Development

Audit the active repo, MCP servers, plugins, connectors, env surfaces, and harness setup, then recommend the highest-value ECC-native skills, hooks, agents, and operator workflows. Use when the user wants help setting up Claude Code or understanding what capabilities are actually available in their environment.

Use this skill to prevent destructive operations when working on production systems or running agents autonomously.

Cross-stack source code asset audit — classifies every file, detects embedded third-party libraries, and delivers actionable four-level verdicts per module with interactive HTML reports.

Operate execution flow across GitHub and Linear by triaging issues and pull requests, linking active work, and keeping GitHub public-facing while Linear remains the internal execution layer. Use when the user wants backlog control, PR triage, or GitHub-to-Linear coordination.

Build reusable Manim explainers for technical concepts, graphs, system diagrams, and product walkthroughs, then hand off to the wider ECC video stack if needed. Use when the user wants a clean animated explainer rather than a generic talking-head script.

Discover and evaluate Laravel packages via LaraPlugins.io MCP. Use when the user wants to find plugins, check package health, or assess Laravel/PHP compatibility.

Use this skill to generate or audit design systems, check visual consistency, and review PRs that touch styling.

Trace every user-facing button/touchpoint through its full state change sequence to find bugs where functions individually work but cancel each other out, produce wrong final state, or leave the UI in an inconsistent state. Use when: systematic debugging found no bugs but users report broken buttons, or after any major refactor touching shared state stores.

Persistent per-project memory for Claude Code. Auto-loads project context on session start, tracks sessions with git activity, and writes to native memory. Commands run deterministic Node.js scripts — behavior is consistent across model versions.

Use this skill to monitor a deployed URL for regressions after deploys, merges, or dependency upgrades.

Use this skill to measure performance baselines, detect regressions before/after PRs, and compare stack alternatives.

SwiftUI 架构模式，使用 @Observable 进行状态管理，视图组合，导航，性能优化，以及现代 iOS/macOS UI 最佳实践。

基于协议的依赖注入，用于可测试的Swift代码——使用聚焦协议和Swift Testing模拟文件系统、网络和外部API。

Swift 6.2 可接近的并发性 — 默认单线程，@concurrent 用于显式后台卸载，隔离一致性用于主 actor 类型。

在 Swift 中使用 actor 实现线程安全的数据持久化——基于内存缓存与文件支持的存储，通过设计消除数据竞争。

用于审计Claude技能和命令的质量。支持快速扫描（仅变更技能）和全面盘点模式，采用顺序子代理批量评估。

选择在解析结构化文本时使用正则表达式还是大型语言模型的决策框架——从正则表达式开始，仅在低置信度的边缘情况下添加大型语言模型。

基于RFC驱动的多智能体DAG执行模式，包含质量门、合并队列和工作单元编排。

使用Plankton进行编写时代码质量强制执行——通过钩子在每次文件编辑时自动格式化、代码检查和Claude驱动的修复。

使用Test2::V0、Test::More、prove runner、模拟、Devel::Cover覆盖率和TDD方法的Perl测试模式。

现代 Perl 5.36+ 的惯用法、最佳实践和约定，用于构建稳健、可维护的 Perl 应用程序。

操作并扩展NanoClaw v2，这是ECC基于claude -p构建的零依赖会话感知REPL。

Ktor 服务器模式，包括路由 DSL、插件、身份验证、Koin DI、kotlinx.serialization、WebSockets 和 testApplication 测试。

JetBrains Exposed ORM 模式，包括 DSL 查询、DAO 模式、事务、HikariCP 连接池、Flyway 迁移和仓库模式。

Kotlin协程与Flow在Android和KMP中的模式——结构化并发、Flow操作符、StateFlow、错误处理和测试。

苹果FoundationModels框架用于设备上的LLM——文本生成、使用@Generable进行引导生成、工具调用，以及在iOS 26+中的快照流。

库无关的Flutter/Dart代码审查清单，涵盖Widget最佳实践、状态管理模式（BLoC、Riverpod、Provider、GetX、MobX、Signals）、Dart惯用法、性能、可访问性、安全性和整洁架构。

基于C++核心指南（isocpp.github.io）的C++编码标准。在编写、审查或重构C++代码时使用，以强制实施现代、安全和惯用的实践。

LLM API 使用成本优化模式 —— 基于任务复杂度的模型路由、预算跟踪、重试逻辑和提示缓存。

具有质量门、评估和恢复控制的连续自主代理循环模式。

使用SHA-256内容哈希缓存昂贵的文件处理结果——路径无关、自动失效、服务层分离。

分析一个陌生的代码库，并生成一个结构化的入门指南，包括架构图、关键入口点、规范和一个起始的CLAUDE.md文件。适用于加入新项目或首次在代码仓库中设置Claude Code时。

通过Claude DevFleet协调多智能体编码任务——规划项目、在隔离的工作树中并行调度智能体、监控进度并读取结构化报告。

## When to use

将单行目标转化为多会话、多代理工程项目的分步构建计划。每个步骤包含独立的上下文简介，以便新代理能直接执行。包括对抗性审查门、依赖图、并行步骤检测、反模式目录和计划突变协议。触发条件：当用户请求复杂多PR任务的计划、蓝图或路线图，或描述需要多个会话的工作时。不触发条件：任务可在单个PR或少于3个工具调用中完成，或用户说“直接执行”时。origin: community

自主Claude代码循环的模式与架构——从简单的顺序管道到基于RFC的多智能体有向无环图系统。

在Claude Code会话期间，将做出的架构决策捕获为结构化的架构决策记录（ADR）。自动检测决策时刻，记录上下文、考虑的替代方案和理由。维护一个ADR日志，以便未来的开发人员理解代码库为何以当前方式构建。

适用于Android和Kotlin多平台项目的Clean Architecture模式——模块结构、依赖规则、用例、仓库以及数据层模式。

团队中人工智能代理生成大部分实施输出的工程运营模型。

设计和优化AI代理的动作空间、工具定义和观察格式，以提高完成率。

编码代理（Claude Code、Aider、Codex等）在自定义任务上的直接比较，包含通过率、成本、时间和一致性指标

Rust testing patterns including unit tests, integration tests, async testing, property-based testing, mocking, and coverage. Follows TDD methodology.

