# Custom GPTs vs. Claude Skills: when to use which by John Morabito · April 18, 2026 · 9 min read **TL;DR** - Custom GPTs win for public-facing use, GPT Store distribution, and teams already on ChatGPT. - Claude Skills win for internal ops, Claude Code integration, code-heavy workflows, and MCP pipelines. - If your team lives in Claude Code, build Skills. If they live in ChatGPT, build GPTs. If elsewhere, pick by integration surface. ## What each actually is **Custom GPT.** OpenAI's packaging format for a task-specific assistant inside ChatGPT. Wraps a system prompt, optional knowledge files, and optional Actions (HTTP calls via OpenAPI) behind a named assistant. Distribution runs through the GPT Store. **Claude Skill.** Anthropic's packaging format for a self-contained capability. A directory with SKILL.md, optional scripts, optional references, and optional MCP server bindings. Runs inside Claude Code, the Claude Agent SDK, Claude.ai, or any harness that loads them. ## What are Claude Skills? Claude Skills are Anthropic's packaging format for a reusable, self-contained capability you load into Claude. A Skill is a folder: a SKILL.md instructions file plus optional scripts, reference files, and MCP server bindings. Claude reads the Skill's description and invokes it automatically when a task matches, then follows the instructions and runs any scripts. Because a Skill is just files, it is portable and version-controlled, so the same Skill runs in Claude Code, the Claude Agent SDK, Claude.ai, or any harness that loads it, and a team can share one identical copy. That portability is the main thing that separates a Skill from a Custom GPT, which lives only inside ChatGPT. ## Claude Projects vs Custom GPTs: where Projects fit People searching for "Claude Projects vs Custom GPT" are usually conflating two different Claude things. There are three primitives here, not two, and they sit at different levels of weight and portability. - **Custom GPT.** OpenAI's packaged assistant. A named system prompt plus knowledge files plus Actions, living inside ChatGPT. - **Claude Skill.** A portable, versioned capability. A SKILL.md plus references and scripts that any Claude runtime can load. This is the true Custom GPT equivalent. - **Claude Project.** The lighter in-app workspace inside Claude.ai. A persistent set of instructions, uploaded files, and chat history scoped to one workspace, with no folder to version-control and nothing to ship outside the app. The cleanest way to map it: a Claude Project is closest in feel to a Custom GPT, because both are configured in a UI and both keep their knowledge inside the host app. A Claude Skill is the more capable, portable layer underneath. So when someone asks how a Project compares to a Custom GPT, the honest answer is that they are cousins, while the Skill is the thing that actually competes on capability. Pick by where the work lives and how far it needs to travel. - **Use a Claude Project** when one person or a small team works inside Claude.ai and wants persistent context (a style guide, a few reference docs, ongoing chats) without standing up a file-based Skill. It is the fastest path to a reusable setup, and it is the right answer for "I just want Claude to remember my context across chats." - **Use a Claude Skill** when the same capability needs to run outside the app, get version-controlled, call scripts or MCP servers, or be shared identically across a team. A Project cannot do any of that. A Skill can. - **Use a Custom GPT** when your audience already lives in ChatGPT and you want GPT Store distribution or a no-code authoring surface for non-engineers. The practical migration path tends to run Project to Skill, not the other way. Teams prototype context inside a Claude Project, decide it needs to be portable and repeatable, and promote the instructions and files into a SKILL.md with a references folder. The Project was the sketch. The Skill is the shipped version. ## The decision table | Dimension | Custom GPT | Claude Skill | |---|---|---| | Best for | Public-facing, consumer | Internal ops, code workflows | | Deployment surface | ChatGPT | Claude Code, Agent SDK, Claude.ai | | Customization depth | Prompt + knowledge + Actions | Full directory, scripts, MCP | | Code execution | Sandboxed Python | Arbitrary shell, local files | | External tools | Actions (REST via OpenAPI) | MCP servers | | Cost model | Bundled in ChatGPT seat | Pay-per-token | | Ecosystem | Older, larger | Newer, engineering-heavy | | Privacy | OpenAI infra by default | Anthropic / Bedrock / Vertex | ## When to build a Custom GPT 1. Public-facing assistants on the GPT Store. 