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Found 1,666 Skills
CI/CD pipeline design with GitHub Actions, Docker, Kubernetes, Helm, and GitOps patterns
Master Node.js streams for memory-efficient processing of large datasets, real-time data handling, and building data pipelines
Generate and prioritize US equity long-side edge research tickets from EOD observations, then export pipeline-ready candidate specs for trade-strategy-pipeline Phase I. Use when users ask to turn hypotheses/anomalies into reproducible research tickets, convert validated ideas into `strategy.yaml` + `metadata.json`, or preflight-check interface compatibility (`edge-finder-candidate/v1`) before running pipeline backtests.
Manages Python project dependencies with uv. Learn how to add, remove, and updates dependencies, organize them into groups (dev, test, lint, docs), pin versions, handle conflicts, and manages lock files for reproducible installations across environments. Use when adding or updating packages, organizing development dependencies, resolving version conflicts, or managing lock files in CI/CD pipelines.
Receive and verify GitLab webhooks. Use when setting up GitLab webhook handlers, debugging token verification, or handling repository events like push, merge_request, issue, pipeline, or release.
Post-completion self-review for coding agents that runs simplify, harden, and micro-documentation passes on non-trivial code changes. Use when: a coding task is complete in a general agent session and you want a bounded quality and security sweep before signaling done. For CI pipeline execution, use simplify-and-harden-ci.
Guides Docker, CI/CD pipelines, deployment strategies, infrastructure as code, and observability setup. Use when writing Dockerfiles, configuring GitHub Actions, planning deployments, setting up monitoring, or when asked about containers, pipelines, Terraform, or production infrastructure.
Generate text embeddings and rerank documents via Together AI. Embedding models include BGE, GTE, E5, UAE families. Reranking via MixedBread reranker. Use when users need text embeddings, vector search, semantic similarity, document reranking, RAG pipeline components, or retrieval-augmented generation.
Log management - search, pipelines, archives, and cost control.
Provides comprehensive code review capability for NestJS applications, analyzing controllers, services, modules, guards, interceptors, pipes, dependency injection, and database integration patterns. Use when reviewing NestJS code changes, before merging pull requests, after implementing new features, or for architecture validation. Triggers on "review NestJS code", "NestJS code review", "check my NestJS controller/service".
MongoDB and PostgreSQL database administration. Databases: MongoDB (document store, aggregation, Atlas), PostgreSQL (relational, SQL, psql). Capabilities: schema design, query optimization, indexing, migrations, replication, sharding, backup/restore, user management, performance analysis. Actions: design, query, optimize, migrate, backup, restore, index, shard databases. Keywords: MongoDB, PostgreSQL, SQL, NoSQL, BSON, aggregation pipeline, Atlas, psql, pgAdmin, schema design, index, query optimization, EXPLAIN, replication, sharding, backup, restore, migration, ORM, Prisma, Mongoose, connection pooling, transactions, ACID. Use when: designing database schemas, writing complex queries, optimizing query performance, creating indexes, performing migrations, setting up replication, implementing backup strategies, managing database permissions, troubleshooting slow queries.
Before running Python scripts or installing packages, check for existing virtual environments and reuse them if found. If no virtual environment exists, ask the user to choose: (1) Create new venv in current directory (recommended), (2) Use system Python directly, or (3) Create venv at custom path. This applies to: running .py files, using pip/uv pip install, or any task requiring third-party packages. Exceptions: simple one-liners using only Python standard library.