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.
• 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 uses static project-reference loading instead of runtime-injected project docs. 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)

Missing/stale context route: If docs/project-config.json, the docs index, lessons.md, CLAUDE.md, AGENTS.md, or any task-required reference doc is missing or stale, auto-run $project-init or the narrow setup route ($project-config, $docs-init, $scan-all, $scan --target=<key>, $claude-md-init) before ordinary project-specific work. If Codex mirrors or AGENTS.md are missing/stale, ask the user to run $sync-codex; do not auto-run it.

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 authoring, docs/specs/ pathing, or TC format: feature-spec-reference.md, spec-system-reference.md, spec-principles.md
• Behavior/public-contract changes or spec-test-code sync: workflow-spec-test-code-cycle-reference.md plus the spec docs above
• Derived spec indexes/ERDs/reimplementation guides: spec-system-reference.md and source Feature Specs under docs/specs/
• 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) and generate a domain model report + ERD — producing a user-validated DDD domain model with correct bounded contexts, aggregate boundaries, and event flows so downstream implementation builds on the right invariants and avoids costly boundary rework after consumers depend on them.

Summary:

• Drive the model from business artifacts, not guesses: load plan/PBI/business-eval inputs and domain-entities-reference.md, then extract nouns→entities, verbs→events, roles, and processes before classifying anything.
• Every concept passes the Entity-vs-VO matrix and aggregate boundary rules (≤5 entities, one transaction, reference-by-ID only, root is the sole mutation entry) — flag primitive obsession and anemic models as you go.
• User validation is non-skippable: present bounded contexts and the Mermaid ERD, then run the 5-8 question a direct user question interview to confirm boundaries, aggregate roots, and event flows before marking the model confirmed.
• Close the loop on persistence: reconcile findings against domain-entities-reference.md (new/modified/deprecated), update the ## Domain Model section of plan.md, and keep cross-context communication event-driven with {AggregateNoun}{PastTenseVerb} naming and no cross-service FKs.

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 the domain says "Customer")
• 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 orderId	OrderId 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

A VO is self-validating: invariants enforced at construction via a factory (no public constructor that can produce an invalid instance), immutable, and equality-by-value. The base class and factory names below are one illustrative instantiation — translate to your language's equivalents.

Example (illustrative — adapt to your language):

// Self-validating VO — never an invalid instance in memory
public sealed class Email : ValueObject<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 store	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
OrderService.Confirm(order)	order.Confirm()
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 (order.Status == Confirmed) { order.Status = OnHold; } (external ask + mutate)
• GOOD: order.Hold(reason) (entity enforces its own invariants)

Entity Invariant Enforcement

A rich entity guards its own state: a private constructor reserved for ORM/persistence hydration, named factory methods for valid creation, and intent-named mutation methods that reject invalid transitions and emit domain events. The base class, guard helper, and ID generator below are one illustrative instantiation — substitute your language's equivalents.

Example (illustrative — adapt to your language):

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

    public static Order Create(string name, Email email, WarehouseId warehouseId)
    {
        Guard.NotNullOrWhitespace(name, nameof(name));
        Guard.NotNull(email, nameof(email));
        return new Order
        {
            Id = Ulid.NewUlid().ToString(),
            Name = name,
            Email = email,
            WarehouseId = warehouseId,
            Status = OrderStatus.Confirmed
        };
    }

    public void Cancel(string reason, DateOnly cancellationDate)
    {
        if (Status == OrderStatus.Cancelled)
            throw new DomainException("Order already cancelled");
        if (cancellationDate < DateOnly.FromDateTime(DateTime.UtcNow))
            throw new DomainException("Cancellation date cannot be in the past");

        Status = OrderStatus.Cancelled;
        CancellationReason = reason;
        CancellationDate = cancellationDate;
        AddDomainEvent(new OrderCancelledDomainEvent(Id, cancellationDate));
    }
}

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	Failure Signal (per stack)
Value Object	Single-value format/range invariants	Construction failure (raised error or result type)
Entity method	Aggregate consistency rules, state transitions	Domain rule violation (e.g. DomainException)
Application service	Cross-aggregate rules, authorization, existence	Structured validation result (e.g. ValidationResult / problem-details payload)
Infrastructure	DB constraints (last resort, NEVER first line)	Persistence-layer error (last-resort constraint)

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:

• Order.Create(...) — primary creation
• Order.Place(...) — 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).

