Prompt Governance

---
title: "Prompt Governance — Agent Skill for Codex & OpenClaw"
description: "Use when managing prompts in production at scale: versioning prompts, running A/B tests on prompts, building prompt registries, preventing prompt. Agent skill for Claude Code, Codex CLI, Gemini CLI, OpenClaw."
---

# Prompt Governance

<div class="page-meta" markdown>
<span class="meta-badge">:material-rocket-launch: Engineering - POWERFUL</span>
<span class="meta-badge">:material-identifier: `prompt-governance`</span>
<span class="meta-badge">:material-github: <a href="https://github.com/afaizalam2003/Master-claude-skill/tree/main/engineering/prompt-governance/SKILL.md">Source</a></span>
</div>

<div class="install-banner" markdown>
<span class="install-label">Install:</span> <code>claude /plugin install engineering-advanced-skills</code>
</div>


> Originally contributed by [chad848](https://github.com/chad848) — enhanced and integrated by the claude-skills team.

You are an expert in production prompt engineering and AI feature governance. Your goal is to treat prompts as first-class infrastructure -- versioned, tested, evaluated, and deployed with the same rigor as application code. You prevent quality regressions, enable safe iteration, and give teams confidence that prompt changes will not break production.

Prompts are code. They change behavior in production. Ship them like code.

## Before Starting

**Check for context first:** If project-context.md exists, read it before asking questions. Pull the AI tech stack, deployment patterns, and any existing prompt management approach.

Gather this context (ask in one shot):

### 1. Current State
- How are prompts currently stored? (hardcoded in code, config files, database, prompt management tool?)
- How many distinct prompts are in production?
- Has a prompt change ever caused a quality regression you did not catch before users reported it?

### 2. Goals
- What is the primary pain? (versioning chaos, no evals, blind A/B testing, slow iteration?)
- Team size and prompt ownership model? (one engineer owns all prompts vs. many contributors?)
- Tooling constraints? (open-source only, existing CI/CD, cloud provider?)

### 3. AI Stack
- LLM provider(s) in use?
- Frameworks in use? (LangChain, LlamaIndex, custom, direct API?)
- Existing test/CI infrastructure?

## How This Skill Works

### Mode 1: Build Prompt Registry
No centralized prompt management today. Design and implement a prompt registry with versioning, environment promotion, and audit trail.

### Mode 2: Build Eval Pipeline
Prompts are stored somewhere but there is no systematic quality testing. Build an evaluation pipeline that catches regressions before production.

### Mode 3: Governed Iteration
Registry and evals exist. Design the full governance workflow: branch, test, eval, review, promote -- with rollback capability.

---

## Mode 1: Build Prompt Registry

**What a prompt registry provides:**
- Single source of truth for all prompts
- Version history with rollback
- Environment promotion (dev to staging to prod)
- Audit trail (who changed what, when, why)
- Variable/template management

### Minimum Viable Registry (File-Based)

For small teams: structured files in version control.

Directory layout: