Keith WilliamsGitHubDevin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>* feat(agents): add modular engineering rules from 2026 standards Add a rules directory with individual rule files derived from the Cal.com Engineering in 2026 and Beyond blog post. Rules are organized by section (architecture, quality, data, api, performance, testing, patterns, culture) following the Vercel agent-skills structure. Includes: - _sections.md defining rule categories and impact levels - _template.md for creating new rules - 14 individual rule files covering key engineering standards - README documenting the rules structure and usage Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(agents): consolidate DI and Repository+DTO docs into rules - Move di-pattern.md content to rules/patterns-di-pattern.md - Extract Repository + DTO section from knowledge-base.md into: - rules/data-repository-methods.md (method naming conventions) - rules/data-dto-boundaries.md (DTO location and naming) - Update knowledge-base.md to reference the new rule files - Delete old di-pattern.md file Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * chore(agents): remove stub reference sections from knowledge-base.md The rules directory is self-contained with its own README, so these redirect sections are unnecessary clutter. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * refactor(agents): combine DI pattern rules into single file Merged patterns-di-pattern.md into patterns-dependency-injection.md to eliminate overlap and create one comprehensive DI guide. Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
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title, impact, impactDescription, tags
| title | impact | impactDescription | tags |
|---|---|---|---|
| Leverage AI for Boilerplate and Testing | MEDIUM | Accelerates development while maintaining quality | culture, ai, automation, testing |
Leverage AI for Boilerplate and Testing
Impact: MEDIUM
Generate 80% of boilerplate and non-critical code using AI, allowing us to focus solely on complex business logic and critical architectures.
Where AI excels:
- Generating boilerplate code (DTOs, basic CRUD operations)
- Building comprehensive test suites
- Creating documentation
- Repetitive refactoring tasks
- Code review assistance
Where humans must focus:
- Complex business logic
- Critical architectural decisions
- Security-sensitive code
- Performance-critical algorithms
- Domain-specific edge cases
Example - AI-assisted test generation:
// Human writes the function
export function calculateOverlap(slot: TimeSlot, busy: BusyTime): boolean {
return slot.start < busy.end && slot.end > busy.start;
}
// AI generates comprehensive tests
describe("calculateOverlap", () => {
it("returns true when slot starts during busy period", () => {
// AI-generated test case
});
it("returns true when slot ends during busy period", () => {
// AI-generated test case
});
it("returns false when slot is completely before busy period", () => {
// AI-generated test case
});
it("returns false when slot is completely after busy period", () => {
// AI-generated test case
});
it("returns true when slot completely contains busy period", () => {
// AI-generated test case
});
it("returns true when busy period completely contains slot", () => {
// AI-generated test case
});
// AI identifies edge cases humans might miss
it("handles exact boundary matches correctly", () => {
// AI-generated edge case
});
});
Our CI is the final boss:
- Everything in our standards document is checked before code is merged in PRs
- No surprises make it into main
- Checks are fast and useful
- AI helps ensure comprehensive coverage
Reference: Cal.com Engineering Blog