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Found 2,505 Skills
Router skill for LLMQuant equities workflows. Use when the user needs stock analysis, equity comparison, research memos, merger-arb memos, or sell/take-profit work.
Input template configuration for Elastic integrations. Covers agent stream templates (agent/stream/*.yml.hbs) for all non-CEL input types: HTTPJSON, AWS S3, CloudWatch, Azure Blob, Azure EventHub, GCS, GCP Pub/Sub, TCP, UDP, HTTP Endpoint, Filestream, Logfile, Journald, Winlog, and WebSocket. For CEL input programs, use the cel-programs skill instead.
Umbrella skill for all Convex development patterns. Routes to specific skills like convex-functions, convex-realtime, convex-agents, etc.
Comprehensive checklist for conducting thorough code reviews covering functionality, security, performance, and maintainability
Python testing with pytest covering fixtures, parametrization, mocking, and test organization for reliable test suites
Quality assurance specialist for security, performance, accessibility, and comprehensive testing
Beads (bd) distributed git-backed issue tracker for AI agents: hash-based IDs, dependency graphs, worktrees, molecules, sync, GitLab/Linear/Jira. Keywords: bd, beads, issue tracker, git-backed, dependencies, molecules, worktree, sync, AI agents.
A Pythonic interface to the HDF5 binary data format. It allows you to store huge amounts of numerical data and easily manipulate that data from NumPy. Features a hierarchical structure similar to a file system. Use for storing datasets larger than RAM, organizing complex scientific data hierarchically, storing numerical arrays with high-speed random access, keeping metadata attached to data, sharing data between languages, and reading/writing large datasets in chunks.
Catalog of non-obvious behaviors, gotchas, and platform-specific quirks in MultiversX that often lead to bugs. Use when debugging unexpected behavior, reviewing code for subtle issues, or learning platform-specific pitfalls.
Principal AI Architect and Machine Learning Engineer.
Linode cloud computing platform. Use for Linux cloud servers.
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.