domain-analysis

name	domain-analysis
description	[Architecture] Use when you need to analyze business domain: bounded contexts, aggregates, entities, ERD, domain events, and cross-context integration.

Codex compatibility note:

• Invoke repository skills with $skill-name in Codex; this mirrored copy rewrites legacy Claude /skill-name references.
• Prefer the plan-hard skill for planning guidance in this Codex mirror.
• Task tracker mandate: BEFORE executing any workflow or skill step, create/update task tracking for all steps and keep it synchronized as progress changes.
• User-question prompts mean to ask the user directly in Codex.
• Ignore Claude-specific mode-switch instructions when they appear.
• Strict execution contract: when a user explicitly invokes a skill, execute that skill protocol as written.
• Subagent authorization: when a skill is user-invoked or AI-detected and its protocol requires subagents, that skill activation authorizes use of the required spawn_agent subagent(s) for that task.
• Do not skip, reorder, or merge protocol steps unless the user explicitly approves the deviation first.
• For workflow skills, execute each listed child-skill step explicitly and report step-by-step evidence.
• If a required step/tool cannot run in this environment, stop and ask the user before adapting.

Codex Project-Reference Loading (No Hooks)

Codex does not receive Claude hook-based doc injection. When coding, planning, debugging, testing, or reviewing, open project docs explicitly using this routing.

Always read:

• docs/project-config.json (project-specific paths, commands, modules, and workflow/test settings)
• docs/project-reference/docs-index-reference.md (routes to the full docs/project-reference/* catalog)
• docs/project-reference/lessons.md (always-on guardrails and anti-patterns)

Situation-based docs:

• Backend/CQRS/API/domain/entity changes: backend-patterns-reference.md, domain-entities-reference.md, project-structure-reference.md
• Frontend/UI/styling/design-system: frontend-patterns-reference.md, scss-styling-guide.md, design-system/README.md
• Spec/test-case planning or TC mapping: feature-docs-reference.md
• Integration test implementation/review: integration-test-reference.md
• E2E test implementation/review: e2e-test-reference.md
• Code review/audit work: code-review-rules.md plus domain docs above based on changed files

Do not read all docs blindly. Start from docs-index-reference.md, then open only relevant files for the task.

[BLOCKING] Execute skill steps in declared order. NEVER skip, reorder, or merge steps without explicit user approval. [BLOCKING] Before each step or sub-skill call, update task tracking: set in_progress when step starts, set completed when step ends. [BLOCKING] Every completed/skipped step MUST include brief evidence or explicit skip reason. [BLOCKING] If Task tools are unavailable, create and maintain an equivalent step-by-step plan tracker with the same status transitions.

Quick Summary

Goal: Analyze business domain: bounded contexts, aggregates, entities, VOs, domain events, cross-context relationships. Generate domain model report + ERD.

Workflow:

1. Load Business Context — Read idea, business evaluation, refined PBI artifacts + domain-entities-reference.md
2. Identify Bounded Contexts — Group related concepts, define context boundaries
3. Model Entities & Aggregates — Define aggregates, entities, value objects per context
4. Map Relationships — Entity relationships, cross-context integration points
5. Domain Events — Identify events crossing context boundaries
6. Generate ERD — Mermaid ER diagram with all entities and relationships
7. User Validation — Present model, ask 5-8 questions, confirm decisions
8. Domain Entity Change Assessment — Compare against domain-entities-reference.md, update if needed

Key Rules:

• MANDATORY IMPORTANT MUST ATTENTION validate every bounded context boundary with user
• MANDATORY IMPORTANT MUST ATTENTION include Mermaid ERD diagram in report
• MANDATORY IMPORTANT MUST ATTENTION run user validation interview at end (NEVER skip)
• Every entity belongs to exactly one bounded context
• Cross-context communication via domain events only — NEVER direct references

Be skeptical. Every claim needs traced proof, confidence percentages >80% to act.

---

DDD Reference: Strategic Design

Bounded Context Rules

Signal	Action
Same term, different meaning across teams	Separate bounded contexts
Different data lifecycles for same concept	Separate contexts
Different invariants on same entity	Separate contexts
Team ownership conflict (Conway's Law)	Separate contexts
Shared DB table touched by two services	Extract shared kernel or introduce ACL

Ubiquitous Language Rules:

• Every noun in codebase matches domain expert vocabulary exactly (not "User" when domain says "Employee")
• Class/method/variable names reflect ubiquitous language — zero translation layers inside bounded context
• Developers say "we call it X but domain means Y" → model is wrong, fix it

Context Map Pattern Decision Table

Situation	Pattern
Two teams, joint success/failure, equal power	Partnership
Small shared code nucleus, joint governance acceptable	Shared Kernel
Downstream can influence upstream roadmap	Customer-Supplier
Downstream has no influence on upstream	Conformist
External/legacy system with hostile or polluting model	Anti-Corruption Layer (ACL)
One upstream, many downstream consumers	Open Host Service + Published Language
Integration cost exceeds integration value	Separate Ways

ACL — when to use: Upstream is external/legacy/third-party (Salesforce, SAP, Workday). Upstream types NEVER cross ACL into domain model.

Shared Kernel — when NOT to use: Teams cannot coordinate on every change → use Customer-Supplier + Published Language instead.

