engineering-standards

name	engineering-standards
description	Engineering standards for design and implementation methodology -- loaded by planner, implementer, and code-reviewer agents for design-first workflow, implementation standards, and quality checklist
user-invocable	false

Clean Code Methodology

A design and implementation methodology that combines the wisdom of legendary programmers with concrete standards for writing code that is readable, maintainable, and built to last.

Core Philosophy

Six foundational perspectives guide every design and implementation decision:

• Rich Hickey: Favor simple, immutable data structures and pure functions without side effects. Complexity is the root of most software problems.
• John Carmack: Implement features directly, avoiding unnecessary abstraction. Maintain clear strategies to measure and reason about performance.
• Joe Armstrong: Isolate failures through rigorous error handling. Ensure faults in one module do not propagate to others.
• Donald Knuth: Prioritize readable, maintainable code above all else. Choose clarity before cleverness.
• Barbara Liskov: Respect interface contracts and ensure substitutability. See skills/solid-principles/SKILL.md for full LSP treatment and depth.
• John Ousterhout: Fight complexity by designing deep modules with simple interfaces. Pull complexity downward into implementations rather than exposing it to callers.
• Cost-benefit, not religion: Every test, abstraction, and interface has an ongoing cost — maintenance, runtime, false-positive triage, cognitive load. A test that catches no real bug and slows the build is a liability, not an asset. The same applies to abstractions — premature DRY couples behaviors that need to evolve independently. Apply the Rule of Three: tolerate duplication the second time, extract on the third occurrence.

Implementation Standards

DRY (Don't Repeat Yourself)

• Extract repeated logic into well-named functions or modules.
• Apply the Rule of Three: tolerate duplication the second time, extract the third time.
• Use configuration and parameterization over duplication.
• Balance DRY with readability -- sometimes a small amount of duplication is clearer than a complex abstraction.

Clean Code Principles

• Naming: Use intention-revealing names that explain what and why, not how.
• Functions: Keep them small, focused on a single responsibility, typically under 20 lines.
• Comments: Write self-documenting code; use comments only for "why" not "what".
• Formatting: Consistent indentation, logical grouping, vertical density that aids comprehension.
• Error Handling: Fail fast, fail loud -- never silently swallow errors.

Reusability

• Design with clear interfaces and minimal dependencies.
• Favor composition over inheritance.
• Create modules that can be used independently of the larger system.
• Parameterize behavior rather than hardcoding specifics.

Maintainability

• Write code that your future self (or a colleague) can understand at 3 AM during an incident.
• Keep cognitive load low -- simple control flow, obvious data transformations.
• Apply single responsibility at every level. See skills/solid-principles/SKILL.md for full SRP depth.
• Structure code so changes are localized, not scattered across files.

Testability

• Design for dependency injection from the start.
• Separate pure logic from side effects (I/O, database, network).
• Create seams in the code where test doubles can be inserted.
• Ensure each function can be tested in isolation with clear input/output contracts.

Design-First Workflow

Follow these five steps for every non-trivial implementation:

1. Understand Requirements: Before writing code, clarify the exact requirements, edge cases, and constraints. Edge case enumeration is mandatory, not aspirational — walk boundary values (empty/zero/max), invalid inputs, failure paths (timeouts, partial writes), concurrency, authorization edges, and resource limits before implementation. Ask questions if anything is ambiguous.

2. Design First: Sketch the interfaces, data structures, and module boundaries before implementation. Think about how components will communicate and where the seams are.

3. Implement Incrementally: Build in small, verifiable steps. Each step should leave the codebase in a working state. Commit at each checkpoint.

4. Self-Review: Before presenting code, run the quality checklist below. Look for unnecessary complexity, potential bugs, naming that could be clearer, and duplication that should be extracted.

5. Explain Decisions: When presenting code, explain key design decisions and trade-offs made. Document non-obvious choices so future readers understand the reasoning.

Quality Checklist

Before considering any implementation complete, verify each item:

1. Single Responsibility -- Each function and module does one thing well.
2. Clear Naming -- Names reveal intent without requiring comments.
3. No Magic Numbers -- All constants are named and explained.
4. Explicit Error Handling -- Error cases are handled; no silent failures.
5. Low Coupling -- Minimal dependencies between modules.
6. Testability -- Code can be tested without complex setup.
7. Readability -- A new developer could understand this in 5 minutes.
8. DRY -- No unnecessary duplication (Rule of Three applied).
9. Performance Awareness -- No unnecessary computation or memory allocation, but no premature optimization either.
10. Functional Core, Imperative Shell -- Pure functions hold business logic; a thin shell handles I/O. Pure logic is unit-testable in isolation; the shell is swap-and-test as a thin integration layer.
11. No Primitive Obsession -- Domain concepts (Money, Duration, EmailAddress, OrderId) carry a type, not a raw string/int. Long parameter lists with related primitives signal a missing value object.
12. Failures are actionable -- Errors and test failures name the failing condition with enough context that the next reader can start debugging without rerunning. Avoid assert(predicate) when assert_eq(actual, expected) would print the values.

When Implementing

Apply this methodology during code writing with these checkpoints:

1. Start with the Design-First Workflow. Do not jump into code. Sketch interfaces and boundaries first, then implement incrementally.
2. Run the Quality Checklist before marking a step complete. Each of the 9 items is a gate -- if any item fails, fix it before moving on.
3. Apply the Core Philosophy as a lens. When making a design decision, ask: does this favor simplicity (Hickey)? Is it direct (Carmack)? Are failures isolated (Armstrong)? Is it readable (Knuth)? Does it honor contracts (Liskov)? Is the interface simple (Ousterhout)?
4. Follow Implementation Standards for the details. Use the DRY, naming, function size, reusability, maintainability, and testability standards as concrete guidelines -- not aspirational goals.
5. Self-review is not optional. Run the Quality Checklist as a literal checklist on every file you touch.

When Reviewing

Use this methodology as review criteria:

1. Evaluate each Quality Checklist item as a review check. Walk through all 9 items for every changed file. Flag violations by checklist item name (e.g., "issue: Clear Naming -- this variable name d does not reveal intent").
2. Check for Design-First evidence. Is the code organized around clear interfaces and boundaries, or does it look like it was written stream-of- consciousness? Lack of structure suggests the Design-First step was skipped.
3. Apply the Core Philosophy as severity guidance. Violations of failure isolation (Armstrong) or interface contracts (Liskov) are higher severity than formatting issues (Knuth).
4. Cross-reference with Implementation Standards. Check function size (~20 line guideline), DRY compliance (Rule of Three), composition over inheritance, and testability seams.
5. Cite the specific checklist item in every finding. This makes findings actionable and traceable to a concrete standard.