Automatically applies when designing multi-agent systems. Ensures proper tool schema design with Pydantic, agent state management, error handling for tool execution, and orchestration patterns.
Automatically applies when securing AI/LLM applications. Ensures prompt injection detection, PII redaction for AI contexts, output filtering, content moderation, and secure prompt handling.
Automatically applies when reviewing code. Ensures structured review checklist covering correctness, security, performance, maintainability, testing, and documentation.
Automatically applies when working with database migrations. Ensures proper Alembic patterns, upgrade/downgrade scripts, data migrations, rollback safety, and migration testing.
Automatically applies when managing Python dependencies. Ensures proper use of uv/Poetry, lock files, version constraints, conflict resolution, and dependency security.
Automatically applies when evaluating LLM performance. Ensures proper eval datasets, metrics computation, A/B testing, LLM-as-judge patterns, and experiment tracking.
Automatically applies when working with git. Ensures conventional commits, branch naming, PR templates, release workflow, and version control best practices.
Automatically applies when building LLM applications. Ensures proper async patterns for LLM calls, streaming responses, token management, retry logic, and error handling.