2. Consumer-style tools. One system prompt, light tool use. 3. Teams standardized on ChatGPT Enterprise. 4. Lightweight Actions against an OpenAPI-defined REST endpoint. ## When to build a Claude Skill 1. Code-adjacent workflows. Reads/writes files, runs shell, commits to git. 2. Multi-tool pipelines orchestrating Firecrawl, DataForSEO, writers. 3. Internal operations with engineering support (git-versioned skills). 4. Privacy-sensitive use cases on Bedrock or Vertex. 5. Agent SDK product deployments. ## When to build both 1. Public "ask our methodology" GPT plus internal "run our methodology" Skill. 2. Teams split between sales (ChatGPT) and engineering (Claude Code) on the same knowledge base. 3. Consumer preview on GPT Store, enterprise tier on Agent SDK. ## A worked example: sales enablement for a 50-person SaaS team ### Custom GPT version ``` Name: SaaS Co Sales Buddy System prompt: You are the SaaS Co sales assistant... Knowledge files: - product-messaging-v7.pdf - objection-handling-playbook.md Actions: - GET /pricing/{plan} - POST /crm/log-call ``` ### Claude Skill version ``` --- name: saasco-sales-buddy description: Sales enablement for SaaS Co reps. Triggers on "prep me for", "draft outreach", "pricing for". --- ## Instructions 1. On "prep me for {account}" → account-research sub-agent. 2. On "draft outreach" → load references/objection-handling.md and voice guide, then produce a 3-paragraph email. 3. On pricing → call pricing MCP server. ``` ### Which to pick For this team: probably both. GPT for the daily 80%, Skill for the weekly 20% of deep account prep. If forced to one, the GPT wins on adoption because reps already live in ChatGPT. The Skill wins on capability ceiling. ## How to create a Claude Skill To create a Claude Skill, make a folder, drop a SKILL.md file inside it with YAML frontmatter (a name and a description), write your instructions in the body, and add optional references/ and scripts/ subfolders. That is the whole format. The hard part is not the structure, it is writing a description sharp enough that Claude invokes the Skill at the right moment and not at the wrong one. Minimum viable layout: ``` my-skill/ ├── SKILL.md # required: frontmatter + instructions ├── references/ # optional: docs Claude reads on demand │ ├── voice-guide.md │ └── checklist.md └── scripts/ # optional: code Claude runs └── audit.py ``` The SKILL.md frontmatter does the routing. The `description` field is the single most important line, because Claude reads it (not the full body) when deciding whether the Skill applies. Write it to name the triggers explicitly. ``` --- name: technical-seo-auditor description: Audits a URL for technical SEO issues (titles, headings, schema, internal links, Core Web Vitals). Use when the user says "audit this page", "technical SEO check", or pastes a URL and asks what is wrong with it. --- You are a technical SEO auditor. ## Instructions 1. Fetch the URL and parse the HTML. 2. Run scripts/audit.py to score the page. 3. Report issues grouped by severity, with the fix for each. ``` Rules that save you a debugging session: - **The description controls invocation.** Vague descriptions get ignored or misfire. List the literal phrases and tasks that should trigger the Skill. - **references/ is loaded on demand, not up front.** Put long rubrics, style guides, and lookup tables here so they do not bloat the context window until Claude needs them. - **scripts/ runs deterministic work.** Anything you want done the same way every time (parsing, scoring, formatting) belongs in a script, not in prose the model re-improvises. - **Keep SKILL.md short.** The body is always-on context. Long procedures go in references and get pulled in only when relevant. To test it, install the Skill in Claude Code (or your harness), prompt it with the exact phrasing from your description and confirm it fires, then prompt it with an unrelated request and confirm it does not. If it fires when it should not, tighten the description. If it never fires, your triggers are too narrow or too abstract. ## Claude Skills examples The clearest way to understand Skills is to see a few real ones. Each is a folder with a SKILL.md and a references or scripts directory, tied to a concrete job a marketing team actually has. - **Brand-voice writer.** Drafts product descriptions or blog copy in a specific brand's voice. The references/ folder holds the voice spec and a corpus of approved examples; the SKILL.md sets generation rules and a compliance gate (banned words, claims that need legal review). Fires on "write a product description for" or "draft this in our voice". Full build: https://www.winstondigitalmarketing.com/playbooks/brand-voice-claude-skill-product-descriptions/ - **Technical-SEO auditor.