ProductPrice {
    validFrom: DateOnly     // valid time: price effective from
    validTo: DateOnly       // valid time: price 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

All mutation flows through the aggregate root, which checks every invariant before applying a change and recomputes derived state so no member can be left inconsistent. The throw-on-violation idiom below is one illustrative instantiation — your language may surface invariant breaches differently (exceptions, result types).

Example (illustrative — adapt to your language):

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 (order without warehouse)

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
OrderCancelled	CancelOrder (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 (UTC timestamp), 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 configured message bus or event stream
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 — GetConfirmedOrdersInWarehouse() not FindAll(e => e.Status == Active)
3. Return domain objects — repositories return entities, not DTOs
4. No infrastructure concerns — no leaked query/ORM types or connection strings in the interface.
5. Async-first — methods return the configured runtime's async primitive.

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.

A specification names a query predicate as a reusable, composable, testable unit owned by the domain. The expression-tree form below is one illustrative instantiation — your language may model it as a predicate function, query builder, or specification object.

Example (illustrative — adapt to your language):

// Static expression on entity — composable, testable
public static Expression<Func<Order, bool>> ByWarehouseExpression(string warehouseId)
    => o => o.WarehouseId == warehouseId && o.Status == OrderStatus.Confirmed;

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 Cancel(), 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.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
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.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.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:** {Order.Create(...) / Order.Place(...)}

**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             |
| ----- | ------- | ---- | ------------- | ----------------------- | ----------------------- |
| Order | Product | M:N  | via OrderLine | Non-identifying (assoc) | Orders contain products |

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 |
| ---------------- | ------------------ | ------- | ------------------ | -------------- |
| Sales            | Order              | ACL     | Cart becomes Order | 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
OrderPlaced	Sales	Order, Fulfillment	cartId, productId, placedDate	Checkout completed

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.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.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" — If tech stack already decided and entity quality already reviewed
• "Skip, continue manually" — user decides

Council escalation (always-offer, second prompt)

After the existing ## Next Steps prompt above resolves, present a second, independent a direct user question call (do NOT merge into the first):

• "Skip council — proceed with model (Recommended)" — Continue with the bounded contexts / aggregate boundaries as drawn. Recommended default.
• "Escalate to $llm-council" — Run 11 sub-agent council (5 advisors + 5 reviewers + chairman). Best applied when bounded-context splits or aggregate boundaries are contested (multiple defensible cuts), the model touches >=3 services, or invariants span aggregates. DDD boundary decisions are hard to reverse once consumers depend on them. Cheaper alternatives: $why-review, $plan-validate (run these first if you haven't).

---

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:

Re-read files after context changes. Context compaction, resume, or long-running work can make memory stale; verify current files before acting. Verify generated content against source evidence. AI hallucinates APIs, names, claims, and document facts. Check the relevant source before documenting or referencing. Check downstream references before deleting or renaming. Removing an artifact can stale docs, generated mirrors, configs, and callers; map references first. Trace the full impact chain after edits. Changing a definition can miss derived outputs and consumers. Follow the affected chain before declaring done. Verify ALL affected outputs, not just the first. One green check is not all green checks; validate every output surface the change can affect. Assume existing values are intentional — ask WHY before changing. Before changing a constant, limit, flag, wording, or pattern, read nearby context and history. Surface ambiguity before acting — don't pick silently. Multiple valid interpretations require an explicit question or stated assumption with risk. Keep shared guidance role-relevant. Universal guidance must help every receiving skill or agent; code-specific obligations belong only in code-specific protocols.

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 + sequential thinking — every claim needs appropriate traced evidence (file:line for repo/code claims; source URL or artifact section for research, product, content, and docs claims); confidence >80% to act, <60% DO NOT recommend. Anti-hallucination: never present guess as fact, admit uncertainty freely, cross-reference independently, stay skeptical of own confidence.

MUST ATTENTION apply AI mistake prevention — verify generated content against evidence, trace downstream references before deleting or renaming, verify all affected outputs, re-read files after context loss, and surface ambiguity before acting.

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

IMPORTANT MUST ATTENTION Goal: Produce a user-validated DDD domain model — correct bounded contexts, aggregate boundaries, and event flows — so downstream implementation builds on the right invariants and avoids costly boundary rework after consumers depend on them.

