본문으로 건너뛰기
cccskills
무료GitHub에 공개

software-architecture

Guide for quality focused software architecture. This skill should be used when users want to write code, design architecture, analyze code, in any case that relates to software development.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

설치 방법 보기

포함된 파일(1)

  • SKILL.md3.9 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Software Architecture Development Skill

This skill provides guidance for quality focused software development and architecture. It is based on Clean Architecture and Domain Driven Design principles.

Code Style Rules

General Principles

  • Early return pattern: Always use early returns when possible, over nested conditions for better readability
  • Avoid code duplication through creation of reusable functions and modules
  • Decompose long (more than 80 lines of code) components and functions into multiple smaller components and functions. If they cannot be used anywhere else, keep it in the same file. But if file longer than 200 lines of code, it should be split into multiple files.
  • Use arrow functions instead of function declarations when possible

Best Practices

Library-First Approach

  • ALWAYS search for existing solutions before writing custom code
    • Check npm for existing libraries that solve the problem
    • Evaluate existing services/SaaS solutions
    • Consider third-party APIs for common functionality
  • Use libraries instead of writing your own utils or helpers. For example, use cockatiel instead of writing your own retry logic.
  • When custom code IS justified:
    • Specific business logic unique to the domain
    • Performance-critical paths with special requirements
    • When external dependencies would be overkill
    • Security-sensitive code requiring full control
    • When existing solutions don't meet requirements after thorough evaluation

Architecture and Design

  • Clean Architecture & DDD Principles:
    • Follow domain-driven design and ubiquitous language
    • Separate domain entities from infrastructure concerns
    • Keep business logic independent of frameworks
    • Define use cases clearly and keep them isolated
  • Naming Conventions:
    • AVOID generic names: utils, helpers, common, shared
    • USE domain-specific names: OrderCalculator, UserAuthenticator, InvoiceGenerator
    • Follow bounded context naming patterns
    • Each module should have a single, clear purpose
  • Separation of Concerns:
    • Do NOT mix business logic with UI components
    • Keep database queries out of controllers
    • Maintain clear boundaries between contexts
    • Ensure proper separation of responsibilities

Anti-Patterns to Avoid

  • NIH (Not Invented Here) Syndrome:
    • Don't build custom auth when Auth0/Supabase exists
    • Don't write custom state management instead of using Redux/Zustand
    • Don't create custom form validation instead of using established libraries
  • Poor Architectural Choices:
    • Mixing business logic with UI components
    • Database queries directly in controllers
    • Lack of clear separation of concerns
  • Generic Naming Anti-Patterns:
    • utils.js with 50 unrelated functions
    • helpers/misc.js as a dumping ground
    • common/shared.js with unclear purpose
  • Remember: Every line of custom code is a liability that needs maintenance, testing, and documentation

Code Quality

  • Proper error handling with typed catch blocks
  • Break down complex logic into smaller, reusable functions
  • Avoid deep nesting (max 3 levels)
  • Keep functions focused and under 50 lines when possible
  • Keep files focused and under 200 lines of code when possible

When to Use

This skill is applicable to execute the workflow or actions described in the overview.

Limitations

  • Use this skill only when the task clearly matches the scope described above.
  • Do not treat the output as a substitute for environment-specific validation, testing, or expert review.
  • Stop and ask for clarification if required inputs, permissions, safety boundaries, or success criteria are missing.

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

12-agent academic paper writing pipeline. 10 modes (full/plan/outline/revision/revision-coach/abstract/lit-review/format-convert/citation-check/disclosure). 6 paper types, 5 citation formats, bilingual abstracts, LaTeX/DOCX-via-Pandoc/PDF output. Style Calibration + Writing Quality Check + Anti-Patterns with IRON RULE markers. Triggers: write paper, academic paper, guide my paper, parse reviews, AI disclosure, 寫論文, 學術論文, 引導我寫論文, 審查意見.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

Multi-perspective academic paper review with dynamic reviewer personas. Simulates 5 independent reviewers (EIC + 3 peer reviewers + Devil's Advocate) with field-specific expertise. Supports full review, re-review (verification), quick assessment, methodology focus, Socratic guided, and calibration modes. Triggers on: review paper, peer review, manuscript review, referee report, review my paper, critique paper, simulate review, editorial review, calibrate reviewer, reviewer calibration, measure reviewer accuracy.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

Create, iterate, and scale paid ad creative for Google Ads, Meta, LinkedIn, TikTok, and similar platforms. Use when generating headlines, descriptions, primary text, or large sets of ad variations for testing and performance optimization.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

AI agent development workflow for building autonomous agents, multi-agent systems, and agent orchestration with CrewAI, LangGraph, and custom agents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

bouclem/skills72026년 5월 31일 업데이트

bouclem의 스킬 모두 보기

이 스킬의 문제 신고