opencode-skill-creator

Automating the OpenCode skill development lifecycle involves coordinating evaluation loops, drafting improvement prompts, and packaging outputs to accelerate testing and deployment. This structured approach reduces drift and improves triggering accuracy across repeated experiments.

Running trigger-evaluation loops requires drafting evaluation prompts, executing tests, collecting grading results, and iterating descriptions. This cycle tracks improvements across iterations to validate trigger tuning and ensure robust skill behavior before packaging.

Yes, packaging Claude skills at scale is supported by verifying distribution readiness and streamlining deployment. Teams can safely bundle iterated, evaluated skills into distributable packages ready for downstream use.

Yes, building and evaluating OpenCode skills requires Anthropic and pyyaml dependencies. These libraries facilitate the underlying prompt execution, evaluation grading, and configuration management needed for the automated iteration workflow.

An automated evaluation loop improves trigger tuning by repeatedly testing skill descriptions against expected triggers, grading the results, and feeding improvements back into the prompt. This iterative process increases accuracy and reduces misfiring over time.

Automating skill packaging without manual review risks deploying unverified trigger logic or incomplete iterations. While automation accelerates readiness checks, human oversight ensures the final distributable bundle safely handles edge cases and complex intents.