---

DDD Reference: Entity vs Value Object

Decision Matrix

Question	Entity	Value Object
Has identity beyond its attributes?	YES	no
Can two instances with same data be distinct?	YES	no
Has a lifecycle (created, modified, deleted)?	YES	no
Identified by an ID in any downstream system?	YES	no
Measured or described (quantity, address, money)?	no	YES
Replaced rather than modified on change?	no	YES
Must be found independently of parent?	YES	no

Fast heuristics:

• Replace with equal-valued copy → breaks nothing? → Value Object
• Must be tracked across time or fetched by ID? → Entity
• Always retrieved as part of another object? → likely Value Object
• Two instances with same data are interchangeable? → Value Object

Canonical Value Objects

VO	Attributes	Key Invariants
Money	amount: Decimal, currency: Currency	amount ≥ 0, valid ISO currency; Add/Subtract require same currency
Email	value: string	RFC 5322 format, normalized to lowercase
Address	street, city, country, postalCode	All fields non-empty; composed of Country + PostalCode VOs
DateRange	start: DateOnly, end: DateOnly	start ≤ end; operations: Contains, Overlaps, Duration
PhoneNumber	countryCode, number	E.164 format
Percentage	value: int	0 ≤ value ≤ 100

Primitive Obsession → Value Object Mapping

Primitive Usage	Replace With
string employeeId	EmployeeId typed wrapper
decimal amount, string currency	Money { amount, currency }
string street, string city, string zip	Address { ... }
DateTime start, DateTime end	DateRange { start, end }
string email	Email { value }
int percentage	Percentage { value }
string phoneNumber	PhoneNumber { countryCode, number }

Rule: Primitive with validation rules, formatting, or always passed grouped with other primitives → missing Value Object.

Value Object Construction Pattern

// Self-validating VO — never an invalid instance in memory
public sealed class Email : PlatformValueObject<Email>
{
    private Email(string value) { Value = value; }
    public string Value { get; }

    public static Email Of(string raw)
    {
        var normalized = raw?.Trim().ToLowerInvariant();
        if (string.IsNullOrWhiteSpace(normalized) || !IsValidFormat(normalized))
            throw new DomainException($"Invalid email: {raw}");
        return new Email(normalized);
    }
}

Rules:

• No public constructor without validation — factory method (Of() / Create()) enforces invariants
• VOs can reference other VOs; VOs NEVER reference entities (lifecycle coupling)
• Mutation means replacement: email = Email.Of(newValue), NEVER email.Value = newValue

VO Persistence Strategies

Strategy	When to Use	Trade-offs
Owned types (EF Core)	VO maps to same table as owning entity	Simple, no FK, nullable columns possible
Embedded document (MongoDB)	VO stored as subdocument	Natural fit, no joins
JSON column	Complex VO, low query frequency on VO fields	Flexible, not queryable by parts
Serialized string	Simple VOs (Email, PostalCode)	Compact, unqueryable by parts

Rule: VOs NEVER have own table with primary key — that makes them entities by infrastructure.

---

DDD Reference: Entity Design

Identity Strategies

Strategy	When to Use	Trade-offs
ULID (default)	New entities in distributed system	Sortable, URL-safe, monotonic, 128-bit
UUID v4	True randomness / security-sensitive IDs	Not sortable, fragmented indexes
UUID v7	Sortable UUID needed	Time-ordered, good index locality
Natural key	Domain guarantees permanent uniqueness (SSN, EAN)	Unstable — domain can change
Surrogate int	Legacy/single-DB sequences	No distributed generation
Composite key	Relationship/join table	Harder to reference from other aggregates

Rules:

• Prefer ULID for new entities — sortable, no coordination overhead
• NEVER use email/username as PK — users change them
• Cross-service references use same ID type as the owning service

Rich vs Anemic Domain Model

Anemic (Anti-Pattern)	Rich (Correct)
Entity is data bag, logic in services	Entity contains behavior + invariants
public set on all properties	Private setters, mutation via named methods
EmployeeService.Activate(employee)	employee.Activate()
Service checks rules then mutates entity	Entity refuses invalid state transitions
Logic duplicated across services	Single authoritative location in entity

Tell Don't Ask Principle:

• BAD: if (employee.Status == Active) { employee.Status = Suspended; } (external ask + mutate)
• GOOD: employee.Suspend(reason) (entity enforces its own invariants)

Entity Invariant Enforcement

public class Employee : RootAuditedEntity<Employee, string, string>
{
    private Employee() { }  // ORM hydration only

    public static Employee Create(string name, Email email, DepartmentId departmentId)
    {
        Guard.NotNullOrWhitespace(name, nameof(name));
        Guard.NotNull(email, nameof(email));
        return new Employee
        {
            Id = Ulid.NewUlid().ToString(),
            Name = name,
            Email = email,
            DepartmentId = departmentId,
            Status = EmployeeStatus.Active
        };
    }

    public void Terminate(string reason, DateOnly terminationDate)
    {
        if (Status == EmployeeStatus.Terminated)
            throw new DomainException("Employee already terminated");
        if (terminationDate < DateOnly.FromDateTime(DateTime.UtcNow))
            throw new DomainException("Termination date cannot be in the past");

        Status = EmployeeStatus.Terminated;
        TerminationReason = reason;
        TerminationDate = terminationDate;
        AddDomainEvent(new EmployeeTerminatedDomainEvent(Id, terminationDate));
    }
}

Entity Lifecycle State Machines

Document ALL transitions explicitly. Unmodeled transitions throw DomainException.