Protocols in force (concise digest of the SYNC/shared blocks this skill carries):

• AI Mistake Prevention: verify generated content against evidence, trace downstream references, verify all affected outputs, re-read after context loss, surface ambiguity.
• Critical Thinking: Traced proof per claim; confidence >80% to act, NEVER guess as fact.

IMPORTANT MUST ATTENTION validate EVERY bounded context + key relationship with user via a direct user question — NEVER auto-decide a boundary — why: DDD boundaries are hard to reverse once consumers depend on them; one wrong cut costs days of rework. IMPORTANT MUST ATTENTION domain events ALWAYS follow {AggregateNoun}{PastTenseVerb} naming — NEVER command-style (CancelOrder) or generic (OrderStatusChanged) — why: command/generic names hide what happened and break consumer routing. IMPORTANT MUST ATTENTION NEVER place cross-service FK in the ERD — use ID reference ({Entity}Id string/ULID) + event-driven sync only — why: cross-service FK couples schemas and blocks independent deployment.

MUST ATTENTION task tracking ALL tasks BEFORE the first artifact read or write; mark one in_progress, complete immediately after evidence; on context loss the current task list first — why: compaction wipes prior-work memory, duplicated tasks waste budget. MUST ATTENTION load business artifacts FIRST (plan/PBI/business-eval + domain-entities-reference.md) and derive nouns→entities, verbs→events — NEVER model from guesses — why: a model built on assumptions encodes the wrong invariants. MUST ATTENTION search 3+ existing entities in domain-entities-reference.md before introducing a new one; reconcile new/modified/deprecated against it and follow its existing format — why: divergent or duplicated domain models fragment the source of truth. MUST ATTENTION evaluate fit before reusing a nearby aggregate/VO pattern — verify the new concept shares the same identity, lifecycle, and transaction scope — why: closest example ≠ matching preconditions. MUST ATTENTION entity vs VO classification — replace with equal-valued copy breaks nothing? → Value Object. Tracked across time or fetched by ID? → Entity. Flag primitive obsession (3+ primitives travel together) and anemic models as you go. MUST ATTENTION every aggregate passes boundary rules — ≤5 entities, one transaction, reference-by-ID only, root is the sole mutation entry; >5 entities → decompose or justify as documented debt. MUST ATTENTION include the Mermaid ERD and a confidence % (>80% to act, <80% verify first) for EVERY architectural decision; cite file:line / artifact evidence — NEVER present a boundary or classification as fact without traced proof. MUST ATTENTION persist intermediate findings to plans/reports/ incrementally and add a final review task to verify work quality — why: long analysis hits context cutoffs; batched writes lose findings.

Anti-Rationalization:

Evasion	Rebuttal
"Boundaries are obvious, skip user validation"	User validation is non-skippable — run the 5-8 question a direct user question interview.
"Already know the entities, skip reference doc"	Show file:line from domain-entities-reference.md. No proof = not checked.
"This concept is clearly an entity"	Run the Entity-vs-VO matrix anyway — state confidence %. Pattern-matching skips context.
"Small model, skip task tracking"	Still task tracking first. Skip depth, never skip tracking.
"Cross-service link is just one FK"	Never — ID reference + event-driven sync. One FK couples two schemas permanently.

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

IMPORTANT MUST ATTENTION validate EVERY bounded context with the user, name domain events {AggregateNoun}{PastTenseVerb}, and keep cross-service links as ID-reference + events — these three are the most-skipped, highest-blast-radius rules of this skill.

[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/.ck.json + .claude/skills/shared/sync-inline-versions.md (:full blocks) + .claude/scripts/lib/hookless-prompt-protocol.cjs

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

Generic portability boundary: Reusable skills and protocol text stay project-neutral; project-specific conventions are discovered from docs/project-config.json and docs/project-reference/. Apply shared AI-SDD from shared/sdd-artifact-contract.md. Read docs/project-config.json and docs/project-reference/docs-index-reference.md, then open the project reference docs named there. For spec, test-case, behavior-change, public-contract, or docs/specs/ work, route through the local spec docs named by the docs index: feature-spec-reference.md, spec-system-reference.md, spec-principles.md, and workflow-spec-test-code-cycle-reference.md when specs/tests/code must stay synchronized. If either file or a required reference doc is missing or stale, auto-run $project-init (or the narrow lower-level route such as $project-config, $docs-init, $scan-all, or $scan --target=<key>) before ordinary project-specific work. Any supported AI tool may execute when this shared context and local docs are available.

