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Pattern for building automation scripts that classify GitHub issues into prompt templates using label-based routing and extract contextual data for batch processing
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/vamseeachanta/workspace-hub --skill label-driven-prompt-generation-architecture命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
基于 SOC 职业分类
正在显示 SKILL.md
| name | label-driven-prompt-generation-architecture |
| description | Pattern for building automation scripts that classify GitHub issues into prompt templates using label-based routing and extract contextual data for batch processing |
| version | 1.0.0 |
| source | auto-extracted |
| extracted | "2026-04-10T00:00:00.000Z" |
| metadata | {"tags":["automation","github-api","python-cli","prompt-engineering","architecture"]} |
When building issue-to-prompt automation, use Python + gh CLI for structured classification: parse issue labels (e.g., cat:bugfix) to map to prompt templates, extract plan files or metadata from issue bodies using path tables, and implement both single-issue and batch-query modes. Store scripts in scripts/automation/, mark transient output directories in .gitignore, and verify classification against real issues before batch deployment. Test label matching, plan extraction, and batch filtering in sequence to catch routing logic errors early.