Draft → Submitted (Submit())
Submitted → Approved (Approve(approverId))
Submitted → Rejected (Reject(reason))
Approved → Active (Activate())
Active → Suspended (Suspend(reason))
Suspended → Active (Reinstate())
Active → Archived (Archive())

Pattern	When to Use
Status enum + transition methods	Simple linear/branching lifecycles (most cases)
State pattern (class per state)	Complex per-state behavior, many states
Event sourcing	Full audit trail + point-in-time reconstruction required

Domain Validation Layers

Layer	What It Validates	Returns
Value Object	Single-value format/range invariants	Exception or Result at construction
Entity method	Aggregate consistency rules, state transitions	DomainException
Application service	Cross-aggregate rules, authorization, existence	ValidationResult / ProblemDetails
Infrastructure	DB constraints (last resort, NEVER first line)	Database exception

Decision rule:

• Rule requires loading another aggregate? → Application service
• Rule needs only data within aggregate? → Entity method
• Rule concerns single value's format? → Value Object constructor

Factory Methods on Entities

Use when: construction requires domain logic, multiple paths, raises domain events, or object graph initialization.

Naming:

• Employee.Create(...) — primary creation
• Employee.Hire(...) — semantically loaded creation (domain language)
• Private constructor — ORM hydration only, NEVER called directly

Temporal (Bi-Temporal) Entities

Two time axes: valid time (fact true in real world) + transaction time (recorded in system).

EmployeeSalary {
    validFrom: DateOnly     // valid time: salary effective from
    validTo: DateOnly       // valid time: salary effective until
    recordedAt: DateTime    // transaction time: when entered into system
}

Use when: regulatory compliance, retroactive corrections, "as-of" queries.

---

DDD Reference: Aggregate Design

Aggregate Boundary Rules

1. Invariant scope — boundary = objects needed to enforce invariants atomically
2. Transaction boundary — exactly one database transaction per aggregate operation
3. Consistency scope — must be consistent atomically? same aggregate. Eventual consistency acceptable? separate aggregates
4. Small aggregates preferred — fewer members = fewer transaction conflicts = better scalability

Aggregate Size Heuristics

Heuristic	Guideline
Default	Start with single-entity aggregate unless invariant demands more
Add member	Only when invariant requires atomic consistency across root + member
Max size	> 5 entities → redesign; likely missing sub-aggregates
Concurrent writes conflict	Reduce aggregate size

Aggregate Root Responsibilities

1. Maintain all invariants across all members
2. All mutation paths go through root (child entities NEVER directly accessible from outside)
3. Emit domain events for significant state changes
4. Control creation of child entities (factory methods on root)
5. Assign IDs to child entities

Rule: Outside code NEVER holds direct reference to non-root entity within aggregate.

Cross-Aggregate References

Rule	Detail
Reference by ID only	NEVER order.Customer.Name — load separately
No FK object navigation	CustomerId field, NEVER Customer Customer navigation property
Cross-aggregate transactions are eventual	Need them in same transaction → boundaries are wrong
Deletion cascade	Domain event → handler → compensating action in other aggregate

Aggregate Invariant Enforcement

public void AddLineItem(ProductId productId, int quantity, Money unitPrice)
{
    if (Status != OrderStatus.Draft)
        throw new DomainException("Cannot modify confirmed order");
    if (LineItems.Count >= 50)
        throw new DomainException("Order cannot exceed 50 line items");

    var item = OrderLineItem.Create(productId, quantity, unitPrice);
    _lineItems.Add(item);
    RecalculateTotal();  // invariant: Total == sum(lineItems)
}

Aggregate Design Patterns

Pattern	When to Use
Single-entity aggregate	Default — most entities are their own aggregate
Nested aggregate	Invariant requires atomic consistency across root + children
Aggregate with VOs	Root + embedded value objects (no IDs, no own table)

When to Break Aggregate Rules (Pragmatic DDD)

Situation	Acceptable Pragmatism
ORM limitation (EF owned entities)	Allow private owned collections even if not strictly necessary
Performance — 1-query load	Embed child data as VO/owned type rather than separate aggregate
Legacy schema migration	Accept cross-aggregate FK temporarily, document as debt

Rule: Breaking aggregate rules acceptable ONLY when explicitly documented as technical debt with mitigation plan.

---

DDD Reference: ERD Design

Cardinality Types

Cardinality	Mermaid	When to Use
1:1	|o--o|	Same-table extension, optional sub-type, shared lifecycle
1:N	|o--{	Parent-child, one entity owns many dependent records
M:N	}o--o{	Peer relationship; ALWAYS use explicit join/association entity

M:N rule: Relationship has attributes (date joined, role) → make join table explicit named entity.

1:1 decision: Same concept with optional attributes → same table. Different concepts with independent lifecycles → separate tables with FK.

Normalization Targets

Normal Form	Rule	Use For
1NF	Atomic values, no repeating groups	Always — baseline
2NF	No partial dependency on composite key	Composite PKs only
3NF	No transitive dependencies	OLTP — standard target
BCNF	Every determinant is a candidate key	When 3NF still has anomalies
Denormalized	Intentional redundancy	Read models, projections, OLAP

Rule: 3NF for OLTP write models; denormalize only in read models/projections with documented justification.

Identifying vs Non-Identifying Relationships

Type	FK in Child PK?	Child Existence
Identifying	YES (FK is part of PK)	Child cannot exist without parent (line item without order)
Non-identifying	NO (FK is separate column)	Child can exist independently (employee without department)

Mapping to DDD: Identifying relationship → child entity inside parent aggregate. Non-identifying FK → likely separate aggregates.

