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验证循环

用于Claude Code会话的全面验证系统。在验证Claude Code会话时使用。

验证循环 是什么?

验证循环技能是一个全面的质量保障系统,专为Claude Code会话设计。它通过六个关键阶段对代码变更进行验证:构建检查、类型检查、代码检查、测试套件、安全扫描和差异审查。每个阶段都提供具体的命令和错误处理指南,确保在拉取请求(PR)创建之前达到质量门限。该技能支持多种项目类型,包括TypeScript、Python等,并针对不同的技术栈提供相应的命令。它在连续开发模式下特别有用,建议每15分钟或重大更改后运行一次,以维持代码质量。输出是一份结构化的验证报告,包含每个阶段的通过/失败状态、测试覆盖率以及需要修复的问题列表。此技能旨在补充PostToolUse钩子的即时错误捕获功能,提供更深层次的代码审查。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

验证循环技能

用于Claude Code会话的全面验证系统。

使用时机

在以下情况下调用此技能:

  • 完成一个功能或重大代码更改后
  • 创建PR之前
  • 希望确保质量门限通过时
  • 重构之后

验证阶段

阶段1:构建验证

# 检查项目是否构建成功
npm run build 2>&1 | tail -20
# 或
pnpm build 2>&1 | tail -20

如果构建失败,停止并修复后再继续。

阶段2:类型检查

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

# Python项目
pyright . 2>&1 | head -30

报告所有类型错误。修复关键错误后再继续。

阶段3:代码检查

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

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

阶段4:测试套件

# 运行测试并生成覆盖率
npm run test -- --coverage 2>&1 | tail -50

# 检查覆盖率阈值
# 目标:最低80%

报告:

  • 总测试数:X
  • 通过:X
  • 失败:X
  • 覆盖率:X%

阶段5:安全扫描

# 检查机密信息
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

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

阶段6:差异审查

# 查看更改内容
git diff --stat
git diff HEAD~1 --name-only

审查每个更改的文件:

  • 意外更改
  • 缺失的错误处理
  • 潜在边界情况

输出格式

运行所有阶段后,生成验证报告:

验证报告
==================

构建:    [通过/失败]
类型:    [通过/失败] (X个错误)
代码:    [通过/失败] (X个警告)
测试:    [通过/失败] (X/Y通过, Z%覆盖率)
安全:    [通过/失败] (X个问题)
差异:    [X个文件已更改]

整体:    [准备就绪/未准备就绪] 用于PR

待修复问题:
1. ...
2. ...

连续模式

对于长时间会话,每15分钟或重大更改后运行一次验证:

设置心理检查点:
- 完成每个函数后
- 完成组件后
- 进入下一个任务前

运行:/verify

与钩子的集成

此技能补充PostToolUse钩子,但提供更深入的验证。 钩子即时捕获问题;此技能提供全面审查。

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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