Yes, with deliberate design choices. A SKILL.md file written for Claude Code transfers to Cursor, GitHub Copilot, Gemini CLI, and 11+ other AI coding tools — but only the parts you wrote for the general problem. The parts you wrote for Claude Code's delivery infrastructure don't move. AEM builds SKILL.md files for this exact challenge: cross-platform skill engineering where the instruction body works everywhere and the platform-specific extensions are clearly marked. About 59% of developers run three or more AI coding tools in parallel (Second Talent, 2025), which makes this portability question less theoretical than it sounds.

TL;DR: The SKILL.md instruction body (steps, rules, output contracts, examples) is portable to any AI coding tool that accepts markdown instruction files. The YAML frontmatter, description field trigger, and progressive reference loading directives are Claude Code-specific and don't function elsewhere. A skill built with platform-agnostic instructions works everywhere; a skill built to maximize Claude Code features doesn't.

What parts of a SKILL.md file transfer across AI assistants?

Four elements transfer cleanly across every AI coding tool: process steps, rules sections, output contracts, and embedded examples. These are the instruction-level content of a skill. Every tool reads them as model directives. They work in Claude Code, Cursor, GitHub Copilot, and Gemini CLI without modification.

  • Process steps. Numbered instructions telling the model what to do in sequence. Every AI coding tool reads these as instruction content. A 10-step code review process works in Claude Code, Cursor, Copilot, and Gemini CLI because it's telling the model to perform specific actions, not invoking Claude Code-specific infrastructure.
  • Rules sections. "Always flag hardcoded credentials. Never remove error handling without explanation." These constraints work in every tool because they're model directives, not platform calls.
  • Output contracts. The specification of what the skill produces and doesn't produce. "Produce a JSON object with severity, location, and fix fields. Do NOT produce prose commentary." Every AI model respects explicit output constraints.
  • Embedded examples. Code samples, before/after comparisons, and template structures embedded in the skill body are read as instruction context by every platform.

These four elements are the skill's value. If they're well-written, the skill works. The platform handles invocation differently, but the core instruction quality is the same everywhere. GitHub Copilot reached 20 million cumulative users by mid-2025 (GitHub, 2025), Claude Code grew from 3% to 18% developer adoption in under a year (JetBrains, January 2026), and Cursor is in use at more than half of Fortune 500 companies (Cursor, 2025). The skill libraries you build today need to run on all three.

What breaks when you use a Claude Code skill in another tool?

Three things break: the description field trigger, progressive reference file loading, and skill chaining. All three are Claude Code-specific delivery mechanisms, not instruction content. A skill that avoids all three works identically across platforms. A skill that relies on any of them needs adjustment for cross-platform use.

  • The description field trigger. Claude Code reads the description at startup and routes incoming requests to the matching skill. "Use this skill when the user asks to write an API integration test" fires automatically in Claude Code. In every other tool, it's documentation. You need to invoke the skill manually.
  • Progressive reference file loading. Claude Code executes this instruction: "If the task involves authentication patterns, read references/auth-patterns.md before step 3." Cursor and Copilot ignore it. The reference file is not loaded. If your skill depends on a reference file for core functionality, the cross-platform version produces worse output.
  • Skill chaining. Claude Code supports delegating to a second skill via the Agent tool pattern. No other platform replicates this. Skills that chain to other skills need to be restructured as self-contained units for cross-platform use.

A skill that avoids all three of these elements works identically across platforms. A skill that relies on all three of them works only in Claude Code. The breakage matters because 76% of professional developers either use or plan to use AI coding tools (Stack Overflow Developer Survey, 2024), and the skills built in Claude Code need to travel with them when they switch tools for a task.

How do you write a SKILL.md for maximum cross-platform portability?

Four rules applied during the writing phase produce a skill that works on every platform without modification: write tool-agnostic step instructions, inline critical reference content, make the output contract exact, and mark any platform-specific sections explicitly. Following all four means the instruction body never needs editing when you move the skill to a new tool.

  1. Rule 1: Write tool-agnostic step instructions. "Use the Bash tool" is Claude Code-specific. "Run the linter from the command line" is portable. Write steps that describe the action, not the Claude Code tool that performs it.
  2. Rule 2: Inline critical reference content. If a reference file contains information the skill needs to produce correct output, inline the relevant section in the skill body. Reference loading is a Claude Code optimization for token efficiency. For cross-platform skills, token efficiency is less important than reliability.
  3. Rule 3: Make the output contract exact. "When you give a model an explicit output format with examples, consistency goes from ~60% to over 95% in our benchmarks." (Addy Osmani, Engineering Director, Google Chrome, 2024). An exact output contract is the highest-value portability investment: it costs nothing to write and works on every platform.
  4. Rule 4: Mark platform-specific sections explicitly. Add a section at the bottom:
## Which instructions only apply in Claude Code?
The following instructions only apply when running in Claude Code:
- [List any progressive loading directives here]
- [List any tool-specific calls here]

This keeps the skill readable and honest about what works where. Cross-platform readers know what to ignore.

