Cursor 必备 MCP 组合推荐

Git workflow patterns including branching strategies, commit conventions, keeping history clean and readable, tidying local commits before merging, merge vs rebase, conflict resolution, and collaborative development best practices for teams of all sizes. Use when choosing a branching strategy, writing commit conventions, cleaning up history before a pull request, deciding merge versus rebase, or resolving conflicts.

Baseline cross-project coding conventions for naming, readability, immutability, and code-quality review. Use detailed frontend or backend skills for framework-specific patterns. Use when reviewing code quality or naming with no framework-specific skill that applies.

Coordinate frontend/backend or service-to-service work through one authoritative machine-checkable contract (OpenAPI, AsyncAPI, Protocol Buffers, or JSON Schema), with generated consumer types and contract-verified integration. Use when parallel consumer and provider work must evolve an API or event schema without field drift, mock/production shape mismatch, or one side silently redefining the interface.

Prevent AI style drift on legacy projects by scanning the codebase for implicit conventions, resolving conflicts with the operator one at a time, and writing an enforceable .ai-style-rules.md (Golden Files, naming rules, DONTs) plus an optional CLAUDE.md hook. Use when onboarding an AI agent onto a hand-written legacy codebase or extracting a project's unwritten coding rules.

Keep a long-lived project's documentation from rotting by assigning existing project docs clear constitution, map, status, and history roles, then wiring the active agent harness to those canonical sources. Use in the maintain phase when docs drift from code, agents lose context between sessions, or intentional removals keep being recreated. Prefer adopting the repository's current docs structure over creating new root files. 中文触发：文档治理、活文档、项目状态追踪、防文档漂移、项目地图、健康仪表盘、删除区、长期项目治理

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.

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.

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.

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.

Redis data structure patterns, caching strategies, distributed locks, rate limiting, pub/sub, and connection management for production applications. Use when adding caching, a distributed lock, rate limiting, or pub/sub with Redis, or when key design needs review.

Simulate a collaborative dev team session where multiple role-based personas (PM, Architect, Developer, QA) respond to the same problem together in one session. Use when designing a feature, reviewing a proposal, or onboarding a new initiative and you want multi-role perspective without switching agents manually.

MySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends. Use when designing MySQL or MariaDB schemas and indexes, or when a query, transaction, or replica lags.

Thread-safe data persistence in Swift using actors — in-memory cache with file-backed storage, eliminating data races by design. Use when persisting data in Swift and a data race or thread-safety problem needs designing out.

JPA/Hibernate patterns for entity design, relationships, query optimization, transactions, auditing, indexing, pagination, and pooling in Spring Boot. Use when designing JPA entities or relationships, or when a Hibernate query, transaction, or N+1 problem needs fixing.

Apply concrete design-engineering details that make interfaces feel polished. Use when reviewing or improving UI spacing, typography, borders, shadows, motion, hit areas, icons, text wrapping, and interaction states.

Design, implement, and audit accessible UI to WCAG 2.2 Level AA across Web, iOS, and Android — semantic ARIA roles and labels, accessibility traits and hints, focus management, contrast, target size, and screen-reader support. Use when building or auditing UI for accessibility compliance, keyboard navigation, or screen-reader support.

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

iOS 26 Liquid Glass design system — dynamic glass material with blur, reflection, and interactive morphing for SwiftUI, UIKit, and WidgetKit. Use when building iOS 26 Liquid Glass UI in SwiftUI, UIKit, or WidgetKit.

Generate a design system from an existing codebase or audit one for visual consistency: extract tokens (colors, typography, spacing, shadows) into design-tokens.json and CSS custom properties with DESIGN.md rationale and an interactive HTML preview, score the UI across 10 dimensions, and flag AI-slop patterns. Use when starting a design system, auditing visual consistency before a redesign, or reviewing a PR that touches styling.

Capture architectural decisions as numbered ADR markdown files in docs/adr/ with context, alternatives considered, consequences, and an index README. Use when the user says 'record this decision' or 'ADR this', chooses between frameworks or databases, discusses trade-offs, or asks why the codebase is shaped this way.

ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads. Use when writing ClickHouse schemas or queries, or when an analytical query is too slow.

Library-agnostic Flutter/Dart code review checklist covering widget best practices, state management patterns (BLoC, Riverpod, Provider, GetX, MobX, Signals), Dart idioms, performance, accessibility, security, and clean architecture. Use when reviewing Flutter or Dart code, whatever state management library the project uses.

Turn ambiguous or high-impact product and engineering changes into scoped, verifiable acceptance criteria before or alongside implementation. Use when a user asks to clarify a feature, define acceptance criteria, de-risk a security/data/migration/integration change, prepare implementation requirements for another agent, or make a complex request testable. Do not trigger for trivial edits, straightforward fixes, active debugging, code review, or implementation requests whose acceptance conditions are already clear unless the user explicitly invokes this skill.

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

Comprehensive Perl security covering taint mode, input validation, safe process execution, DBI parameterized queries, web security (XSS/SQLi/CSRF), and perlcritic security policies. Use when reviewing Perl input handling, process execution, DBI queries, or web-facing code.

Quarkus security implementation patterns: JWT and OIDC authentication, @RolesAllowed RBAC and SecurityIdentity checks, Bean Validation and custom validators, parameterized Panache queries, BCrypt password hashing, CORS and security headers, rate limiting, audit logging, Vault or environment-variable secrets, and dependency CVE scanning. Use when adding authentication or authorization, validating input, managing secrets, or hardening a Quarkus application.

