# Label Driven Prompt Generation Architecture

> Pattern for building automation scripts that classify GitHub issues into prompt templates using label-based routing and extract contextual data for batch processing

- Skill: `vamseeachanta/label-driven-prompt-generation-architecture` (Agent Skill)
- Install (CLI): `npx skillmds@latest add vamseeachanta/label-driven-prompt-generation-architecture`
- Raw SKILL.md: https://api.skillmd.com/api/skills/vamseeachanta/label-driven-prompt-generation-architecture/raw
- Safety review: PASS (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: vamseeachanta (https://skillmd.com/u/vamseeachanta)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/vamseeachanta/label-driven-prompt-generation-architecture

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# Label-Driven Prompt Generation 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.