ERD for Microservices

• Each bounded context has its OWN ERD — NEVER cross-service entity arrows
• Cross-service references shown as ExternalEntityId (string/ULID) — ID only, no relationship line
• Shared data shown as replicated/synced data note, NEVER shared table
• Event-sourced aggregates: ERD shows current state projection, not event stream

ERD Anti-Patterns

Anti-Pattern	Problem	Fix
God table (50+ columns)	Every feature adds more columns	Extract sub-entities, decompose by bounded context
Cross-service FK	Direct FK across microservice schemas	Replicate needed data, use events to sync
Polymorphic association	entityType + entityId columns	Separate tables per concrete type or JSON column
EAV (Entity-Attribute-Value)	Key-value rows replacing typed columns	JSON column or explicit schema with migration
Implicit M:N (two FK cols, no PK)	Hard to add relationship attributes	Explicit join table with surrogate PK
Nullable FK everywhere	Unclear cardinality	Separate optional relationship into explicit table

ERD to Aggregate Mapping

ERD Pattern	DDD Mapping
Parent-child with identifying relationship	Child entity inside parent aggregate
Parent-child with non-identifying FK	Likely separate aggregates (independent lifecycle)
M:N join table with no extra attributes	Both sides separate aggregates, IDs in domain events
M:N join table with attributes	Association entity as separate aggregate
Strong entity + many weak dependents	Root entity + owned collection aggregate

---

DDD Reference: Domain Events

Event Naming Conventions

Format: {AggregateNoun}{PastTenseVerb} — what happened, not what to do.

Good	Bad
EmployeeTerminated	TerminateEmployee (command naming)
OrderConfirmed	OrderStatusChanged (too generic)
PaymentProcessed	PaymentComplete (not past tense)
SalaryBandUpdated	SalaryChanged (vague)

Event Payload Design Rules

Decision	Rule
Minimal vs fat	Minimal (default): AggregateId + what changed. Consumer queries for rest if needed
Fat event	Accept when: round-trip cost high AND consumers known AND staleness acceptable
Required fields always	AggregateId, OccurredOn: DateTime (UTC), Version, CorrelationId
No mutable references	Payload contains value copies, not object references

Domain Events vs Integration Events

Dimension	Domain Event	Integration Event
Scope	Within one bounded context	Across bounded contexts
Delivery	In-process, post-commit	Via message bus (RabbitMQ, Kafka)
Schema ownership	Domain owns, internal	Published Language contract
Versioning	Internal refactor freely	Versioned, backward-compatible
Failure handling	Transaction rollback	At-least-once delivery, idempotent consumer

Rule: Domain event raised → in-process handlers fire → if cross-service needed, handler publishes integration event to message bus.

Event Versioning Strategies

Strategy	Mechanism	Trade-offs
Additive only	NEVER remove/rename fields, only add	Simple, payload bloats over time
Multiple versions	OrderConfirmedV1, OrderConfirmedV2	Clear versioning, consumers handle both
Upcasting	Transform old events to new on deserialization	Transparent to consumers, complex infra

Backward compatibility rules:

• Adding optional field → backward compatible
• Removing or renaming field → breaking
• Changing field type → breaking

---

DDD Reference: Repository Pattern

Interface Design Principles

1. One repository per aggregate root — NEVER one for child entities
2. Domain language in methods — GetActiveEmployeesInDepartment() not FindAll(e => e.Status == Active)
3. Return domain objects — repositories return entities, not DTOs
4. No infrastructure concerns — no IQueryable, no DbSet, no connection strings in interface
5. Async-first — all methods return Task<T> or IAsyncEnumerable<T>

Repository vs DAO

Repository	DAO
Domain-oriented interface	Data-oriented interface
Returns entities/VOs	Returns DTOs or raw data
Used in application/domain layer	Used in infrastructure layer
Hides persistence mechanism	Often tied to persistence mechanism

Query Objects — When to Use Specification

Use when: rule used in multiple places, warrants naming + testing, needs composition.

// Static expression on entity — composable, testable
public static Expression<Func<Employee, bool>> ByDepartmentExpression(string deptId)
    => e => e.DepartmentId == deptId && e.Status == EmployeeStatus.Active;

When NOT to use Specification: Simple single-use predicate → inline lambda. Rule only used once → repository method directly.

---

DDD Reference: Anti-Patterns Quick Reference

Anti-Pattern	Detection Signal	Fix
Anemic domain model	Services have entity-specific logic; entity has all public setters	Move logic to entity
God aggregate	Aggregate > 5 entities or 100+ ms load time	Extract sub-aggregates
Leaky aggregate	External code mutates child entities directly	Private setters + root mutation methods
Primitive obsession	3+ primitives always travel together as group	Introduce Value Object
Implicit concept	Domain expert names it, code doesn't model it	Explicit class
Feature envy	Method uses more of B's data than A's	Move method to B
Getter/setter entity	All mutation via property assignment, no intent-named methods	Replace with Terminate(), Approve(), etc.
Cross-aggregate loading	Full aggregate loaded just to read one field	Pass scalar; resolve in app service
Side effects in handler	Command handler calls multiple services after save	Domain event + separate handlers
Cross-service FK	Database FK across microservice schemas	ID reference + event-driven sync
Shared Kernel overuse	Two teams, one shared model, constant coordination overhead	Split to Customer-Supplier
Missing ACL	External model types bleed into domain classes	ACL at integration boundary

---

Skill Workflow

Step 0: Locate Active Plan & Domain Reference (MANDATORY)

1. Glob plans/*$plan-hard.md sorted by modification time — find active plan directory
2. Read plan.md — project scope, goals, prior decisions
3. Read all {plan-dir}$research/*.md — avoid duplicating prior work
4. Read docs/project-reference/domain-entities-reference.md (if exists) — project's single source of truth for domain entities (read directly when relevant; do not rely on hook-injected conversation text)
5. Set {plan-dir} variable — all outputs write to this directory

If no plan directory, create using naming convention from session context.

