skill-forge-convert

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.