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verification-loop

Claude Code 세션을 위한 포괄적인 검증 시스템입니다. Claude Code 세션을 확인할 때 사용하세요.

verification-loop란 무엇인가요?

이 스킬은 Claude Code 세션에서 코드 변경 사항을 검증할 때 사용하는 포괄적인 검증 시스템입니다. 기능 완료, PR 생성 전, 리팩토링 후 등 품질 게이트를 통과해야 하는 시점에 호출하여 사용합니다. 빌드 검증, 타입 검사, 린트 검사, 테스트 스위트 실행, 보안 스캔, diff 리뷰 등 6단계 검증 단계를 순차적으로 수행합니다. 각 단계는 명시적인 CLI 명령어로 실행되며, 최종적으로 종합 검증 보고서를 생성합니다. JavaScript/TypeScript 및 Python 프로젝트를 모두 지원하며, 15분 간격 또는 주요 변경 후 연속 모드로도 실행할 수 있습니다.

지원 대상✓Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/verification-loop

즐겨 사용하는 AI에게 물어보기

이 에이전트 스킬이 미리 로드된 새 채팅을 엽니다.

문서

Verification Loop Skill (검증 루프 스킬)

Claude Code 세션을 위한 포괄적인 검증 시스템입니다.

사용 시점

다음과 같은 경우에 이 스킬을 호출하세요:

  • 기능 또는 중요한 코드 변경을 완료한 후
  • PR을 생성하기 전
  • 품질 게이트가 통과되었는지 확인하려는 경우
  • 리팩토링 후

검증 단계

단계 1: 빌드 검증

# 프로젝트가 정상적으로 빌드되는지 확인
npm run build 2>&1 | tail -20
# OR
pnpm build 2>&1 | tail -20

빌드에 실패하면 중단하고 계속 진행하기 전에 수정하세요.

단계 2: 타입 검사

set -o pipefail
# TypeScript 프로젝트
npx --no-install tsc --noEmit 2>&1 | head -30

# Python 프로젝트
pyright . 2>&1 | head -30

모든 타입 오류를 보고하세요. 중요한 오류는 계속 진행하기 전에 수정하세요.

단계 3: 린트 검사

# JavaScript/TypeScript
npm run lint 2>&1 | head -30

# Python
ruff check . 2>&1 | head -30

단계 4: 테스트 스위트

# 커버리지와 함께 테스트 실행
npm run test -- --coverage 2>&1 | tail -50

# 커버리지 임계값 확인
# 목표: 최소 80%

보고:

  • 총 테스트: X
  • 통과: X
  • 실패: X
  • 커버리지: X%

단계 5: 보안 스캔

# 시크릿(비밀 키) 확인
grep -rn "sk-" --include="*.ts" --include="*.js" . 2>/dev/null | head -10
grep -rn "api_key" --include="*.ts" --include="*.js" . 2>/dev/null | head -10

# console.log 확인
grep -rn "console.log" --include="*.ts" --include="*.tsx" src/ 2>/dev/null | head -10

단계 6: Diff 리뷰

# 변경 사항 확인
git diff --stat
git diff HEAD~1 --name-only

변경된 각 파일을 검토하세요:

  • 의도하지 않은 변경 사항
  • 누락된 오류 처리
  • 잠재적인 엣지 케이스

출력 형식

모든 단계를 실행한 후 검증 보고서를 생성하세요:

VERIFICATION REPORT
==================

Build:     [PASS/FAIL]
Types:     [PASS/FAIL] (X errors)
Lint:      [PASS/FAIL] (X warnings)
Tests:     [PASS/FAIL] (X/Y passed, Z% coverage)
Security:  [PASS/FAIL] (X issues)
Diff:      [X files changed]

Overall:   [READY/NOT READY] for PR

Issues to Fix:
1. ...
2. ...

연속 모드

긴 세션의 경우 15분마다 또는 주요 변경 후 검증을 실행하세요:

Set a mental checkpoint:
- After completing each function
- After finishing a component
- Before moving to next task

Run: /verify

훅과의 통합

이 스킬은 PostToolUse 훅을 보완하지만 더 깊은 검증을 제공합니다. 훅은 즉시 문제를 잡아내고, 이 스킬은 포괄적인 검토를 제공합니다.

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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