cyborgs: employees quietly getting strong results from AI, with no reason and no obvious place to share what works.

Option 1: a shared prompt document

Every team starts here, and it is the right place to start. A Notion page or Google Doc costs nothing, takes minutes to set up, and captures know-how that would otherwise stay in personal chat histories. Notion's marketplace sells ready-made prompt template databases, which are the same thing with better formatting. Snippet tools such as TextBlaze or Raycast presets add hotkeys to the idea, and hit the same ceiling.

The limits arrive with scale, because a document is text with no machinery behind it. Nobody knows whether a prompt still works, which model it was written for, or which of three near-identical variants is the current one. Prompts also decay: a study of model updates found roughly one prompt in five regresses when the underlying model changes, and a document does not notice.

The signal that you have outgrown a document is simple: someone asks which prompt the team uses for a task and gets two different answers. If nobody owns a prompt, everyone is running their own version of it.

Option 2: a vendor prompt gallery (Microsoft Copilot)

Microsoft 365 Copilot ships the most complete built-in gallery. Prompt Gallery lives inside Copilot Chat (the standalone app was retired in July 2025), showing Microsoft's suggested prompts, your saved prompts, and prompts shared with a Teams team side by side. Admins can export reports of what users saved, liked and shared.

That is a genuine step up from a document. Prompts sit inside the tool where they run, and using a colleague's prompt is a click rather than a paste. But the gallery is a flat share: no versions, no rollback, no approval step before a prompt spreads and nothing that tells anyone which shared prompts actually work. It is also Copilot-only. A prompt shared here does not exist for the colleague working in ChatGPT or Claude.

Option 3: shared assistants (custom GPTs, Gemini Gems, Claude Projects)

A gallery shares a prompt's text. An assistant bakes it in. In ChatGPT Business and Enterprise, Projects carry files and instructions that apply to every chat inside them, and custom GPTs can be published to the whole workspace. Workspace agents, launched in April 2026, are positioned as the successor to both. Google's Gemini Gems gained sharing in September 2025, with Drive-style view and edit permissions. Claude Projects do the same job for teams on Claude.

This works, and adoption proves it: Moderna employees built 750 custom GPTs in two months. The same number shows the failure mode. Which of the 750 are good? Who maintains them when their author leaves? An assistant hides the prompt, so a colleague cannot break it, but they also cannot see it, improve it, or tell when it silently changes. And each assistant lives inside one vendor's walls.

Option 4: developer prompt platforms (PromptLayer, Langfuse, PromptHub)

Developer prompt platforms treat prompts as code: versioned, diffed, tested against evaluations, deployed and rolled back. For the prompts inside your software products, this is the correct discipline, and tools like PromptLayer, Langfuse and PromptHub do it well.

But they are engineering tools, priced and designed for teams shipping LLM features, not for a sales team that needs the good proposal prompt. The category has also been consolidating toward developers rather than expanding beyond them: Humanloop wound down in September 2025 after Anthropic hired its founding team, and Vellum repositioned around a personal AI assistant in May 2026. The versioning discipline is right. The audience is not your workforce.

Option 5: admin-provisioned skills

Skills are the first mechanism where a prompt travels to your workforce with a version number attached. A skill is a folder: instructions in a SKILL.md file, plus the reference files, examples and scripts the instructions depend on. The format was published as an open standard in December 2025 and is now read by more than 30 tools, ChatGPT, Copilot, Cursor and Gemini CLI among them.

Crucially, skills come with real distribution. Claude Team and Enterprise admins can provision skills for the whole organisation, enabled by default for every user. ChatGPT added Skills for work plans in July 2026, on by default for Enterprise workspaces from 23 July. The Claude API manages skill versions explicitly.

A shared document distributes text. A skill distributes a working method. That difference matters enough that we wrote a separate piece on it. Two caveats remain. Skill uploads do not sync across surfaces: a skill uploaded to the Claude API is not available in claude.ai or Claude Code, so org-wide still means per-surface housekeeping. And quality is still your problem, because a skill packages a bad method exactly as efficiently as a good one.

Option 6: a distribution layer above the vendors

The vendor options above all share within their own walls, and that stopped being sufficient the moment skills began running in ChatGPT, Claude and Copilot at once. Three admin panels, three uploads, three versions of the truth. Vendors are adding admin inventories (Atlassian's Rovo shipped an org-wide agent inventory in May 2026), but each covers only its own estate.

A distribution layer sits above the tools: one catalogue where capabilities are published once, versioned, approved before they spread, permitted per team and observed in use, whichever AI tool they run in. The trade is one more system to buy and run, and below a certain scale the overhead is not worth it. But inside each vendor's product, sharing is at least on someone's roadmap. Across vendors, it is on nobody's. Closing that gap is the same job an AI control plane exists to do.

How do the options compare?

Read down the last three columns and the shape is hard to miss. Every vendor-native option is strong inside its own walls and blank outside them, because sharing across vendors is not a feature any of them has a reason to build. The distribution layer is the only row without a gap, and the honest cost of that is the one named in its first column: it is another system to buy and run.

How do you choose?

Match the option to the stage you are at, not the stage you hope to reach.

1. Under ten regular AI users: use a shared document, and give it a named owner. The owner matters more than the tool.
2. Standardised on one vendor: use its native sharing. Options 2, 3 and 5 are really one choice: take whatever your vendor gives you. A prompt that lives where it runs beats a document.
3. A special case: prompts inside your product need a developer platform. Customer-facing prompts need evals and rollback, and nothing else provides them.
4. Multiple teams, multiple tools or compliance requirements: you need versions, approval and visibility across vendors, which is the point where teams start needing a distribution layer.

Most teams discover the fourth stage the same way: prompts and skills are spreading, but nobody can say which version is where, or whether the one that spread is any good.

That last stage is the one Connor is built for. Capabilities are prompts and skills published once, versioned, permitted per team, and observable in use, across the AI tools your company already runs.