 | Hey r/LocalLLaMA — I open-sourced a tool that brings eval-driven development to AI agent skills. It's based on Anthropic's official skill-creator for Claude Code, but rewritten in TypeScript to work with OpenCode (which supports 300+ models including local ones). The problem: creating skills for AI agents is trial-and-error. You write a skill, test it manually, and hope it triggers on the right prompts. There's no systematic way to measure if a skill works. What this does:
The most interesting part for this community: it works with any of OpenCode's supported models. If you're running local models through OpenCode, you can use this tool with them. One-command install: Apache 2.0 license. Based on Anthropic's skill-creator with attribution. GitHub: https://github.com/antongulin/opencode-skill-creator npm: https://www.npmjs.com/package/opencode-skill-creator Happy to answer questions about the eval methodology, local model support, or architecture. [link] [comments] |
I ported Anthropic's official skill-creator from Claude Code to OpenCode — now you can create and evaluate AI agent skills with any model
Reddit r/LocalLLaMA / 4/11/2026
💬 OpinionDeveloper Stack & InfrastructureSignals & Early TrendsTools & Practical Usage
Key Points
- A developer open-sourced an “eval-driven” AI agent skill creator that ports Anthropic’s official Claude Code skill-creator to OpenCode using TypeScript.
- The tool supports guided skill creation via an intake interview, automatically generates eval sets (should-trigger/should-not-trigger prompts), and measures trigger accuracy by comparing runs with and without the skill.
- It iteratively optimizes skill descriptions using an LLM loop with a train/test split (up to five iterations), and provides an HTML viewer plus variance/benchmark reporting for human review.
- Because it is designed to work with OpenCode, it can evaluate and develop skills using any of OpenCode’s 300+ models, including locally hosted models.
- Installation is offered via an npm one-command workflow, with the project released under an Apache 2.0 license and attributed to Anthropic’s original approach.