MUST ATTENTION update {plan-dir}$plan-hard.md with domain model summary section after completing analysis.

Step 1: Load Business Context

Read artifacts from prior workflow steps (search plans/ + team-artifacts/):

• Active plan ({plan-dir}$plan-hard.md) — scope, goals, constraints
• Business evaluation report — value proposition, customer segments
• Refined PBI — acceptance criteria, user stories, features
• Discovery interview notes — problem statement, user roles

Extract and list:

• Nouns — Candidate entities (user, order, product, etc.)
• Verbs — Candidate domain events (created, approved, assigned, etc.)
• Roles — User types with different permissions/views
• Processes — Business workflows (application flow, review cycle, etc.)

Step 2: Identify Bounded Contexts

Group related entities using DDD principles. Apply context boundary signals from reference table above.

### Bounded Context: {Name}

**Purpose:** {What this context owns — one sentence}
**Classification:** Core domain / Supporting / Generic
**Key Responsibility:** {primary business capability}
**Team ownership:** {suggested team or role}
**Ubiquitous language:** {key terms and their meaning in this context}

Context boundary tests:

• Same term used by two teams with different meanings? → separate contexts
• Two entities with same name but different invariants? → separate contexts
• Can this context be worked on without understanding the other? → good separation

MANDATORY IMPORTANT MUST ATTENTION present identified contexts to user via a direct user question:

• "I identified {N} bounded contexts: {list}. Does this grouping make sense?"
• Options: Agree (Recommended) | Merge {X} and {Y} | Split {Z} | Add missing context

Step 3: Model Entities & Aggregates

Per bounded context: classify each concept using Entity vs VO matrix above, then apply aggregate boundary rules.

### {Context Name}

**Aggregate Root:** {EntityName}
**Identity:** {ULID / UUID / Natural key — justify}
**Lifecycle states:** {Draft → Active → Archived, etc.}

- **Child Entities:** {list — share aggregate boundary, identifying FK}
- **Value Objects:** {list — immutable, no identity, embedded}
- **Invariants:** {business rules this aggregate enforces atomically}
- **Factory method:** {Employee.Create(...) / Employee.Hire(...)}

**Other Aggregates in this Context:**

- {Entity} — {purpose, identity strategy, lifecycle}

Entity Detail Template

Entity	Classification	Identity	Key Fields	Lifecycle States	Invariants
{Name}	Aggregate Root / Entity / Value Object	ULID / UUID / Composite	{fields}	{states}	{business rules}

VO Composition — MUST ATTENTION verify:

• 3+ primitives always travel together? → extract VO
• String field has format rules? → extract VO (Email, PhoneNumber)
• Numeric field has range rules? → extract VO (Percentage, Money)
• Concept named by domain experts but not modeled? → make explicit class

Aggregate Boundary — MUST ATTENTION verify:

• All entities in aggregate enforced in one transaction?
• Cross-aggregate references use ID only (no navigation properties)?
• Aggregate root is sole entry point for ALL mutations?
• Aggregate ≤ 5 entities? If not — justify or decompose

Step 4: Map Relationships

Intra-Context Relationships

| From | To        | Type | Cardinality     | Relationship Kind       | Description              |
| ---- | --------- | ---- | --------------- | ----------------------- | ------------------------ |
| Job  | Candidate | M:N  | via Application | Non-identifying (assoc) | Candidates apply to jobs |

Apply identifying vs non-identifying rule:

• Identifying relationship (child FK is part of PK) → child entity inside parent aggregate
• Non-identifying FK → likely separate aggregates

Cross-Context Integration (Context Map)

| Upstream Context | Downstream Context | Pattern | Integration Point          | Sync Mechanism |
| ---------------- | ------------------ | ------- | -------------------------- | -------------- |
| Recruitment      | Employee           | ACL     | Candidate becomes Employee | Domain event   |

Integration patterns to apply (see context map reference table above):

• Anti-Corruption Layer — external/hostile upstream model
• Customer-Supplier — downstream can negotiate with upstream
• Open Host Service — one upstream, many consumers
• Conformist — no negotiating power with upstream

Step 5: Domain Events

Identify events crossing bounded context boundaries. Apply naming: {AggregateNoun}{PastTenseVerb}.

Event	Source Context	Target Context(s)	Payload (minimal)	Trigger
CandidateHired	Recruitment	Employee, Onboarding	candidateId, jobId, hireDate	Offer accepted

Event design — MUST ATTENTION verify:

• Name is past tense + includes aggregate noun?
• Payload includes AggregateId, OccurredOn (UTC), Version?
• Payload minimal — just what changed + correlation IDs?
• Version field included for schema evolution?
• Domain event vs integration event clearly classified?

Step 6: Generate ERD

Produce Mermaid ER diagram. Apply ERD-to-Aggregate mapping + anti-patterns from reference above.

```mermaid
erDiagram
 %% Bounded Context: {Name}
 ENTITY_A ||--o{ ENTITY_B: "has many"
 ENTITY_A {
 string id PK
 string name
 string status
 datetime createdAt
 }
 ENTITY_B {
 string id PK
 string entityAId FK
 string externalServiceId "ID only - no FK across services"
 string type
 }
```

ERD requirements:

• Group entities by bounded context (use Mermaid comments %% Context: ...)
• Show PK/FK fields and key business fields (not all fields)
• Show cardinality (1:1, 1:N, M:N) using Crow's Foot notation
• Cross-service references: string/ULID field with comment, no relationship line
• Identify association entities for M:N relationships

ERD — MUST ATTENTION verify before finalizing:

• No God tables (>20 columns — consider decomposing)?
• No cross-service FK arrows?
• All M:N through explicit named join entity?
• No nullable FK everywhere (clarify optional relationships)?

