Skill Conversion — Multi-Platform

Convert Claude Code skills to work on OpenAI Codex, Google Gemini CLI, Google Antigravity, and Cursor while maintaining quality and following each platform's best practices.

Process

Step 1: Read Source Skill

1. Read the source SKILL.md and parse frontmatter
2. Detect skill complexity tier (1-4)
3. Inventory all files: scripts/, references/, agents/, sub-skills
4. Identify MCP config (.mcp.json) if present

Step 2: Analyze Platform Compatibility

Run dry-run analysis first:

python scripts/convert_skill.py <path> --dry-run --target all

Review the compatibility report:

• Portable fields: Transfer directly (name, description, license)
• Adaptable fields: Need platform-specific handling
• Claude-only fields: Will be stripped with warnings

Step 3: Convert to Target Platforms

Run the conversion:

python scripts/convert_skill.py <path> --target codex,gemini,antigravity,cursor --output dist/

For MCP config conversion:

python scripts/convert_skill.py <path> --target all --output dist/ --include-mcp

Step 4: Handle Claude-Only Features

Features that need manual adaptation per platform:

Claude Feature	Codex	Gemini/Antigravity	Cursor
allowed-tools	Supported	Remove; all tools available	Remove; no equivalent
context: fork	No equivalent	No equivalent	No equivalent
hooks	1 event (notify)	Partial (CLI)	6 events (beta)
model selection	No equivalent	No equivalent	No equivalent
Task delegation	Break into separate skills	Break into separate skills	Background Agents (Ultra plan)
/slash commands	$mention syntax	Description-based activation	Description-based + @rule
Sub-skill routing	Separate skills with $mention	Separate skills, LLM-routed	Separate skills, LLM-routed

Step 5: Validate Converted Skills

Run validation on each generated output:

python scripts/validate_skill.py dist/codex/<skill-name>/
python scripts/validate_skill.py dist/gemini/<skill-name>/
python scripts/validate_skill.py dist/antigravity/<skill-name>/
python scripts/validate_skill.py dist/cursor/<skill-name>/

Fix any critical or high-priority issues before proceeding.

Step 6: Generate Deployment Report

Present the user with a summary:

## Conversion Report: {skill-name}

| Platform | Score | Files | Warnings | Manual Steps |
|----------|-------|-------|----------|-------------|
| Codex | 92% | 4 | 2 | 1 |
| Gemini | 88% | 3 | 3 | 1 |
| Antigravity | 88% | 3 | 3 | 1 |
| Cursor | 88% | 3 | 3 | 1 |

### Generated Files
[list per platform]

### Warnings
[list per platform]

### Manual Steps Required
[list per platform]

Step 7: Generate Multi-Platform Install Script

When converting for all platforms (--target all), the script auto-generates install-multiplatform.sh that:

• Auto-detects the current agent platform
• Installs to the correct skill path
• Supports --platform flag for explicit selection

Platform Quick Reference

Platform	Skill Path	Instruction File	Config Format
Claude Code	.claude/skills/	CLAUDE.md	JSON (.mcp.json)
OpenAI Codex	.agents/skills/	AGENTS.md	TOML (config.toml)
Gemini CLI	.gemini/skills/	GEMINI.md	JSON (settings.json)
Antigravity	.agent/skills/	GEMINI.md	JSON (mcp_config.json)
Cursor	.cursor/skills/	.cursor/rules/*.mdc	JSON (mcp.json)

For full platform specs, load references/platforms.md.

Tier Conversion Guidelines

• Tier 1 (single SKILL.md): Converts cleanly. Auto-convert recommended.
• Tier 2 (skill + scripts): Converts well. Verify script paths.
• Tier 3 (multi-skill): Partial. Each sub-skill converts independently. Routing needs manual work.
• Tier 4 (ecosystem): Manual review required. Subagent delegation needs platform adaptation.