The Specific Situation

A developer evaluates whether to build reusable AI workflows as Claude Code skills or ChatGPT Custom GPTs. Both promise “tell the AI once, reuse forever.” But they serve different audiences and different workflows. Skills target developers working in code editors and terminals. GPTs target anyone with a web browser. Understanding where each excels prevents building in the wrong platform.

Technical Foundation

Claude Code Skills are SKILL.md markdown files stored locally in .claude/skills/ or distributed via plugins. They follow the agentskills.io open standard. Skills activate through progressive disclosure: description always available, body loaded on invocation, references read on demand. Skills have direct access to your local filesystem, terminal, git, and any CLI tool. They support frontmatter controls for invocation behavior, path-conditional activation, subagent delegation, dynamic context injection, and permission management.

ChatGPT Custom GPTs are web-based configurations created through the GPT Builder interface or API. Each GPT has instructions (system prompt), conversation starters, knowledge files (uploaded documents up to 20 files), and optional Actions (API integrations via OpenAPI specs). GPTs run in ChatGPT’s web interface and are shared via URL. They cannot access local files, run terminal commands, or interact with version control.

The Working SKILL.md

A deployment verification workflow – comparing both approaches:

Claude Code skill:

---
name: verify-deploy
description: >
  Verify a deployment by checking health endpoints, running smoke
  tests, and comparing response times against baseline. Use after
  deploying to staging or production.
disable-model-invocation: true
allowed-tools: Bash(curl *) Bash(node *) Read
---

# Deployment Verification

## Health Checks
1. Hit each endpoint in `config/health-endpoints.json`
2. Verify HTTP 200 response within 5 seconds
3. Check response body contains expected version string

## Smoke Tests
Run `pnpm test:smoke --env=$ARGUMENTS[0]`
Parse test results from stdout.

## Baseline Comparison
!`cat reports/baseline-response-times.json`
Compare current response times against baseline.
Flag any endpoint 50%+ slower than baseline.

ChatGPT Custom GPT equivalent: You would paste the instructions into the GPT Builder, upload the health endpoints and baseline JSON as knowledge files, and create an Action pointing to your health check API. The GPT cannot run pnpm test:smoke because it has no terminal access. It cannot read reports/baseline-response-times.json unless you uploaded it manually. Every deployment would require re-uploading current data.

Where ChatGPT Custom GPTs Win

1. Non-developer accessibility. GPTs work in a web browser with a chat interface. Product managers, designers, and marketing teams can use a GPT without installing CLI tools, configuring environments, or understanding file systems. If your workflow consumers are non-technical, GPTs have zero setup friction.

2. Built-in public sharing and marketplace. GPTs can be published to the GPT Store and shared via URL. Anyone with a ChatGPT Plus subscription can use a shared GPT. Claude Code skills require cloning a repo or installing a plugin. For public distribution to a broad audience, GPTs have the infrastructure built in.

3. DALL-E image generation. GPTs can generate images natively via DALL-E integration. If your workflow involves creating diagrams, mockups, or visual assets alongside text, GPTs have this built in. Claude Code skills can generate images only by calling external tools or scripts.

4. Actions for API integration without code. GPT Actions let you connect to any API by pasting an OpenAPI spec – no code required. The API calls happen server-side. For simple API workflows (fetch weather, check stock prices, query a database API), Actions are faster to set up than writing a skill’s bash script wrapper.

Feature comparison at a glance:

Feature                  | Claude Skills  | Custom GPTs
------------------------|----------------|------------------
Storage                 | Local files    | Cloud-hosted
Version control         | Git-tracked    | None
Filesystem access       | Full           | None
Terminal execution      | Yes            | No
Open standard           | agentskills.io | OpenAI-only
Auto-activation         | paths field    | Not available
Team sharing            | Git commit     | URL sharing
Non-developer access    | Requires CLI   | Web browser
Image generation        | External tools | DALL-E built-in
API integration         | Scripts/MCP    | Actions (no-code)

Where Claude Code Skills Win

Local filesystem access. Skills read and write files directly in your project. GPTs cannot touch your codebase – every file interaction requires manual upload/download.

Version control. Skills are git-tracked markdown files. You get branch-specific skills, diff history, and code review. GPTs have no version control – changes are immediate and irreversible unless you maintain external backups.

Open standard (agentskills.io). The same SKILL.md works in Claude Code, Codex, Gemini CLI, Cursor, and 8+ tools. GPTs work only in ChatGPT. No vendor lock-in with skills.

Progressive disclosure. Skills load body content only when invoked, saving tokens. GPTs load their full instructions on every conversation start, regardless of whether the user needs all capabilities.

Path-conditional activation. Skills auto-activate when you open specific file types (paths: ["**/*.ts"]). GPTs have no awareness of your file system – they are always fully loaded or not loaded at all.

Deterministic tool control. Skills use allowed-tools to pre-approve specific tool calls and disable-model-invocation to prevent auto-loading. GPTs have coarser control: tools are either enabled or disabled globally.

Hybrid Use Case

Use Claude Code skills for development workflows where local file access, version control, and CLI integration are essential: code review, test generation, deployment, refactoring. Use Custom GPTs for non-developer-facing workflows: a customer FAQ bot built from uploaded knowledge files, a product specification generator for the design team, or a report summarizer for management.

Practical hybrid: your Claude Code deployment skill produces a JSON report. A GPT Action consumes that report via a webhook and generates a visual dashboard summary for stakeholders who do not use the terminal.

Common Problems and Fixes

GPT knowledge files outdated. GPTs do not auto-update their knowledge files. When your docs change, you must re-upload manually. Skills reference files on disk that stay current automatically.

Skill too complex for a GPT migration. Skills with dynamic context injection, subagent delegation, and file-based pipelines have no GPT equivalent. These features are developer-specific and should stay as skills.

GPT Actions failing silently. When a GPT Action’s API returns an error, the GPT often hallucinates a response instead of showing the error. Always validate Action responses by asking the GPT to show the raw API response.

Production Gotchas

GPT instructions are visible to users who ask “show me your system prompt.” Sensitive internal instructions should not go into a public GPT. Skills are local files with standard file system permissions – they are only accessible to users with repository access.

GPTs have a 128K context window but load all instructions and knowledge file context on every turn. A GPT with 15 large knowledge files may hit context limits on complex queries. Skills load references on demand, avoiding this problem entirely.

Checklist