** Takes a URL, parses the HTML, and reports issues by severity. A scripts/audit.py does the deterministic scoring (titles, headings, schema, internal links); references/checklist.md holds the full rubric. Fires on "audit this page" or a pasted URL. - **Doc generator.** Turns a loose spec or notes into a structured document (a brief, a one-pager, a release note) using a fixed template. The template and section rules live in references/; a script handles file output. Fires on "write the brief for" or "turn these notes into a one-pager". The pattern repeats: a tight description for routing, references for slow-changing knowledge, scripts for the work that must be identical every time. Once you have built two or three, new Skills take an afternoon. ## Claude Skills vs MCP servers A Claude Skill and an MCP server are not competitors, they are different layers that work together. A Skill packages instructions, knowledge, and procedure (the "what to do and how"). An MCP server exposes live tools and data (the "what to call"). A Skill can call an MCP server. An MCP server cannot call a Skill. People conflate them because both extend Claude, but they solve different halves of the problem. | Dimension | Claude Skill | MCP server | |---|---|---| | What it is | A folder of instructions, references, scripts | A running service exposing tools and data | | What it provides | Procedure and knowledge (the playbook) | Live capabilities (search, query a DB, hit an API) | | Who calls whom | A Skill can call MCP servers | Called by Claude; does not load Skills | | State | Stateless files loaded into context | Stateful, can hold connections and sessions | | Reuse | Shared by copying the folder | Shared by pointing many clients at one endpoint | A concrete pairing: the technical-SEO auditor Skill holds the rubric and the report format, but the actual ranking data comes from a DataForSEO MCP server it calls mid-run. The Skill is the auditor's brain; the MCP server is its hands. For which MCP servers to stand up first: https://www.winstondigitalmarketing.com/playbooks/mcp-servers-for-marketing-teams/ ## FAQ **Can I migrate from a Custom GPT to a Claude Skill?** Yes. The system prompt becomes SKILL.md. Knowledge files become references/. Actions become scripts or MCP calls. **Do Claude Skills work outside Claude Code?** Yes. Skills are a packaging format. They run anywhere the Claude API is called with the right loader. Agent SDK, MCP, Claude.ai, custom harnesses. **How is data privacy handled differently?** GPTs route through OpenAI by default. Skills run wherever you run the Claude API, including Bedrock and Vertex. **Which is cheaper to run at scale?** Volume matters more than per-token rate. Small teams with heavy usage often win on ChatGPT seat bundling. Larger teams with selective power users often win on pay-per-token Claude. **What about Google Gems?** Closest to GPTs in shape. Lands in organizations already on Google Workspace with Gemini as default. ### What is the Claude equivalent of a Custom GPT? Claude Skills. A Skill is Anthropic's packaging for a reusable, specialized assistant: an instructions file plus reference files and optional scripts that Claude loads when a task matches. Claude Projects are the lighter-weight cousin, but Skills are the true Custom GPT equivalent because they are portable, versioned, and shareable. ### Can you create Custom GPTs in Claude? Not literally. Custom GPTs are an OpenAI product and only exist inside ChatGPT. What you build in Claude instead is a Skill, which covers the same job. Everything a Custom GPT's system prompt and knowledge files do maps one-to-one onto a Skill's SKILL.md and references folder. ### Can Claude Skills be used in ChatGPT? No. Skills are a Claude-side format and ChatGPT does not load them. The content ports easily in either direction: a Skill's instructions file becomes a Custom GPT's system prompt and its reference files become GPT knowledge uploads. Maintain the source content once, package it twice. ### What is the difference between Claude Projects and Custom GPTs? A Claude Project is a workspace inside Claude.ai that holds persistent instructions, uploaded files, and chat history scoped to one project. A Custom GPT is OpenAI's named assistant inside ChatGPT, with a system prompt, knowledge files, and Actions. They feel similar because both are configured in a UI and keep their knowledge inside the host app, but they live in different ecosystems and neither loads the other. A Custom GPT can call external Actions and be published to the GPT Store, while a Claude Project is a lighter personal or team workspace with no distribution surface. For a portable, version-controlled equivalent