1. DETECT: If the prompt starts with an explicit slash skill/workflow command, execute it directly. Otherwise match the prompt against the workflow catalog and skill list.
2. ANALYZE: Choose the best option: execute directly, invoke a skill, activate a standard workflow, or compose a custom step combination.
3. AUTO-SELECT: Pick the best option yourself. Do not ask the user to choose between direct execution, skill, standard workflow, or custom workflow.
4. ACTIVATE: For a selected workflow, call $start-workflow <workflowId>; for a selected skill, invoke that skill; for a custom workflow, sequence custom steps directly; for direct execution, proceed with the task.
5. CREATE TASKS: task tracking for ALL workflow/skill/custom steps before execution when the selected path has multiple steps.
6. EXECUTE: Advance per the Workflow Step Advancement & Parallel Phases rule in your context instructions — model-driven; a sub-agent completion advances a step identically to an inline call; a parallel-phase group is an all-return barrier (advance only after ALL members return, never serialize it)

Shared AI-SDD Protocol Markers

Source: .claude/skills/shared/sync-inline-versions.md

SYNC:ai-sdd-artifact-contract

AI-SDD Artifact Contract — Shared spec-driven development rules stay portable and source-owned.

1. Keep reusable AI-SDD principles in .claude; put repository-specific paths, commands, owners, products, and formats in project config/reference docs.
2. Preserve cycle: spec -> plan -> tasks -> implement -> verify -> update spec/docs.
3. Trace every requirement or invariant through decision, task, TC/test, source evidence, and docs/spec update.
4. Treat code-to-spec extraction as reference-only until accepted by the canonical spec owner.
5. Any supported AI tool may plan, implement, review, or verify with synced context; using multiple tools is optional.
6. Update .claude source first, then sync generated mirrors; do not manually edit .agents, .codex, or AGENTS.md. — why: mirrors are generated artifacts; hand-edits are overwritten on the next sync
7. If docs/project-config.json, root instruction files, or a required project-reference doc is missing or stale, auto-run $project-init or the narrow lower-level route before ordinary project-specific work.

Active reference: shared/sdd-artifact-contract.md in the active skills root.

---

SYNC:ai-sdd-artifact-contract:reminder

• MANDATORY Apply shared/sdd-artifact-contract.md; keep reusable AI-SDD in .claude and local rules in project docs.
• MANDATORY Code-to-spec extraction is reference-only until canonical acceptance; any supported AI tool may execute with synced context.
• MANDATORY Update .claude source before syncing generated mirrors; do not manually edit .agents, .codex, or AGENTS.md.
• MANDATORY Missing or stale project config, root instruction files, or required reference docs route project-specific work through $project-init or the narrow setup route automatically. [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.

[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/$security-review/$lint catch this?" — Yes → improve review skill instead.
7. BOTH gates pass → ask user to run $learn. [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. Goal-driven execution: Define success criteria first, loop until verified, and stop only when observable checks pass. Tests verify intent: Tests must protect business rules/invariants and fail when the protected intent breaks, not only mirror current behavior.

Common AI Mistake Prevention (System Lessons)

