This is the meta layer for all prompt engineering in PAI. It owns templates, standards, and composition tools so other skills can generate prompts programmatically instead of duplicating patterns everywhere. The Handlebars-based system cut 35,000 tokens across PAI by separating structure from content. You'd use this when building dynamic agents, rendering workflow steps from YAML, or generating eval judges. The real value is treating prompts like code: templates are structure, data is content, and you compose them at runtime. It references 1,500+ papers and Anthropic's latest practices, but the practical win is that when you need to generate a briefing or validation gate, you call RenderTemplate instead of writing another 2,000 token blob.
- skills.sh installs
- 116
- GitHub stars
- 14.6K
- last updated
- Jun 3, 2026
Install
$ npx -y skills add danielmiessler/personal_ai_infrastructure --skill Prompting --agent claude-codeInstalls into .claude/skills of the current project.
Files
Prompting - Meta-Prompting & Template System
Contents: Ideal State · Anti-criteria · Intent Routing · Data & Config · Gotchas · Examples · References
Generates, optimizes, and composes prompts programmatically. It's the standard library for prompt engineering — other skills call it when they need to build or improve a prompt. The output is always a prompt to be used elsewhere, never the final content itself.
Philosophy: Prompts that write prompts. Structure is code, content is data. Meta-prompting enables dynamic composition where the same template with different data generates specialized agents, workflows, and evaluation frameworks.
Ideal State
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- Repository
- github.com/danielmiessler/personal_ai_infrastructure
- Categories
- AI & Agent Building, Cloud & Infrastructure
- First seen
- Jun 3, 2026
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