Modular Design Principles

Use this skill when reasoning about structure and boundaries in any codebase. It intentionally avoids framework names, folder conventions, and tooling — map principles to your stack locally.

What to load

Task	Where
Principles table + violations + workflows (this file)	`SKILL.md`
Per-principle definition, agent rules, abstract examples	`references/principles.md`

Layered mental model

Composition roots (applications, hosts, runners): wire modules together; keep orchestration thin.
Modules / bounded contexts: cohesive units of behavior and data ownership; each should be understandable and testable on its own.
Shared kernels (use sparingly): only stable, truly cross-cutting concepts; resist turning them into a grab-bag of “everything everyone needs.”

How you physically lay this out (mono repo, multi repo, packages, libraries) is a delivery choice, not the definition of modularity. The principles below still apply.

The ten principles

#	Principle	Intent
1	Well-defined boundaries	A small, stable public surface; everything else is internal. Consumers depend on contracts, not internals.
2	Composability	Modules can be used alone or combined without special knowledge of each other’s internals.
3	Independence	No hidden shared mutable state across boundaries; each module should be testable in isolation (with fakes or test doubles at the edges).
4	Individual scale	Resources (compute, storage, rate limits, batch size) can be tuned per module where it matters, without rewriting others.
5	Explicit communication	Cross-module interaction uses documented contracts (APIs, events, messages, shared types) — not incidental coupling.
6	Replaceability	Dependencies on other modules are expressed through interfaces or protocols so implementations can change.
7	Deployment independence	Modules do not assume they share a process, host, or release cadence unless that is an explicit architectural decision.
8	State isolation	Each module owns its persistent state and naming; no silent sharing of the same logical data store or ambiguous global names across boundaries.
9	Observability	Each module can be diagnosed on its own: logs, metrics, traces, health — attributable to the unit that emitted them.
10	Fail independence	Failures are contained (timeouts, bulkheads, circuit breaking, idempotency) so one module’s outage does not blindly cascade.

Principle 8 is often the hardest: ambiguous ownership of data or names is a frequent source of “works until it doesn’t” integration bugs.

For depth (rules for agents + abstract examples per principle), load references/principles.md.

Typical violations (stated abstractly)

Colliding concepts — the same name or schema for different things in different modules, or duplicate “global” definitions that diverge over time.
Reach-through persistence — one module reading or writing another module’s tables, buckets, or documents without going through an agreed contract.
Centralized data ownership — a single persistence layer that registers and exposes all stores for all modules, encouraging hidden coupling.
Logic at the edge — business rules in transport adapters (HTTP handlers, UI, CLI) instead of domain/application code.
Edge talking to storage directly — adapters depending on low-level persistence APIs instead of use cases or application services.
Unscoped transactions — writes that span boundaries without clear transaction ownership and failure semantics.
Leaky exports — repositories, internal services, or implementation types exposed as the module’s public API.
Facades that aren’t thin — “public” entry points that embed querying, mapping, or policy instead of delegating to the right layer inside the module.

Creating a bounded context (workflow)

Use when introducing a new cohesive area of the system (greenfield module or extracted domain).

Scope and language — Name the context; list core nouns/verbs (ubiquitous language). Reject vague names that collide with other contexts.
Responsibilities — What decisions happen only here? What is explicitly out of scope?
State ownership — Which facts are authoritative in this context? Where are they stored conceptually (even if storage tech is undecided)?
Public contract — Operations and/or events other contexts may use. Version or evolve this contract intentionally.
Integrations — For each neighbor: sync call, async message, shared read model, or batch sync? Document consistency (immediate, eventual) and failure behavior.
Invariants and lifecycles — What must always be true inside this boundary? What starts/completes a lifecycle?
Isolation check — Can you test core behavior without spinning up unrelated contexts (fakes at ports)?
Observability — How will you trace a request or job through this context with clear identifiers?

Cross-module interaction (while designing): prefer the minimal contract; define timeouts, retries, idempotency for async; avoid “temporary” direct store access as a shortcut.

When to split or merge

Default: fewer boundaries until real pain appears — “flat is often better” than premature fragmentation. Splitting adds coordination, versioning, and operational cost.