Step 7: User Validation Interview

MANDATORY IMPORTANT MUST ATTENTION present domain model and ask 5-8 questions via a direct user question:

Required Questions

1. Context boundaries — "Are these {N} bounded contexts correct? Any missing or misplaced?"
   • Options: Correct (Recommended) | Need changes | Not sure, explain more
2. Aggregate roots — "Is {Entity} the right aggregate root for {Context}? It controls {child entities}."
3. Entity vs VO — "I modeled {concept} as a Value Object because it has no independent identity. Does that match the business model?"
4. Relationship verification — "The {Entity A} to {Entity B} relationship is {type/cardinality}. Is that correct?"
5. Missing entities — "Are there business concepts — workflows, roles, rules — I haven't captured as explicit domain objects?"
6. Event verification — "When {event} happens, should {contexts} be notified? What data do they need?"

Deep-Dive Questions (pick 2-3 based on complexity)

• "Should {Entity} be a separate aggregate or part of {Aggregate}? [separating = eventual consistency between them]"
• "Is {field} really a Value Object or does it need its own identity and lifecycle?"
• "How does {process} work step-by-step? What state transitions are involved?"
• "What happens when {edge case}? Which invariant prevents the bad state?"
• "Which entities change most frequently under concurrent load? (impacts aggregate design)"
• "Any concepts domain experts name that I haven't modeled explicitly?"

After user confirms, update report with final decisions and mark as status: confirmed.

Step 8: Domain Entity Change Assessment (MANDATORY)

MANDATORY IMPORTANT MUST ATTENTION compare domain analysis results against docs/project-reference/domain-entities-reference.md (if exists):

1. Identify new entities — in analysis but not in reference doc
2. Identify modified entities — changed fields, relationships, or bounded context assignment
3. Identify deprecated entities — in reference doc but no longer needed by feature
4. Present changes to user via a direct user question:
   • "Domain entity changes detected: {N} new, {N} modified, {N} deprecated. Proceed with updating domain-entities-reference.md?"
   • Options: Approve all (Recommended) | Review each change | Skip update

If docs/project-reference/domain-entities-reference.md does NOT exist, ask user:

• "No domain-entities-reference.md found. Create it with all entities from this analysis?"
• Options: Yes, create it (Recommended) | No, skip

After user confirms: update/create docs/project-reference/domain-entities-reference.md following existing format. Append new entities to appropriate bounded context section. Update field lists + relationships for modified entities.

Step 9: Update Main Plan (MANDATORY)

Read {plan-dir}$plan-hard.md, append/update ## Domain Model section:

## Domain Model

- **Bounded Contexts:** {N} — {list names with context map patterns between them}
- **Total Entities:** {N} ({N} aggregates, {N} child entities, {N} value objects)
- **Domain Events:** {N} cross-context events
- **Key Aggregates:** {list with invariant summaries}
- **Key Relationships:** {summary of critical relationships}
- **ERD:** See `phase-01-domain-model.md`
- **Full Analysis:** See `research/domain-analysis.md`

---

Output

{plan-dir}$research/domain-analysis.md          # Full domain analysis report
{plan-dir}/phase-01-domain-model.md             # Confirmed domain model with ERD
{plan-dir}$plan-hard.md                              # Updated with domain model summary
docs/project-reference/domain-entities-reference.md  # Updated/created with new/modified entities

Report structure:

1. Executive summary (bounded contexts + entity count + key decisions)
2. Context map (bounded contexts with integration patterns)
3. Per-context entity/aggregate detail with identity strategy and invariants
4. Relationship tables (intra + cross-context, identifying vs non-identifying)
5. Value Objects catalog (VO type, attributes, invariants)
6. Domain events catalog (with payload design)
7. Mermaid ERD diagram
8. Anti-pattern warnings (any detected issues in model)
9. Unresolved questions

Report must be ≤250 lines. Use tables over prose.

---

MANDATORY IMPORTANT MUST ATTENTION break work into small todo tasks using task tracking BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION validate EVERY bounded context and key relationship with user via a direct user question. MANDATORY IMPORTANT MUST ATTENTION include Mermaid ERD and confidence % for all architectural decisions. MANDATORY IMPORTANT MUST ATTENTION add a final review todo task to verify work quality.

---

Next Steps

MANDATORY IMPORTANT MUST ATTENTION — NO EXCEPTIONS after completing this skill, use a direct user question to present these options:

• "$review-domain-entities (Recommended)" — Review DDD quality of entities modeled/modified in this analysis (anemic model, VO classification, invariant enforcement, aggregate boundaries)
• "$tech-stack-research" — Research tech stack based on domain model
• "$plan-hard" — If tech stack already decided and entity quality already reviewed
• "Skip, continue manually" — user decides

---

MANDATORY IMPORTANT MUST ATTENTION use task tracking to break ALL work into small tasks BEFORE starting. MANDATORY IMPORTANT MUST ATTENTION use a direct user question at EVERY decision point — validate every bounded context and entity relationship with user. MANDATORY IMPORTANT MUST ATTENTION produce ERD diagram (Mermaid) and domain model report with confidence %.

