Design Principles Audit

Audit the codebase against well-known design principles. Produce a scored DESIGN_AUDIT.md with prioritized remediation actions.

Core principle: Investigate, do not pattern-match. Read the actual code to determine whether a violation is genuine, intentional, or a false positive. Only flag findings with specific file:line evidence.

Prerequisite check

git --version 2>/dev/null && git rev-parse --is-inside-work-tree 2>/dev/null

If not in a git repository, warn but proceed — git history will be unavailable but file analysis still works.

Step 1: Detect context

Determine language, framework, and paradigm — this governs which principles apply and how violations manifest:

ls go.mod package.json pyproject.toml Cargo.toml *.csproj 2>/dev/null

Read README.md and CONTEXT.md (if present) for architectural intent.

Determine audit scope from input:

• Full audit (default) — entire codebase, excluding vendor/, node_modules/, generated files
• Module audit — specific directory or package
• Delegated mode — called by tech-debt; focus on changed files and surface findings for the scorecard

For language-specific violation patterns per principle, see references/principles-guide.md.

Step 2: Scan for violations

For each applicable principle, read actual code and identify violations. Do not grep for keywords — trace call paths and responsibilities.

SOLID

• S — Single Responsibility: Find files/classes/functions with multiple distinct reasons to change. Signals: files > 300 lines mixing concerns, functions doing I/O + business logic + formatting.
• O — Open/Closed: Find code that requires modification (not extension) to add new behavior. Signals: long switch/if-else chains on type tags, no interface/abstraction at extension points.
• L — Liskov Substitution: Find subtypes that weaken preconditions, strengthen postconditions, or throw unexpected errors. Signals: overrides that do nothing, panic on valid input, type assertions before use.
• I — Interface Segregation: Find broad interfaces forcing implementors to satisfy methods they do not use. Signals: interface with > 5 methods, stub/no-op implementations.
• D — Dependency Inversion: Find high-level modules importing concrete low-level types. Signals: direct instantiation of dependencies, no interfaces at package boundaries, untestable code.

DRY

Find logic, structure, or data duplicated in two or more places where a single abstraction would reduce maintenance risk. Distinguish genuine duplication from coincidental similarity — two functions that look alike but evolve independently are not DRY violations.

YAGNI

Find abstractions, configuration options, or generality added without a current use case. Signals: unused parameters, dead code paths, interfaces with one implementation, config keys never read.

KISS

Find needlessly complex solutions. Signals: indirection that adds no value, abstractions solving problems the codebase does not have, multi-layer delegation for simple operations.

Law of Demeter

Find call chains that expose internal structure: a.GetB().GetC().Do(). Each unit should talk only to its immediate collaborators.

Separation of Concerns

Find mixed layers: HTTP logic in domain models, SQL in handlers, presentation in business rules. Trace data flow from entry points.

Composition over Inheritance

Find deep inheritance hierarchies (> 2 levels), base classes modified to satisfy subclasses, or tight coupling through inheritance. Note: in Go and functional languages, flag missing interface usage rather than inheritance misuse.

12-Factor (code-relevant subset)

• Config: Hardcoded values that should come from environment variables (URLs, credentials, feature flags, thresholds).
• Logs: fmt.Println, print(), or ad-hoc string concatenation instead of a structured logging library.
• Stateless processes: In-memory state that prevents horizontal scaling (package-level mutable globals, local file caching of user-specific data).

Step 3: Score findings

For each finding assign Impact, Effort, and Priority using the framework in references/report-template.md.

• Impact: CRITICAL / HIGH / MEDIUM / LOW
• Effort: S (< 1 day) / M (1–5 days) / L (1–2 weeks) / XL
• Priority: P1 (high impact + low effort) through P4 (low impact, any effort)

Score each principle area 0–10 for the summary scorecard.

Step 4: Adversarial debate

Identify the top 3 findings with the highest combined Impact × Effort score where the violation is genuinely contested (reasonable engineers could disagree). Skip findings that are clear-cut.

For each debated finding:

• Red (refactor): argues the violation should be fixed. Must propose a concrete diff or refactoring approach. Challenges whether the current design is justified.
• Blue (keep): argues the current design is correct given context. Must cite specific reasons beyond "it works." Points out risks of the proposed change.
• White (verdict): evaluates on evidence. Decides: refactor, keep, or add justification comment.

Debate rules

• Red and Blue must cite specific code, not generalities
• Red must propose a concrete change, not just identify the smell
• Blue cannot simply defend status quo without reasoning
• White's verdict is final and must be actioned in the report

Step 5: Generate DESIGN_AUDIT.md

Write the report using the template in references/report-template.md. Include executive summary, principle scorecard, detailed findings with evidence, debate verdicts, and prioritized remediation roadmap.

Run markdownlint on the generated file:

markdownlint DESIGN_AUDIT.md 2>/dev/null || \
  npx markdownlint-cli DESIGN_AUDIT.md 2>/dev/null

Fix any issues before presenting.

Step 6: Present summary

Show the user:

1. Principle scorecard (name, score 0–10, top finding)
2. Overall design health score (sum of principle scores / max)
3. Debate verdicts for contested findings
4. P1 items requiring immediate attention
5. Top 5 remediation priorities with estimated effort
6. Ask which items to address first