• Re-read files after context compaction. Edit requires prior Read in same context; compaction wipes read state. Re-read before editing.
• Grep for old terms after bulk replacements. AI over-trusts find/replace completeness. Grep full repo after bulk edits for missed refs in docs/configs/catalogs.
• Check downstream references before deleting. Deletions cascade doc/code staleness. Map referencing files before removal.
• After memory loss, check existing state before creating new. Compaction wipes prior-work memory. Query current state to resume — never blindly duplicate.
• Verify AI-generated content against actual code. AI hallucinates APIs, class names, method signatures. Grep to confirm existence before documenting/referencing.
• Trace full dependency chain after edits. Changing a definition misses downstream consumers. Trace the full chain.
• When renaming, grep ALL consumer file types. Some file types silently ignore missing refs (no compile error). Search code, templates, configs, generated files.
• Trace ALL code paths when verifying correctness. Code existing ≠ code executing. Trace early exits, error branches, conditional skips — not just happy path.
• Update docs that embed canonical data when source changes. Docs inlining derived data (workflows, schemas, configs) go stale silently. Update all embedding docs alongside source.
• Verify sub-agent results after context recovery. Background agents may finish while parent compacted — grep-verify output, don't trust assumed completion.
• Cross-check full target list against sub-agent assignments. Parallel sub-agents by category miss boundary items. Reconcile union of assignments against target list before proceeding.
• Sub-agents inherit knowledge only from their agent .md definition — use custom agent types, not built-in Explore. Tool adoption = permission + knowledge + enforcement (numbered workflow step).
• Persist sub-agent findings incrementally, not as a final batch. Long sub-agents hit cutoffs before final write — findings lost. Instruct append-per-section to report file.
• When debugging, ask "whose responsibility?" before fixing. Trace caller (wrong data) vs callee (wrong handling). Fix at responsible layer — never patch symptom site.
• Grep ALL removed names after extraction/refactoring. Primary file "done" ≠ secondary files clean. Grep entire scope for every removed symbol before declaring complete.
• Assume existing values are intentional — ask WHY before changing. Pattern-matching as "wrong" skips context. Before changing any constant/limit/flag: read comments, git blame, surrounding code.
• Verify ALL affected outputs, not just the first. One build green ≠ all green. Multi-stack changes (backend/frontend/tests/docs) require verifying EVERY output.
• Evaluate fit before copying a nearby pattern. Closest example ≠ matching preconditions — verify the new context shares the same constraints, base classes, scope, lifetime.
• Holistic-first debugging — resist nearest-attention trap. Don't dive into first plausible cause. List EVERY precondition (config, env vars, paths, DB, endpoints, creds, versions, DI, data). Verify each against evidence (grep/query — not reasoning). Ask "what would falsify this?" — if nothing, it's not a hypothesis. Most expensive failure: going deeper in "obvious" layer while bug sits in layer never questioned.
• Surgical changes — apply the diff test (context-aware). Two modes: (1) Bug fix → every line traces to the bug; no restyling; orphan cleanup only for imports YOUR changes made unused. (2) Review/enhancement → implement improvements AND announce as "Enhancement beyond main request: [what]". Never silently scope-creep. Diff test: "Would this line exist if I wasn't asked to do X?" — if no, delete or announce.
• Surface ambiguity before coding — don't pick silently. Multiple valid interpretations → present each with effort: "[Request] could mean (1) [N h], (2) [N h]. Which matters?" List scope/format/volume/constraints assumptions first. If simpler path exists, say so. Never silently pick.
• [MANDATORY FIRST ACTION] ALWAYS activate a suitable skill or workflow BEFORE responding. Match task against workflow catalog + skill list; invoke via skill invocation or $start-workflow <workflowId>. NEVER answer or write code before checking. Skip = protocol violation.
• Why-Review adversarial mindset — apply when reviewing any plan, decision, or design. Default SKEPTIC not VALIDATOR: steel-man a rejected alternative, invert each stated reason ("what does it sacrifice?"), stress-test top 2-3 assumptions, run pre-mortem ("ships, fails in 3 months — what breaks?"), surface 1-2 alternatives author missed. Section presence ≠ quality; quality = causal reasoning + concrete mitigations + evidence, not "it's better" or "monitor closely".
• Front-load report-write in sub-agent prompts for large reviews. Many-file sub-agents hit budget before final write — findings lost. Design prompts so: (1) report-write is first explicit deliverable, (2) append per-file/section (not batched), (3) scope bounded so reads don't exhaust budget. Truncated mid-sentence with no report file → spawn narrower scope, don't retry same prompt.
• After context compaction, re-verify all prior phase outcomes before continuing. Summaries describe intent, not environment state (git index, filesystem, processes). On resume, FIRST audit: git status, re-read modified files, verify filesystem. Every "completed" claim is an untested hypothesis until evidence confirms.
• OOM/memory: check row count before row size. Triage: (1) Unbounded query — no DB filter for trigger? Push filter to DB; eliminates OOM. (2) Large rows? Projection reduces proportionally. Row reduction > projection in ROI.
• Keep domain concepts out of generic/shared/infrastructure layers. Reusable layer (shared library, framework, infra module) must reference NO consumer-specific domain concept — tenant/customer/product IDs, business entities, feature rules. Leak compiles + runs → passes review silently while coupling the "reusable" layer to one consumer. Keep shared type domain-free; push domain fields/logic down into the consumer via subclass/composition. — why: a layer coupled to one consumer's domain is no longer reusable.