External Memory: For complex or lengthy work (research, analysis, scan, review), write intermediate findings and final results to a report file in plans/reports/ — prevents context loss and serves as deliverable.

Evidence Gate: MANDATORY IMPORTANT MUST ATTENTION — every claim, finding, and recommendation requires file:line proof or traced evidence with confidence percentage (>80% to act, <80% must verify first).

---

AI Mistake Prevention — Failure modes to avoid on every task:

Check downstream references before deleting. Deleting components causes documentation and code staleness cascades. Map all referencing files before removal. Verify AI-generated content against actual code. AI hallucinates APIs, class names, and method signatures. Always grep to confirm existence before documenting or referencing. Trace full dependency chain after edits. Changing a definition misses downstream variables and consumers derived from it. Always trace the full chain. Trace ALL code paths when verifying correctness. Confirming code exists is not confirming it executes. Always trace early exits, error branches, and conditional skips — not just happy path. When debugging, ask "whose responsibility?" before fixing. Trace whether bug is in caller (wrong data) or callee (wrong handling). Fix at responsible layer — never patch symptom site. Assume existing values are intentional — ask WHY before changing. Before changing any constant, limit, flag, or pattern: read comments, check git blame, examine surrounding code. Verify ALL affected outputs, not just the first. Changes touching multiple stacks require verifying EVERY output. One green check is not all green checks. Holistic-first debugging — resist nearest-attention trap. When investigating any failure, list EVERY precondition first (config, env vars, DB names, endpoints, DI registrations, data preconditions), then verify each against evidence before forming any code-layer hypothesis. Surgical changes — apply the diff test. Bug fix: every changed line must trace directly to the bug. Don't restyle or improve adjacent code. Enhancement task: implement improvements AND announce them explicitly. Surface ambiguity before coding — don't pick silently. If request has multiple interpretations, present each with effort estimate and ask. Never assume all-records, file-based, or more complex path.

Critical Thinking Mindset — Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination.

MUST ATTENTION apply critical thinking — every claim needs traced proof, confidence >80% to act. Anti-hallucination: never present guess as fact.

MUST ATTENTION apply AI mistake prevention — holistic-first debugging, fix at responsible layer, surface ambiguity before coding, re-read files after compaction.

Prompt-Enhance Closing Anchors

IMPORTANT MUST ATTENTION follow declared step order for this skill; NEVER skip, reorder, or merge steps without explicit user approval IMPORTANT MUST ATTENTION for every step/sub-skill call: set in_progress before execution, set completed after execution IMPORTANT MUST ATTENTION every skipped step MUST include explicit reason; every completed step MUST include concise evidence IMPORTANT MUST ATTENTION if Task tools unavailable, maintain an equivalent step-by-step plan tracker with synchronized statuses

Closing Reminders

MUST ATTENTION task tracking ALL tasks BEFORE starting — never begin without task breakdown MUST ATTENTION validate EVERY bounded context + key relationship with user via a direct user question — never auto-decide MUST ATTENTION domain events ALWAYS follow {AggregateNoun}{PastTenseVerb} naming — NEVER command-style MUST ATTENTION NEVER have cross-service FK in ERD — ID reference + event-driven sync only MUST ATTENTION entity vs VO classification: replace with equal-valued copy breaks nothing? → VO. Else → Entity MUST ATTENTION add final review task to verify work quality

[TASK-PLANNING] Before acting, analyze task scope and systematically break it into small todo tasks and sub-tasks using task tracking.

[IMPORTANT] Analyze how big the task is and break it into many small todo tasks systematically before starting — this is very important.

Hookless Prompt Protocol Mirror (Auto-Synced)

Source: .claude/hooks/lib/prompt-injections.cjs + .claude/.ck.json

[WORKFLOW-EXECUTION-PROTOCOL] [BLOCKING] Workflow Execution Protocol — MANDATORY IMPORTANT MUST CRITICAL. Do not skip for any reason.

1. DETECT: Match prompt against workflow catalog
2. ANALYZE: Find best-match workflow AND evaluate if a custom step combination would fit better
3. ASK (REQUIRED FORMAT): Use a direct user question with this structure:
   • Question: "Which workflow do you want to activate?"
   • Option 1: "Activate [BestMatch Workflow] (Recommended)"
   • Option 2: "Activate custom workflow: [step1 → step2 → ...]" (include one-line rationale)
4. ACTIVATE (if confirmed): Call $workflow-start <workflowId> for standard; sequence custom steps manually
5. CREATE TASKS: task tracking for ALL workflow steps
6. EXECUTE: Follow each step in sequence [CRITICAL-THINKING-MINDSET] Apply critical thinking, sequential thinking. Every claim needs traced proof, confidence >80% to act. Anti-hallucination principle: Never present guess as fact — cite sources for every claim, admit uncertainty freely, self-check output for errors, cross-reference independently, stay skeptical of own confidence — certainty without evidence root of all hallucination. AI Attention principle (Primacy-Recency): Put the 3 most critical rules at both top and bottom of long prompts/protocols so instruction adherence survives long context windows.

Learned Lessons

Lessons Learned

[CRITICAL] Hard-won project debugging/architecture rules. MUST ATTENTION apply BEFORE forming hypothesis or writing code.

Quick Summary

Goal: Prevent recurrence of known failure patterns — debugging, architecture, naming, AI orchestration, environment.

