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Domain Analysis

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tech-leads-club
domain-analysis

Maps business domains and suggests service boundaries in any codebase using DDD Strategic Design. Use when asking "what are the domains in this codebase?", "where should I draw service boundaries?", "identify bounded contexts", "classify subdomains", "DDD analysis", or analyzing domain cohesion. Do NOT use for grouping existing components into domains (use domain-identification-grouping) or dependency analysis (use coupling-analysis).

Overview

Publishertech-leads-club
Repositoryagent-skills
Skill namedomain-analysis
Stars
6.3K
Forks
530
Bundled files
2
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by tech-leads-club on GitHub. Read the source before you install it.

Installation

Install the Domain Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Use it in TypingMind

Enable Domain Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Domain Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Domain Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Subdomain Identification & Bounded Context Analysis

This skill analyzes codebases to identify subdomains (Core, Supporting, Generic) and suggest bounded contexts following Domain-Driven Design Strategic Design principles.

When to Use

Apply this skill when:

  • Analyzing domain boundaries in any codebase
  • Identifying Core, Supporting, and Generic subdomains
  • Mapping bounded contexts from problem space to solution space
  • Assessing domain cohesion and detecting coupling issues
  • Planning domain-driven refactoring
  • Understanding business capabilities in code

Core Principles

Subdomain Classification

Core Domain: Competitive advantage, highest business value, requires best developers

  • Indicators: Complex business logic, frequent changes, domain experts needed

Supporting Subdomain: Essential but not differentiating, business-specific

  • Indicators: Supports Core Domain, moderate complexity, business-specific rules

Generic Subdomain: Common functionality, could be outsourced

  • Indicators: Well-understood problem, low differentiation, standard functionality

Bounded Context

An explicit linguistic boundary where domain terms have specific, unambiguous meanings.

  • Primary nature: Linguistic boundary, not technical
  • Key rule: Inside boundary, all Ubiquitous Language terms are unambiguous
  • Goal: Align 1 subdomain to 1 bounded context (ideal)

Analysis Process

Phase 1: Extract Concepts

Scan codebase for business concepts (not infrastructure):

  1. Entities (domain models with identity)

    • Patterns: @Entity, class, domain models
    • Focus: Business concepts, not technical classes
  2. Services (business operations)

    • Patterns: *Service, *Manager, *Handler
    • Focus: Business logic, not technical utilities
  3. Use Cases (business workflows)

    • Patterns: *UseCase, *Command, *Handler
    • Focus: Business processes, not CRUD
  4. Controllers/Resolvers (entry points)

    • Patterns: *Controller, *Resolver, API endpoints
    • Focus: Business capabilities, not technical routes

Phase 2: Group by Ubiquitous Language

For each concept, determine:

Primary Language Context

  • What business vocabulary does this belong to?
  • Examples:
    • Subscription, Invoice, Payment → Billing language
    • Movie, Video, Episode → Content language
    • User, Authentication → Identity language

Linguistic Boundaries

  • Where do term meanings change?
  • Same term, different meaning = different bounded context
  • Example: "Customer" in Sales vs "Customer" in Support

Concept Relationships

  • Which concepts naturally belong together?
  • Which share business vocabulary?
  • Which reference each other?

Phase 3: Identify Subdomains

A subdomain has:

  • Distinct business capability
  • Independent business value
  • Unique vocabulary
  • Multiple related entities working together
  • Cohesive set of business operations

Common Domain Patterns:

  • Billing/Subscription: Payments, invoices, plans
  • Content/Catalog: Media, products, inventory
  • Identity/Access: Users, authentication, authorization
  • Analytics: Metrics, dashboards, insights
  • Notifications: Messages, alerts, communications

Classify Each Subdomain:

Use this decision tree:

Is it a competitive advantage?
  YES → Core Domain
  NO → Does it require business-specific knowledge?
        YES → Supporting Subdomain
        NO → Generic Subdomain

Phase 4: Assess Cohesion

High Cohesion Indicators

  • Concepts share Ubiquitous Language
  • Concepts frequently used together
  • Direct business relationships
  • Changes to one affect others in group
  • Solve same business problem