Six-criteria test (favor split when several are true)

#	Criterion	Question
1	Language	Do the sub-areas use different vocabulary or conflicting definitions of the same word?
2	Rate of change	Do parts change on different cadences or for unrelated reasons (most edits touch one side)?
3	Scale / SLO	Do parts need different throughput, latency, or availability targets?
4	Consistency	Do they need different transaction boundaries (cannot share one atomic write model cleanly)?
5	Ownership	Would different teams or clear ownership lines reduce conflict and review churn?
6	Pain signal	Is there observable integration pain: ripple effects, fear of change, unclear who owns a bug?

Cohesion / coupling (qualitative). Favor high cohesion inside a module and low, explicit coupling between modules. If the only motivation is “files got big” or “folder aesthetics,” merge or wait.

When to merge or not split yet

Boundaries are artificial (same language, same lifecycle, constant cross-calls).
Splitting would duplicate logic or data without a clear single writer rule.
Team is not ready to own contracts, versioning, and ops for extra units.

Decision prompts (short)

Would separation reduce accidental coupling more than it increases coordination cost?
Is there a natural ubiquitous language boundary, or only a technical seam?

Sub-units inside a bounded context

Sometimes one outer boundary is right, but inside it there are named sub-areas (subdomains, feature areas). Principles still apply within the context.

Ownership

Each sub-unit should own its slice of model and persistence concerns where possible — avoid one mega registration layer that wires every store and repository for every sub-unit in one place (encourages reach-through and hidden coupling).

Cross-sub-unit access

Prefer internal application APIs or thin internal facades (same context, explicit surface) over peers importing each other’s storage types directly.
For async flows, prefer enriched payloads so handlers do not chat across sub-units for data that could travel with the event/command.

Shared kernel inside the context

Small, stable shared types or enums can live in a narrow shared area — but resist a growing “utils” dump that becomes the real coupling point.

Anti-pattern: A single “persistence” or “data” sub-module that becomes the only place that knows about all tables/documents for all sub-units, and everyone else reaches through it — same problems as cross-context reach-through, inside the boundary.

Architecture compliance pass

Use for reviews or audits without assuming tooling. Treat items as signals, not proof — confirm with domain experts.

Dependency and API signals

Inbound vs outbound: Dependencies should align with your chosen architecture (e.g. domain at the center, adapters outside). Inward leaks of infrastructure types into core logic are a smell.
Public surface: Can you list exported operations/events/types without including storage or internal services? If not, boundaries are leaky.
Neighbor imports: Types or clients from module A used in module B — are they only contract types, or persistence/implementation types?

Persistence and data signals

Reach-through: References to another context’s physical data (schema, collection, bucket name) outside an agreed contract.
Naming collisions: Same logical name for different things, or shared global IDs without a documented mapping rule.
Transaction ownership: Writes that span contexts without a clear saga, outbox, or single-owner rule and documented failure cases.

Operational signals

Blame: Incidents where “we don’t know which module owns this row/behavior” → ownership or observability gap.
Cascades: One dependency’s slowdown or failure takes down unrelated user journeys → missing timeouts, bulkheads, or degradation paths.

Severity heuristic (for reporting)

Tier	Meaning
P0	Data corruption risk, security boundary violation, or cross-context persistence with no contract
P1	Unclear ownership, leaky public API, missing failure semantics at boundaries
P2	Observability gaps, composability smells, tech debt that increases future coupling

Maturity note: Scoring is qualitative unless the team defines numeric gates. Use trends: fewer P0/P1 over time, clearer contracts.

Quick checklist (before proposing structure)

Public API is minimal; internals are not exported casually.
Names and storage ownership are unambiguous per module.
No cross-module persistence shortcuts without an explicit contract.
Business rules sit behind a clear application/domain layer, not only in adapters.
Cross-module calls have explicit failure and timeout behavior.
Observability can answer “which module failed and why?” without spelunking.
If the context has sub-units: each has clear ownership; no monolithic “registers everything” persistence grab-bag.

Relationship to stack-specific skills

When a project has concrete conventions (framework modules, DI, repository patterns, folder layout, codegen, CI checks), prefer those documents for how to implement. Use this skill for why boundaries exist and what good modular design optimizes for — so stack-specific advice stays aligned with the same principles.

modular-design-principles