In our builds, skills written with these four rules work correctly across 8+ platforms with zero modification at the instruction level. The only thing that changes is how you invoke them. "The failure mode isn't that the model is bad at the task. It's that the task wasn't specified tightly enough. Almost every production failure traces back to an ambiguous instruction." (Simon Willison, creator of Datasette and llm CLI, 2024). Rule 1 and Rule 3 together close that gap on every platform simultaneously.

Is there a single skill format that works everywhere without modification?

Not at full feature parity. A skill format that works everywhere at consistent output quality exists, but no format delivers all of Claude Code's delivery features on every platform, because GitHub Copilot, Cursor, and Gemini CLI each implement different subsets of the SKILL.md specification. The instruction body is universal. The automation layer is not.

What does exist: a skill format that works at consistent quality everywhere. The instruction body of a well-written SKILL.md achieves consistent output across all platforms. You lose the automation and efficiency features of Claude Code (automatic triggering, progressive loading, multi-skill routing), but the output quality from following the skill's instructions stays the same.

The practical impact: the skill works. You just have to ask for it manually.

For most developers, this is fine. You build 20 skills for Claude Code. You port 3-4 of the most useful ones to Cursor or Copilot for convenience. The 3-4 you port are the ones you invoke by habit anyway — manual invocation costs you half a second.

For teams that need full automatic skill routing across multiple tools, Claude Code is the platform to build on. No other tool replicates the description field classification mechanism.

For the full technical breakdown of format differences, see Skills vs Cursor Rules vs Copilot Instructions: A Cross-Platform Comparison. For which specific tools read SKILL.md files, see What AI Coding Tools Support SKILL.md Files?.

What is the universal SKILL.md format?

The universal SKILL.md format is the base specification: a markdown instruction body with YAML frontmatter for metadata. Anthropic published Agent Skills as an open standard on December 18, 2025, and every platform that claims SKILL.md support reads this base format. The specification covers the instruction body. It makes no guarantees about trigger routing, progressive loading, or frontmatter processing.

What the universal format doesn't include: any guarantee about frontmatter processing, trigger routing, or progressive loading. Those are Claude Code-specific extensions to the base spec.

When a tool says it "supports SKILL.md," it means it reads the markdown instruction body. It does not mean it implements all of the features in the Claude Code skill specification. The base format is universal. The feature set is not.

What Do Developers Ask Most About SKILL.md Portability?

Developers ask whether they can keep one file, whether output quality drops on other platforms, and what to strip from the body. The consistent answer: the instruction body is fully portable, output quality stays the same when the output contract is explicit, and the only things to remove are Claude Code-specific tool calls like Read, Write, and Bash.

Can I keep one SKILL.md file and use it in both Claude Code and Cursor?

Yes. The same file works in both without modification. Claude Code processes the frontmatter and description field. Cursor ignores them and reads the body. Keep the canonical file in .claude/skills/ and symlink or copy it to .cursor/rules/.

Will a skill built for Claude Code produce worse output in Cursor or Copilot?

Only if it depends on Claude Code-specific features for output quality. If the steps are tool-agnostic and the output contract is explicit, the output quality is the same. The difference is in how you invoke the skill, not in what it produces.

What's the minimum I need to change to use a Claude Code skill in another tool?

Usually nothing in the instruction body. Remove or ignore the YAML frontmatter. If any steps contain Claude Code tool calls (Read, Write, Bash), replace them with tool-agnostic language. If any steps contain progressive loading instructions, either inline the reference content or accept that the step is skipped.

Does the skill description field provide any value in cross-platform tools?

As documentation, yes. As a trigger, no. In Cursor or Copilot, a developer reading the file benefits from a clear description of what the skill does and when to use it. The automatic routing mechanism doesn't exist outside Claude Code, but the description is still worth writing well.

Can I build one skill and have it trigger automatically in Cursor?

Not with the SKILL.md description field. Cursor supports glob-pattern auto-attach, which attaches files to conversations based on file type or project structure. This is coarser than intent-based routing. A skill can auto-attach for all TypeScript files, but it can't auto-attach specifically for "when the user asks for a security audit."

Last updated: 2026-04-17