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驗證循環

為 Claude Code 工作階段提供全面的驗證系統。用於驗證 Claude Code 工作階段。

驗證循環 是什麼?

這項「驗證循環」技能為 Claude Code 工作階段提供結構化的驗證流程。它涵蓋六個關鍵階段:建置驗證、型別檢查、程式碼風格檢查、測試套件、安全掃描以及差異審查。每個階段都包含可執行的指令與通過/失敗標準,確保開發人員在合併程式碼或建立 Pull Request 之前符合品質門檻。該技能支援 JavaScript/TypeScript 與 Python 專案,並提供統一的驗證報告格式,以輕鬆評估準備就緒狀態。它還包含一個持續模式,可在長時間工作階段中定期進行檢查,並可與 PostToolUse 鉤子整合以進行深入的程式碼審查。

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

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

驗證循環技能

為 Claude Code 工作階段提供全面的驗證系統。

使用時機

在以下情況調用此技能:

  • 完成一個功能或重大程式碼變更後
  • 建立 PR 之前
  • 當您想要確保品質關卡通過時
  • 重構之後

驗證階段

階段 1:建置驗證

# 檢查專案是否能建置
npm run build 2>&1 | tail -20
# 或
pnpm build 2>&1 | tail -20

如果建置失敗,請停止並先修復再繼續。

階段 2:型別檢查

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

# Python 專案
pyright . 2>&1 | head -30

回報所有型別錯誤。先修正關鍵錯誤再繼續。

階段 3:程式碼風格檢查

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

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

階段 4:測試套件

# 執行測試並包含涵蓋率
npm run test -- --coverage 2>&1 | tail -50

# 檢查涵蓋率閾值
# 目標:最低 80%

回報:

  • 總測試數:X
  • 通過:X
  • 失敗:X
  • 涵蓋率:X%

階段 5:安全掃描

# 檢查機密資訊
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

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

階段 6:差異審查

# 顯示變更內容
git diff --stat
git diff HEAD~1 --name-only

審查每個變更的檔案,檢查:

  • 非預期的變更
  • 缺少的錯誤處理
  • 潛在的邊界情況

輸出格式

執行所有階段後,產生驗證報告:

驗證報告
==================

建置:     [通過/失敗]
型別:     [通過/失敗] (X 個錯誤)
程式碼風格:[通過/失敗] (X 個警告)
測試:     [通過/失敗] (X/Y 通過,Z% 涵蓋率)
安全:     [通過/失敗] (X 個問題)
差異:     [X 個檔案變更]

整體:     [準備就緒/尚未準備好] 提交 PR

待修正問題:
1. ...
2. ...

持續模式

對於長時間工作階段,每 15 分鐘或在重大變更後執行驗證:

設定心理檢查點:
- 完成每個函式後
- 完成一個元件後
- 移動到下一個任務前

執行:/verify

與鉤子整合

此技能可補充 PostToolUse 鉤子,但提供更深入的驗證。 鉤子能立即捕捉問題;此技能則提供全面審查。

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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