engineer-production-prompts

Writes, versions, and regression-guards production system prompts for shipped AI features.

1|Updated Jul 13, 2026
One-click install
npx skills add https://github.com/dineshrevunuru/SuperSkills --skill engineer-production-prompts-dineshrevunuru
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: engineer-production-prompts
Source: https://github.com/dineshrevunuru/SuperSkills/tree/main/engineer-production-prompts
Command: npx skills add https://github.com/dineshrevunuru/SuperSkills --skill engineer-production-prompts-dineshrevunuru

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Prompts that ship to users are often edited ad hoc, stored outside version control, and changed without testing, causing silent behavior regressions. This Skill treats system prompts as production artifacts with enforced structure, versioning, and regression gates. ## Core Features & Use Cases - Seven-Section Prompt Anatomy: Enforces a load-bearing structure covering role, negative charter, injected facts, field-level register specs, numbered behavior flows, tie-break rules, and enumerated off-limits topics. - Versioning Discipline: Requires prompts in source control, single-source-of-truth fact injection, dated model pinning with env revert levers, and reviewed diffs with stated reasons. - Regression Gates: Mandates golden routing sets, 100% off-limits suites, register lint checks, and latency measurement before any prompt change merges. - Use Case: When a teammate says "the model keeps saying X" or proposes switching models, use this Skill to diagnose whether the fix is structural or prompt-level, write the change as a reviewed diff, and gate it behind a full regression run. ## Quick Start Review my system prompt for the customer support agent and tell me whether it is production-grade, then rewrite the weak sections.

Frequently Asked Questions about engineer-production-prompts

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I write a production-grade system prompt?

Structure it in seven ordered sections: role and surface, negative charter, injected closed-world facts, per-field register specs, numbered behavior flow, explicit tie-break rules, and enumerated off-limits topics. Keep it in source control and gate every change behind a regression run.

How do I version and test prompt changes like code?

Store the prompt in the repo next to the code that sends it, pin the model to a dated snapshot with an env revert lever, and require a reviewed diff with a stated reason. Re-run the golden routing set, off-limits suite at 100%, and register lint before merging.

When should I use few-shot examples in a system prompt?

Add examples only for what schema and rules cannot pin: register calibration, near-miss disambiguation, and format subtleties. Always include one refusal and one fallback example, draw them from real data, and cover them with the same regression set.

What belongs in the prompt versus enforced in code?

Anything code can guarantee, such as forced tool_choice, closed enums, or server-side safety gates, must not live in the prompt. Prompts handle tendencies like tone, tie-breaks, and pacing; structural constraints handle guarantees.

Why did my model behavior change when nobody edited the code?

Undated model aliases or dashboard-edited prompts drift silently. Pin the model to a dated snapshot, keep the prompt in version control, and measure any model switch against the same utterance set before adopting it.