Low Cohesion Indicators

  • Different business vocabularies mixed
  • Concepts rarely used together
  • No direct business relationship
  • Changes don't affect others
  • Solve different business problems

Cohesion Score Formula:

Score = (
  Linguistic Cohesion (0-3) +    // Shared vocabulary
  Usage Cohesion (0-3) +         // Used together
  Data Cohesion (0-2) +          // Entity relationships
  Change Cohesion (0-2)          // Change together
) / 10

8-10: High Cohesion ✅
5-7:  Medium Cohesion ⚠️
0-4:  Low Cohesion ❌

Phase 5: Detect Low Cohesion Issues

Rule 1: Linguistic Mismatch

  • Problem: Different business vocabularies mixed
  • Example: User (identity) + Subscription (billing) in same service
  • Action: Suggest separation into different bounded contexts

Rule 2: Cross-Domain Dependencies

  • Problem: Tight coupling between domains
  • Example: Service A directly instantiates entities from Domain B
  • Action: Suggest interface-based integration

Rule 3: Mixed Responsibilities

  • Problem: Single class handles multiple business concerns
  • Example: Service handling both billing and content
  • Action: Suggest splitting by subdomain

Rule 4: Generic in Core

  • Problem: Generic functionality in core business logic
  • Example: Email sending in billing service
  • Action: Extract to Generic Subdomain

Rule 5: Unclear Boundaries

  • Problem: Cannot determine which domain concept belongs to
  • Example: Entity with relationships to multiple domains
  • Action: Clarify boundaries, possibly split concept

Phase 6: Map Bounded Contexts

For each subdomain identified, suggest bounded context:

Bounded Context Characteristics:

  • Name reflects Ubiquitous Language
  • Contains complete domain model
  • Has explicit integration points
  • Clear linguistic boundary

Integration Patterns:

  • Shared Kernel: Shared model between contexts (use sparingly)
  • Customer/Supplier: Downstream depends on upstream
  • Conformist: Downstream conforms to upstream
  • Anti-corruption Layer: Translation layer between contexts
  • Open Host Service: Published interface for integration
  • Published Language: Well-documented integration protocol

Output Format

Domain Map

For each domain/subdomain:

markdown
## Domain: {Name}

**Type**: Core Domain | Supporting Subdomain | Generic Subdomain

**Ubiquitous Language**: {key business terms}

**Business Capability**: {what business problem it solves}

**Key Concepts**:

- {Concept} (Entity|Service|UseCase) - {brief description}

**Subdomains** (if applicable):

1. {Subdomain} (Core|Supporting|Generic)
   - Concepts: {list}
   - Cohesion: {score}/10
   - Dependencies: → {other domains}

**Suggested Bounded Context**: {Name}Context

- Linguistic boundary: {where terms have specific meaning}
- Integration: {how it should integrate with other contexts}

**Dependencies**:

- → {OtherDomain} via {interface/API}
- ← {OtherDomain} via {interface/API}

**Cohesion Score**: {score}/10

Cohesion Matrix

markdown
## Cross-Domain Cohesion

| Domain A | Domain B | Cohesion | Issue              | Recommendation          |
| -------- | -------- | -------- | ------------------ | ----------------------- |
| Billing  | Identity | 2/10     | ❌ Direct coupling | Use interface           |
| Content  | Billing  | 6/10     | ⚠️ Usage tracking  | Event-based integration |

Low Cohesion Report

markdown
## Issues Detected

### Priority: High

**Issue**: {description}

- **Location**: {file/class/method}
- **Problem**: {what's wrong}
- **Concepts**: {involved concepts}
- **Cohesion**: {score}/10
- **Recommendation**: {suggested fix}

### Priority: Medium

{similar format}

Bounded Context Map

markdown
## Suggested Bounded Contexts

### {ContextName}Context

**Contains Subdomains**:

- {Subdomain1} (Core)
- {Subdomain2} (Supporting)

**Ubiquitous Language**:

- Term: Definition in this context

**Integration Requirements**:

- Consumes from: {OtherContext} via {pattern}
- Publishes to: {OtherContext} via {pattern}