Idiomatic Rust patterns, ownership, error handling, traits, concurrency, and best practices for building safe, performant applications.

Verification loop for Laravel projects: env checks, linting, static analysis, tests with coverage, security scans, and deployment readiness.

Test-driven development for Laravel with PHPUnit and Pest, factories, database testing, fakes, and coverage targets.

Laravel security best practices for authn/authz, validation, CSRF, mass assignment, file uploads, secrets, rate limiting, and secure deployment.

Laravel architecture patterns, routing/controllers, Eloquent ORM, service layers, queues, events, caching, and API resources for production apps.

Kotest, MockK, coroutine testi, property-based testing ve Kover coverage ile Kotlin test kalıpları. İdiomatic Kotlin uygulamalarıyla TDD metodolojisini takip eder.

Verification loop for Spring Boot projects: build, static analysis, tests with coverage, security scans, and diff review before release or PR.

Test-driven development for Spring Boot using JUnit 5, Mockito, MockMvc, Testcontainers, and JaCoCo. Use when adding features, fixing bugs, or refactoring.

Spring Security best practices for authn/authz, validation, CSRF, secrets, headers, rate limiting, and dependency security in Java Spring Boot services.

Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.

これはプロジェクト固有のスキルの例です。自分のプロジェクトのテンプレートとして使用してください。

JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot.

サブエージェントのコンテキスト問題を解決するために、コンテキスト取得を段階的に洗練するパターン

Verification loop for Django projects: migrations, linting, tests with coverage, security scans, and deployment readiness checks before release or PR.

Django testing strategies with pytest-django, TDD methodology, factory_boy, mocking, coverage, and testing Django REST Framework APIs.

Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations.

Django architecture patterns, REST API design with DRF, ORM best practices, caching, signals, middleware, and production-grade Django apps.

C++ テストの作成/更新/修正、GoogleTest/CTest の設定、失敗またはフレーキーなテストの診断、カバレッジ/サニタイザーの追加時にのみ使用します。

フックを介してセッションを観察し、信頼度スコアリング付きのアトミックなインスティンクトを作成し、スキル/コマンド/エージェントに進化させるインスティンクトベースの学習システム。

Research-before-coding workflow. Search for existing tools, libraries, and patterns before writing custom code. Systematizes the "search for existing solutions before implementing" approach. Use when starting new features or adding functionality.

Python testing best practices using pytest including fixtures, parametrization, mocking, coverage analysis, async testing, and test organization. Use when writing or improving Python tests.

Python-specific design patterns and best practices including protocols, dataclasses, context managers, decorators, async/await, type hints, and package organization. Use when working with Python code to apply Pythonic patterns.

PostgreSQL database patterns for query optimization, schema design, indexing, and security. Quick reference for common patterns, index types, data types, and anti-pattern detection. Based on Supabase best practices.

Go testing best practices including table-driven tests, test helpers, benchmarking, race detection, coverage analysis, and integration testing patterns. Use when writing or improving Go tests.

Go-specific design patterns and best practices including functional options, small interfaces, dependency injection, concurrency patterns, error handling, and package organization. Use when working with Go code to apply idiomatic Go patterns.

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications. Use when setting up deployment infrastructure or planning releases.

Database migration best practices for schema changes, data migrations, rollbacks, and zero-downtime deployments across PostgreSQL, MySQL, and common ORMs (Prisma, Drizzle, Django, TypeORM, golang-migrate). Use when planning or implementing database schema changes.

Use this skill when writing new features, fixing bugs, or refactoring code. Enforces test-driven development with 80%+ coverage including unit, integration, and E2E tests.

Next.js 16+ and Turbopack — incremental bundling, FS caching, dev speed, and when to use Turbopack vs webpack.

Build MCP servers with Node/TypeScript SDK — tools, resources, prompts, Zod validation, stdio vs Streamable HTTP. Use Context7 or official MCP docs for latest API.

Frontend development patterns for React, Next.js, state management, performance optimization, and UI best practices.

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

Formal evaluation framework for Claude Code sessions implementing eval-driven development (EDD) principles

Use up-to-date library and framework docs via Context7 MCP instead of training data. Activates for setup questions, API references, code examples, or when the user names a framework (e.g. React, Next.js, Prisma).

Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns.

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

Bun as runtime, package manager, bundler, and test runner. When to choose Bun vs Node, migration notes, and Vercel support.

Backend architecture patterns, API design, database optimization, and server-side best practices for Node.js, Express, and Next.js API routes.

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs.

GDB debugging assistant for AI agents - analyze core dumps, debug live processes, investigate crashes and deadlocks with source code correlation

Build React components, implement responsive layouts, and handle client-side state management. Masters React 19, Next.js 15, and modern frontend architecture.

Deep audit before GitHub push: removes junk files, dead code, security holes, and optimization issues. Checks every file line-by-line for production readiness.

Systematically finds and fixes bugs using proven debugging techniques. Traces from symptoms to root cause, implements fixes, and prevents regression.