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
- Read the source
SKILL.mdand parse frontmatter - Detect skill complexity tier (1-4)
- Inventory all files: scripts/, references/, agents/, sub-skills
- 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
--platformflag 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.
Source: AgriciDaniel/skill-forge — distributed by TomeVault.