**Implementation Notes**:

- Separate persistence
- Independent deployment
- Explicit API boundaries

Best Practices

Do's ✅

  • Focus on business language, not code structure
  • Let Ubiquitous Language guide boundaries
  • Measure cohesion objectively
  • Identify clear integration points
  • Classify every subdomain (Core/Supporting/Generic)
  • Look for linguistic boundaries first

Don'ts ❌

  • Don't group by technical layers
  • Don't force single global model
  • Don't ignore linguistic differences
  • Don't couple domains directly
  • Don't create contexts by architecture
  • Don't eliminate all dependencies (some are necessary)

Analysis Checklist

For Each Concept:

  • What business language does it belong to?
  • What domain/subdomain is it part of?
  • Is it Core, Supporting, or Generic?
  • What other concepts does it relate to?
  • Are dependencies within same domain?
  • Any linguistic mismatches?

For Each Domain:

  • What is the Ubiquitous Language?
  • What are the key concepts?
  • What are the subdomains?
  • Which is the Core Domain?
  • What are cross-domain dependencies?
  • Is internal cohesion high?
  • Are boundaries clear?

For Cohesion Analysis:

  • Calculate cohesion scores
  • Identify low cohesion areas
  • Map cross-domain dependencies
  • Flag linguistic mismatches
  • Note tight coupling
  • Suggest boundary clarifications

Quick Reference

Subdomain Decision Tree

Analyze business capability
└─ Is it competitive advantage?
   ├─ YES → Core Domain
   └─ NO → Is it business-specific?
      ├─ YES → Supporting Subdomain
      └─ NO → Generic Subdomain

Cohesion Quick Check

Same vocabulary? → High linguistic cohesion
Used together? → High usage cohesion
Direct relationships? → High data cohesion
Change together? → High change cohesion

All high → Strong subdomain candidate
Mix of high/low → Review boundaries
All low → Likely wrong grouping

Bounded Context Signals

Clear boundary signs:
✅ Distinct Ubiquitous Language
✅ Concepts have unambiguous meaning
✅ Different meanings across contexts
✅ Clear integration points

Unclear boundary signs:
❌ Same terms with same meanings everywhere
❌ Concepts used identically across system
❌ No clear linguistic differences
❌ Tight coupling everywhere

Anti-Patterns to Avoid

Big Ball of Mud

  • Everything connected to everything
  • No clear boundaries
  • Mixed vocabularies
  • Prevention: Explicit bounded contexts

All-Inclusive Model

  • Single model for entire business
  • Impossible global definitions
  • Creates conflicts
  • Prevention: Embrace multiple contexts

Mixed Linguistic Concepts

  • Different vocabularies in same context
  • Example: User/Permission with Forum/Post
  • Prevention: Keep linguistic associations

Notes

  • This is strategic analysis, not tactical implementation
  • Focus on WHAT domains exist, not HOW to implement
  • Some cross-domain dependencies are normal
  • Low cohesion doesn't always mean "bad," it means "needs attention"
  • Generic Subdomains naturally have lower cohesion
  • Always validate with domain experts when possible

Validation Criteria

Good domain identification has:

  • ✅ Clear boundaries with distinct Ubiquitous Language
  • ✅ High internal cohesion within domains
  • ✅ Explicit cross-domain dependencies
  • ✅ Business alignment with capabilities
  • ✅ Actionable recommendations for issues

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Domain Analysis AI skill do?

Maps business domains and suggests service boundaries in any codebase using DDD Strategic Design. Use when asking "what are the domains in this codebase?", "where should I draw service boundaries?", "identify bounded contexts", "classify subdomains", "DDD analysis", or analyzing domain cohesion. Do NOT use for grouping existing components into domains (use domain-identification-grouping) or dependency analysis (use coupling-analysis).

Why use Domain Analysis on TypingMind?

Because you install it once and use it with any model. Domain Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Domain Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tech-leads-club/agent-skills/tree/main/packages/skills-catalog/skills/(architecture)/domain-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Domain Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Domain Analysis?

As many as you like. As long as a model supports skills, you can use Domain Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Domain Analysis AI skill free?

It is published on GitHub by tech-leads-club. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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