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Found 1,666 Skills
Senior DevOps Engineer with expertise in CI/CD automation, infrastructure as code, monitoring, and SRE practices. Proficient in cloud platforms, containerization, configuration management, and building scalable DevOps pipelines with focus on automation and operational excellence.
Complete CI/CD guide for Capacitor apps covering GitHub Actions, GitLab CI, build automation, app signing, and deployment pipelines. Use this skill when users need to automate their build and release process.
Configure and run openapi-format CLI workflows for OpenAPI/AsyncAPI documents. Use when you need to sort fields, filter operations/tags/flags/content, change casing, generate operationIds, apply overlays, convert OpenAPI versions (3.0/3.1 to 3.1/3.2), rename titles, split specs, bundle refs, or manage .openapiformatrc/--configFile driven formatting pipelines with minimal config overrides.
Intershop Commerce Management (ICM) backend development best practices. This skill should be used when writing, reviewing, or refactoring ICM Java code to ensure optimal patterns for customization, performance, B2B features, security, testing, and maintainability. Triggers on tasks involving ICM cartridge development, REST API creation, business objects, pipelines, database operations, jobs, events, or search.
This skill should be used when containerizing applications with Docker, creating Dockerfiles, docker-compose configurations, or deploying containers to various platforms. Ideal for Next.js, React, Node.js applications requiring containerization for development, production, or CI/CD pipelines. Use this skill when users need Docker configurations, multi-stage builds, container orchestration, or deployment to Kubernetes, ECS, Cloud Run, etc.
This skill should be used when users need to interact with GitLab via the glab CLI. It covers repository management (create, delete, clone, fork), CI/CD workflows (pipelines, jobs, schedules), Merge Requests, Issues, Releases, and other GitLab operations. Triggers on requests mentioning GitLab, repos, MRs, issues, pipelines, or CI/CD workflows.
Uses the uv Python package and project manager correctly for dependencies, venvs, and scripts. Use when creating or modifying Python projects, adding dependencies, running scripts with inline deps, managing virtual environments, pinning Python versions, running CLI tools from PyPI, setting the IDE Python interpreter, or using uv in CI (e.g. GitHub Actions) or Docker containers. Use when the user mentions uv, pyproject.toml, uv.lock, uv run, uv add, uv sync, .venv, Python interpreter, poetry, pipenv, conda, CI, Docker, GitHub Actions, or asks to use uv instead of pip or poetry.
Run this repo’s Units+Checkpoints research pipelines end-to-end (survey/综述/review/调研/教程/系统综述/审稿), with workspaces + checkpoints. **Trigger**: run pipeline, kickoff, 继续执行, 自动跑, 写一篇, survey/综述/review/调研/教程/系统综述/审稿. **Use when**: 用户希望端到端跑流程（创建 `workspaces/<name>/`、生成/执行 `UNITS.csv`、遇到 HUMAN checkpoint 停下等待）。 **Skip if**: 用户明确要手工逐条执行（用 `unit-executor`），或你不应自动推进到 prose 阶段。 **Network**: depends on selected pipeline (arXiv/PDF/citation verification may need network; offline import supported where available). **Guardrail**: 必须尊重 checkpoints（无 Approve 不写 prose）；遇到 HUMAN 单元必须停下等待；禁止在 repo root 创建 workspace 工件。
Expert guidance for working with Dagster and the dg CLI. ALWAYS use before doing any task that requires knowledge specific to Dagster, or that references assets, materialization, or data pipelines. Common tasks may include creating a new project, adding new definitions, understanding the current project structure, answering general questions about the codebase (finding asset, schedule, sensor, component or job definitions), debugging issues, or providing deep information about a specific Dagster concept.
Build AI that answers questions about your database. Use when you need text-to-SQL, natural language database queries, a data assistant for non-technical users, AI-powered analytics, plain English database search, or a chatbot that talks to your database. Covers DSPy pipelines for schema understanding, SQL generation, validation, and result interpretation.
Optimizing vector embeddings for RAG systems through model selection, chunking strategies, caching, and performance tuning. Use when building semantic search, RAG pipelines, or document retrieval systems that require cost-effective, high-quality embeddings.
Author and maintain Eve manifest files (.eve/manifest.yaml) for services, environments, pipelines, workflows, and secret interpolation. Use when changing deployment shape or runtime configuration in an Eve-compatible repo.