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

• Every prompt this library generates or optimizes articulates the ideal state, not the procedure: WHAT done looks like as testable outcomes, the CONSTRAINTS, and the high-quality TOOLS available, with the specificity moved to the outcome so the prompt is more precise, not vaguer.
• In every prompt this library generates or optimizes, the only HOW that survives is the four keep-classes (safety-gate, verified-gotcha, tool-contract, output-format-contract); every other procedural line failed the test of whether a smarter model would make it unnecessary. Deterministic tools (*.ts) are exempt. The full standard is Standards.md § Ideal-State Prompting.
• A prompt composed from data keeps structure as code and content as data: a Handlebars template under Templates/ rendered with a data object, or from a .yaml or .json file, through RenderTemplate.ts.
• A reusable prompt captures the supplied intent, constraints, and output contract; its output contract can guide the intended downstream task; and it was tested before it was declared ready.
• When Standards.md is authored or audited (not on routine template renders), both official anchors were fetched and every place our standards diverge from the current official guidance is flagged.

Anti-criteria