Agent skills vs prompts vs custom GPTs — the honest comparison
Written by the maintainers of PM Skills — so read the conclusions knowing where we sit. The trade-offs against us are listed too. Updated 2026-07-26.
Should I use skills, a prompt library, or a custom GPT?
Use agent skills when quality must be repeatable across a team and across tools — they are versioned files that activate automatically and improve by pull request. A prompt library is enough for personal, occasional use with no consistency requirement. A custom GPT fits when your users live only in ChatGPT and you want a packaged assistant with bundled files and actions. The deciding factor: does the method need to outlive the person who wrote it?
| Prompt library | Custom GPT | Agent skills |
|---|
| Portability | any tool (paste) | ChatGPT only | any tool (file / MCP) |
| Auto-activation | no — you find & paste | within the GPT | yes, by description |
| Auditable content | yes | no — system prompt hidden | yes — it's the file |
| Versioning & review | rarely | owner-only edits | git, PRs, CI gates |
| Bundled tools/files | no | yes — actions, knowledge | deliberately no* |
| Works offline / local models | yes | no | yes |
*Skills can reference scripts and templates shipped alongside them, but the contract stays "instructions the model reads", which is what keeps them portable and auditable. If you need hosted actions with secrets, a GPT or an MCP server is the right tool — sometimes the answer isn't a skill.
What is the difference between PM Skills and the GPT Store?
The GPT Store distributes closed assistants that run only inside ChatGPT; PM Skills distributes open markdown files that run in Claude, ChatGPT, Gemini, Cursor, Codex, and any MCP client. A store GPT's system prompt cannot be read or edited by its users; every PM Skills file is MIT-licensed, human-readable, and security-scanned in CI. The Store's advantage is packaging — a GPT bundles files, actions, and branding in one shareable unit.
Pick the Store when your audience is ChatGPT-only consumers. Pick skills when your audience is professionals who switch tools, need to audit what instructs their AI, or want to fork the method.
How is a skills library different from an awesome list?
An awesome list curates links to other people's tools; the list owner vouches for relevance, not quality, and every entry has a different standard. A skills library maintains the content itself behind one bar — in this library's case: one structure standard enforced in CI, a security scan on every file, published eval scores, and one-command install. Lists solve discovery; libraries solve trust and consistency.
They compose: this library is itself listed in several awesome lists, and its community registry gives independent authors the library treatment — structural validation and a security scan — while their skill stays in their repo.
Where PM Skills is the wrong choice
Skip skills when you need hosted actions with secrets (build an MCP server or GPT), when your task is one-off and generic (just prompt), when you need retrieval over private documents (that's RAG, not instructions), or when your organisation requires vendor support contracts — this is an MIT project with community support and a small paid services tier, not an enterprise vendor.
▶ Try the playground · What are agent skills? · GitHub