Top Rules (apply always):

• MUST ATTENTION verify ALL preconditions (config, env, DB names, DI regs) BEFORE code-layer hypothesis
• MUST ATTENTION fix responsible layer — NEVER patch symptom sites with caller-specific defensive code
• MUST ATTENTION use ExecuteInjectScopedAsync for parallel async + repo/UoW — NEVER ExecuteUowTask
• MUST ATTENTION name by PURPOSE not CONTENT — adding member forces rename = abstraction broken
• MUST ATTENTION persist sub-agent findings incrementally after each file — NEVER batch at end
• MUST ATTENTION Windows bash: verify Python alias (where python/where py) — NEVER assume python/python3 resolves

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Debugging & Root Cause Reasoning

• [2026-04-11] Holistic-first: verify environment before code. Failure → list ALL preconditions (config, env vars, DB names, endpoints, DI regs, credentials, permissions, data prerequisites) → verify each via evidence (grep/cat/query) BEFORE code-layer hypothesis. Worst rabbit holes: diving nearest layer while bug sits elsewhere — e.g., hours debugging "sync timeout", real cause: test appsettings pointing wrong DB. Cheapest check first.
• [2026-04-01] Ask "whose responsibility?" before fixing. Trace: bug in caller (wrong data) or callee (wrong handling)? Fix responsible layer — NEVER patch symptom site masking real issue.
• [2026-04-01] Trace data lifecycle, not error site. Follow data: creation → transformation → consumption. Bug usually where data created wrong, not consumed.
• [2026-04-01] Code is caller-agnostic. Functions/handlers/consumers don't know who invokes them. Comments/guards/messages describe business intent — NEVER reference specific callers (tests, seeders, scripts).

Architecture Invariants

• [2026-03-31] ParallelAsync + repo/UoW MUST use ExecuteInjectScopedAsync, NEVER ExecuteUowTask. ExecuteUowTask creates new UoW but reuses outer DI scope (same DbContext) — parallel iterations sharing non-thread-safe DbContext silently corrupt data. ExecuteInjectScopedAsync creates new UoW + new DI scope (fresh repo per iteration).
• [2026-03-31] Bus message naming MUST include service name prefix — core services NEVER consume feature events. Prefix declares schema ownership (AccountUserEntityEventBusMessage = Accounts owns). Core services (Accounts, Communication) are leaders. Feature services (Growth, Talents) sending to core MUST use {CoreServiceName}...RequestBusMessage — never define own event for core to consume.

Naming & Abstraction

• [2026-04-12] Name PURPOSE not CONTENT — "OrXxx" anti-pattern. HrManagerOrHrOrPayrollHrOperationsPolicy names set members, not what it guards. Add role → rename = broken abstraction. Rule: names express DOES/GUARDS, not CONTAINS. Test: adding/removing member forces rename? YES = content-driven = bad → rename to purpose (e.g., HrOperationsAccessPolicy). Nuance: "Or" fine in behavioral idioms (FirstOrDefault, SuccessOrThrow) — expresses HAPPENS, not membership.

Environment & Tooling

• [2026-04-20] Windows bash: NEVER assume python/python3 resolves — verify alias first. Python may not be in bash PATH under those names. Check: where python / where py. Prefer py (Windows Python Launcher) for one-liners, node if JS alternative exists.

Test-specific lessons → docs/project-reference/integration-test-reference.md Lessons Learned section. Production-code anti-patterns → docs/project-reference/backend-patterns-reference.md Anti-Patterns section. Generic debugging/refactoring reminders → System Lessons in .claude/hooks/lib/prompt-injections.cjs.

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Closing Reminders

• IMPORTANT MUST ATTENTION holistic-first: verify ALL preconditions (config, env, DB names, endpoints, DI regs) BEFORE code-layer hypothesis — cheapest check first
• IMPORTANT MUST ATTENTION fix responsible layer — NEVER patch symptom site; trace caller (wrong data) vs callee (wrong handling), fix root owner
• IMPORTANT MUST ATTENTION parallel async + repo/UoW → ALWAYS ExecuteInjectScopedAsync, NEVER ExecuteUowTask (shared DbContext = silent data corruption)
• IMPORTANT MUST ATTENTION bus message prefix = schema ownership; feature services NEVER define events for core services — use {CoreServiceName}...RequestBusMessage
• IMPORTANT MUST ATTENTION name by PURPOSE — adding/removing member forces rename = broken abstraction
• IMPORTANT MUST ATTENTION sub-agents MUST write findings after each file/section — NEVER batch all findings into one final write
• IMPORTANT MUST ATTENTION Windows bash: NEVER assume python/python3 resolves — run where python/where py first, use py launcher or node

[LESSON-LEARNED-REMINDER] [BLOCKING] Task Planning & Continuous Improvement — MANDATORY. Do not skip.

Break work into small tasks (task tracking) before starting. Add final task: "Analyze AI mistakes & lessons learned".

Extract lessons — ROOT CAUSE ONLY, not symptom fixes:

1. Name the FAILURE MODE (reasoning/assumption failure), not symptom — "assumed API existed without reading source" not "used wrong enum value".
2. Generality test: does this failure mode apply to ≥3 contexts/codebases? If not, abstract one level up.
3. Write as a universal rule — strip project-specific names/paths/classes. Useful on any codebase.
4. Consolidate: multiple mistakes sharing one failure mode → ONE lesson.
5. Recurrence gate: "Would this recur in future session WITHOUT this reminder?" — No → skip $learn.
6. Auto-fix gate: "Could $code-review/$code-simplifier/$security/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [TASK-PLANNING] [MANDATORY] BEFORE executing any workflow or skill step, create/update task tracking for all planned steps, then keep it synchronized as each step starts/completes.