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Found 1,507 Skills
Git 버전 관리 모범 관례 및 워크플로우 가이드. 다음 상황에서 사용: (1) Git 커밋 메시지 작성 시 (Conventional Commits 규칙 적용), (2) 브랜치 생성 및 관리 시 (GitHub Flow 기반), (3) PR 생성 및 병합 전략 선택 시, (4) Git 히스토리 정리 작업 시 (rebase, squash, cherry-pick), (5) Merge conflict 해결 시, (6) 'git', '.git', 'commit', 'branch', 'merge', 'rebase' 키워드가 포함된 작업 시
Framework-agnostic HTTP API route testing patterns, authentication strategies, and integration testing best practices. Supports REST APIs with JWT cookie authentication and other common auth patterns.
Use Gemini to find existing solutions before building from scratch. Leverages Google Search grounding to discover code examples, libraries, and best practices to avoid reinventing the wheel.
This skill should be activated when the user requests to "create PRD", "write product requirements document", "generate PRD", "new PRD" (either in Chinese or English), or mentions "product requirements document" or "PRD template". It automatically generates comprehensive Chinese PRD documents in accordance with 2026 best practices.
Expert in Python development with best practices across web, data science, and automation
Builds AI-native products using Dan Shipper's 5-product playbook and Brandon Chu's AI product frameworks. Use when implementing prompt engineering, creating AI-native UX, scaling AI products, or optimizing costs. Focuses on 2025+ best practices.
PostgreSQL best practices: multi-tenancy with RLS, schema design, Alembic migrations, async SQLAlchemy, and query optimization.
Manage secrets with Doppler: CLI operations, project/config/environment management, secrets injection, CI/CD integrations, and security best practices.
Use this skill when optimizing Jazz applications for speed, responsiveness, and scalability. Covers crypto setup, efficient data modeling, and UI patterns to prevent lag.
FastAPI best practices, async patterns, and Pydantic validation
Refine prompts for Claude models (Opus, Sonnet, Haiku) using Anthropic's best practices. Use when preparing complex tasks for Claude.
Principal backend engineering intelligence for Python services and data systems. Actions: plan, design, build, implement, review, fix, optimize, refactor, debug, secure, scale backend code and architectures. Focus: correctness, reliability, performance, security, observability, scalability, operability, cost.