that runs outside the app, use a Claude Skill, not a Project. ### Is a Claude Project the same as a Custom GPT? Not the same, but they are the closest cousins across the two platforms. Both let you save instructions and reference files into a reusable workspace you return to across chats. A Custom GPT lives in ChatGPT, can call Actions, and can be shared through the GPT Store, while a Claude Project lives in Claude.ai and stays inside the app. For a true portable, shareable, version-controlled equivalent of a Custom GPT, use a Claude Skill instead, since a Project cannot be exported or run outside Claude.ai. ### Are Claude Skills like Custom GPTs? Yes, conceptually they are the same primitive. Both let you save a configured agent (instructions, knowledge, tool access) and reuse it across conversations. The differences are how they are packaged (Custom GPT is configured in the ChatGPT UI; Claude Skill is a folder of files you can version-control), where they run (Custom GPT runs in OpenAI's infrastructure; Claude Skill runs anywhere the Claude API runs, including on AWS Bedrock and Google Vertex), and what they can call (Custom GPT calls Actions; Claude Skill calls scripts and MCP servers). Choose based on which ecosystem and runtime fits your stack. ### Custom GPTs vs Claude Skills: which should I use? Use a Custom GPT if your users live in ChatGPT and your team wants a no-code authoring surface. Use a Claude Skill if you want version-controlled agents that run inside your own runtime, integrate with MCP servers, or need to keep data inside your existing cloud (Bedrock or Vertex). For most agency or enterprise workloads where the agent runs as part of a larger pipeline, Claude Skills are the better primitive because they compose with other tools cleanly. For internal team productivity bots where the consumers are ChatGPT users already, Custom GPTs win on adoption. ### How do I create a Claude Skill? Make a folder, add a SKILL.md file with YAML frontmatter (a name and a description) and your instructions in the body, then add optional references/ and scripts/ subfolders. The description field controls invocation, so write it to name the literal phrases and tasks that should trigger the Skill. Put long rubrics and style guides in references/ (loaded on demand) and deterministic work in scripts/. To test it, install the Skill in Claude Code, prompt it with the exact phrasing from your description and confirm it fires, then prompt it with an unrelated request and confirm it does not. Tighten the description until both pass. ### What are some examples of Claude Skills? Three that map cleanly to marketing work: a brand-voice writer that drafts copy in a specific brand's voice using a voice spec and corpus in references/ plus a compliance gate; a technical-SEO auditor that parses a URL and reports issues by severity, with a scripts/audit.py doing the scoring and references/checklist.md holding the rubric; and a doc generator that turns loose notes into a structured brief or one-pager from a fixed template. The pattern repeats across all three: a tight description for routing, references for slow-changing knowledge, and scripts for the work that must be identical every run. ### What is the difference between a Claude Skill and an MCP server? A Skill packages instructions, knowledge, and procedure (what to do and how). An MCP server exposes live tools and data (what to call). A Skill can call an MCP server; an MCP server cannot call a Skill. They are different layers that work together, not competitors. Example: a technical-SEO auditor Skill holds the rubric and report format, but the ranking data comes from a DataForSEO MCP server it calls mid-run. The Skill is the brain; the MCP server is the hands. ## Related playbooks - [Custom GPTs and Claude Skills](https://www.winstondigitalmarketing.com/services/ai-marketing/custom-gpts-and-skills/). The service page. Design, build, and deploy GPTs and Skills for your team. - [How we built this site in 6 hours with Claude Code](https://www.winstondigitalmarketing.com/playbooks/how-we-built-this-site-in-6-hours/). The full session log. Skills, prompts, files touched, time per phase. - [A brand-voice Claude Skill for product descriptions](https://www.winstondigitalmarketing.com/playbooks/brand-voice-claude-skill-product-descriptions/). A Skill in practice: corpus, voice spec, generation rules, and a compliance gate. ## Need help deciding? Submit your use case. We will tell you whether it is a GPT, a Skill, or both, and scope the build in 48 hours. Service: https://www.winstondigitalmarketing.com/services/ai-marketing/custom-gpts-and-skills/ Audit: https://www.winstondigitalmarketing.com/contact/#audit