Continuous AI Workflow for Reviewing Dictionary (Word) Contributions

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# Implementation

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# Summary

Introduce a Continuous AI (CAI) workflow that automatically reviews word contributions made to the dictionary. Contributions submitted via the jargons-editor are already tagged with labels like `📖new-word`, ` 📖edit-word`, and `💻via-jargons-editor`. The workflow will leverage these labels to identify relevant pull requests and automatically perform an AI-assisted review.

# Motivation

Consistency and clarity are essential for building a developer-friendly dictionary. Currently, human reviewers ensure that contributions follow our desired structure `meaning → explanation → example (if necessary)`, but this adds friction and may discourage contributors due to slower feedback loops. Automating this first-pass review ensures contributors get instant, constructive guidance while maintainers focus on higher-level decisions.

By using GitHub Models, GenAIScript, or other AI technologies, we can integrate this process directly into GitHub Actions, embedding AI review directly into our contribution workflow.

# Goal

- Automatically trigger AI review on pull requests with labels `📖new-word` or `📖edit-word` (and `💻via-jargons-editor`).
- Validate whether the contribution follows the expected structure:
- Meaning (concise definition)
- Explanation (clear, contextual elaboration)
- Example (optional, if applicable)
- Provide actionable suggestions or improvements as review comments.
- Ensure feedback feels constructive and educational, not blocking.
- Allow maintainers to accept, refine, or dismiss AI suggestions before merging.

Start by mapping the GitHub Actions workflow entry point and the pull-request labels named in the issue: 📖new-word, 📖edit-word, and 💻via-jargons-editor. Compare GitHub Models and GenAIScript as possible implementation paths, then define done as constructive review comments that check meaning, explanation, and optional examples without blocking maintainers.

Written by the indexing model from the issue text.