Verification loop for Quarkus projects: build, static analysis (Checkstyle, PMD, SpotBugs), tests with JaCoCo coverage, OWASP dependency and container security scans, GraalVM native compilation, health checks, and config validation. Use when verifying a Quarkus service before a PR, after major refactoring or dependency upgrades, or pre-deploy.

Safe, reversible database migration patterns: forward-only production changes, expand-contract zero-downtime renames, concurrent indexes, batched backfills, and per-tool workflows for PostgreSQL, Prisma, Drizzle, Kysely, Django, and golang-migrate. Use when writing a schema or data migration, adding a column or index to a large table, planning a rollback, or preparing a zero-downtime deploy.

Run the full Django verification loop — environment check, mypy/ruff/black linting, migration safety, pytest with coverage targets, pip-audit and bandit security scans, settings and logging review, and diff review — producing a phased pass/fail report before release or PR. Use when preparing a Django pull request, validating migrations or coverage, or running pre-deploy readiness checks.

Run the full Spring Boot verification loop — Maven or Gradle build, SpotBugs, PMD, and Checkstyle static analysis, unit and Testcontainers integration tests with JaCoCo coverage, OWASP dependency and secret scans, and diff review — producing a pass/fail readiness report. Use when preparing a Spring Boot pull request, validating coverage thresholds, or running pre-deploy verification.

Kubernetes workload patterns, resource management, RBAC, probes, autoscaling, ConfigMap/Secret handling, and kubectl debugging for production-grade deployments. Use when writing or reviewing Kubernetes manifests, or debugging probes, RBAC, autoscaling, or resource limits.

Production machine-learning engineering workflow for data contracts, reproducible training, model evaluation, deployment, monitoring, and rollback. Use when building, reviewing, or hardening ML systems beyond one-off notebooks.

Django security best practices, authentication, authorization, CSRF protection, SQL injection prevention, XSS prevention, and secure deployment configurations. Use when reviewing Django authentication, authorization, input handling, or deployment settings.

Verification loop for Laravel projects: env checks, linting, static analysis, tests with coverage, security scans, and deployment readiness. Use when verifying a Laravel project before merge or deploy — lint, static analysis, tests, coverage, security.

Deployment workflows, CI/CD pipeline patterns, Docker containerization, health checks, rollback strategies, and production readiness checklists for web applications. Use when setting up CI/CD, containerizing an app, or checking production readiness before a release.

Use this skill when adding authentication, handling user input, working with secrets, creating API endpoints, or implementing payment/sensitive features. Provides comprehensive security checklist and patterns.

Laravel architecture patterns, routing/controllers, Eloquent ORM, service layers, queues, events, caching, and API resources for production apps. Use when building or reviewing Laravel apps — controllers, Eloquent, service layers, queues, or API resources.

Measure performance baselines and detect regressions across browser Core Web Vitals (LCP, INP, CLS, page weight), API endpoint latency percentiles, and build/test feedback times, with before/after comparison stored in git-tracked .ecc/benchmarks JSON. Use when checking page speed, responding to 'it feels slow' reports, verifying launch performance targets, or comparing stack alternatives.

Quarkus 3.x LTS architecture patterns with Camel for messaging, RESTful API design, CDI services, data access with Panache, and async processing. Use for Java Quarkus backend work with event-driven architectures. Use when building or reviewing a Quarkus service, especially with Camel messaging or Panache data access.

Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work. Use when building or reviewing a Spring Boot backend — REST layer, services, data access, caching, or async work.

Django architecture patterns, REST API design with DRF, ORM best practices, caching, signals, middleware, and production-grade Django apps. Use when building or reviewing Django apps, DRF APIs, ORM queries, or caching.

Cost optimization patterns for LLM API usage — model routing by task complexity, budget tracking, retry logic, and prompt caching. Use when LLM spend needs to come down, or when routing tasks across model tiers and budgets.

REST API design patterns including resource naming, status codes, pagination, filtering, error responses, versioning, and rate limiting for production APIs. Use when designing or reviewing REST endpoints, resource names, status codes, pagination, or versioning.

Build a new API connector or provider by matching the target repo's existing integration pattern exactly. Use when adding one more integration without inventing a second architecture.

Validate the why before building through four product diagnostics — a YC-style product diagnostic that produces PRODUCT-BRIEF.md with a go/no-go recommendation, a founder review scoring product-market-fit signals, a user journey audit measuring time-to-value, and ICE feature prioritization. Use when pressure-testing product direction, choosing between features, sanity-checking a launch, or converting a vague idea into a product brief.

Production-ready Dart and Flutter patterns covering null safety, immutable state with Freezed, async composition, widget architecture, state management (BLoC, Riverpod, Provider), GoRouter navigation with auth guards, Dio networking, error handling, and testing. Use when writing or reviewing Dart and Flutter code — state, widgets, navigation, networking, or architecture.

FastAPI best practices covering project structure, Pydantic v2 schemas, dependency injection, async handlers, authentication, authorization, transactional service layers, and testing with httpx and pytest. Use when building or reviewing FastAPI apps — Pydantic schemas, dependencies, async handlers, auth, or tests.

Ktor server patterns including routing DSL, plugins, authentication, Koin DI, kotlinx.serialization, WebSockets, and testApplication testing. Use when building a Ktor server — routing, plugins, auth, DI, serialization, or tests.