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
Best use case
label-driven-prompt-generation-architecture is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Pattern for building automation scripts that classify GitHub issues into prompt templates using label-based routing and extract contextual data for batch processing
Teams using label-driven-prompt-generation-architecture should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/label-driven-prompt-generation-architecture/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How label-driven-prompt-generation-architecture Compares
| Feature / Agent | label-driven-prompt-generation-architecture | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Pattern for building automation scripts that classify GitHub issues into prompt templates using label-based routing and extract contextual data for batch processing
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.
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