Domain Packs: Teach DecisionBox Your Industry in Minutes

Create, edit, import, and share AI discovery templates — entirely from the dashboard.

👋 Cross-posting from our blog for anyone who hasn't seen it. Original post: https://decisionbox.io/blog/domain-packs

TL;DR

Domain packs are how DecisionBox knows what to look for in your data. From the dashboard, you can create a domain pack, edit it with a built-in markdown editor, export it as JSON to share with others, or import one someone else made. No restarts, no deploys — just save and run a discovery.

What Is a Domain Pack?

A domain pack teaches DecisionBox's AI agent how to analyze a specific industry. Think of it as a recipe: it tells the agent what questions to ask, what patterns matter, and what context it needs about your business.

Every pack has four parts:

DecisionBox ships with built-in packs for E-Commerce, Gaming, Social Network, and Real Estate CRM. But you can create your own for any industry.

Browsing Your Packs

Navigate to Domain Packs in the sidebar. Each pack appears as a card with its name, status (Published or Draft), number of categories and analysis areas, and version.

You can create a new pack, import one from a JSON file, export any pack, edit it, or delete it. Deleting a pack doesn't affect projects that already use it — they keep their own copy.

Inside a Domain Pack: E-Commerce

Let's open the built-in E-Commerce pack to see how everything fits together.

General

The basics: a slug (ecommerce), a name, a description, a version number, and a Published toggle that controls whether the pack shows up when creating new projects.

Categories

Categories are sub-types within your domain. The E-Commerce pack ships with Multi-Category Store — for large retailers selling across electronics, appliances, fashion, and more.

You could add others like "Subscription Box" or "Marketplace", each with its own tailored prompts and analysis areas.

For comparison, the Gaming pack has three categories — Match-3, Idle, and Casual — because a puzzle game and an idle clicker need completely different analysis strategies.

Prompts

This is where the magic happens. Prompts are markdown documents that tell the AI agent how to think about your industry.

There are three base prompts:

Base Context gives the agent background about the user's specific business. It includes {{PROFILE}} — the information the user filled in about their store (business model, KPI targets, product categories). If a user sets a 3% conversion rate target, the agent uses that to judge whether what it finds is a problem or not.

Exploration is the main prompt that drives autonomous SQL generation. It tells the agent what dataset to connect to, what tables are available, and lays out a phased strategy: start broad with baseline metrics, deep-dive into each analysis area, then look for cross-area correlations. It even includes example SQL queries tailored to e-commerce — conversion funnels, revenue by category, repeat customer behavior — so the agent hits the ground running.

Recommendations takes what the agent discovered and turns it into specific, actionable steps — with target segments, expected impact, and implementation details.

Each prompt is edited with a markdown editor that supports preview mode.

You can also write category-specific prompts. Select "Multi-Category Store" from the dropdown and you'll see an exploration context that tells the agent to focus on cross-category shopping behavior, brand dynamics, and price range diversity — things that only matter for large multi-category retailers.

The Gaming pack leans on this heavily: the Idle game context explains prestige cycles and offline earnings, while the Casual game context focuses on ad tolerance and onboarding funnels. Same domain, very different analysis.

Analysis Areas

Analysis areas define what the agent should look for and how to interpret what it finds.

The E-Commerce pack has five areas — three at the base level (for all categories) and two specific to Multi-Category Store:

Each area includes its own detailed analysis prompt. For example, the Conversion Funnel prompt tells the agent to check cart abandonment by price range, compare category-level conversion, and estimate revenue at risk — and it defines what "critical" vs "medium" severity means for this type of insight.

The Gaming pack shows how category-specific areas work: Churn, Engagement, and Monetization apply to all game types, but ad_performance only activates for Casual games and economy only for Idle games.

Profile Schema

The profile schema defines the form that users fill out when creating a project. It's a JSON Schema that gets rendered as a dynamic form in the project settings.

The E-Commerce pack asks about:

• Business: Industry, business model, target market, growth stage

• Product Catalog: Number of products, average price, categories, inventory model

• Shipping: Free shipping threshold, delivery time, return rate

• Payment: Accepted methods, supported currencies

• KPI Targets: Conversion rate, AOV, 30-day retention, CAC, LTV

The Multi-Category Store category adds extra fields for catalog depth, search and discovery features, and cross-sell/bundling capabilities.

All of this becomes the {{PROFILE}} that the agent receives in every prompt. The more context users provide, the more targeted the analysis — setting a conversion rate target of 3% means the agent flags anything below that as a problem.

Create Your Own

Click New Pack and follow the same structure: general info, categories, prompts, analysis areas, and profile schema.

For example, a FinTech pack might have categories like "Neobank", "Lending", and "Payments", with analysis areas for transaction fraud, customer lifetime value, and churn prediction.

DecisionBox validates everything when you save — required template variables in prompts, analysis areas with prompts and keywords, valid slug format. Once saved, the pack is immediately available for new projects.

Sharing Packs

Every pack can be exported as a single JSON file and imported elsewhere.

Export: Click Export on any pack card. The browser downloads a portable JSON file — no database IDs, no timestamps, just the pack definition.

Import: Click Import, upload a .json file or paste the JSON directly, and the pack is created.

This makes it easy to:

• Move packs between environments (staging to production)

• Share packs with your team or the community

• Keep pack definitions in version control alongside your code

What Happens When You Run a Discovery

When a user creates a project, they pick a domain pack and category. DecisionBox copies the pack's prompts and analysis areas into the project — the project gets its own independent copy. The user fills in the profile form, clicks Run Discovery, and the agent takes it from there.

A few things worth noting:

• Changing a pack doesn't change existing projects. Projects snapshot the pack at creation time, so updates to a pack only affect new projects.

• Projects can be customized independently. You can tweak a project's prompts without touching the source pack.

What's Next

• Community pack registry — browse and install packs with one click

• Pack versioning — track changes, diff, and rollback

• AI-assisted pack creation — describe your industry and let the AI draft prompts for you

Get Started

DecisionBox ships with built-in packs seeded automatically on startup.

To create your own:

1. Navigate to Domain Packs in the sidebar

2. Click New Pack

3. Fill in your categories, prompts, analysis areas, and profile schema

4. Save — and start running discoveries

Or grab a JSON file from someone who already built a pack for your industry, and Import it.

Links:

• 📖 Full blog post: https://decisionbox.io/blog/domain-packs

• 📚 Docs: Creating Domain Packs

• ⭐ Repo: https://github.com/decisionbox-io/decisionbox-platform

Happy to answer questions in the thread — especially if you're thinking about building a pack for your own industry and want a sanity check on the structure.