ricardos-claude-code

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.

Automatically applies when choosing LLM models and providers. Ensures proper model comparison, provider selection, cost optimization, fallback patterns, and multi-model strategies.

Automatically applies when implementing monitoring and alerting. Ensures proper metric instrumentation, alerts, SLO/SLI definition, dashboards, and observability patterns.

Automatically applies when adding logging and observability. Ensures structured logging, OpenTelemetry tracing, LLM-specific metrics (tokens, cost, latency), and proper log correlation.

Automatically applies when profiling Python performance. Ensures proper use of profiling tools, async profiling, benchmarking, memory analysis, and optimization strategies.

Automatically applies when engineering prompts for LLMs. Ensures proper prompt structure, templates, few-shot examples, context management, and injection prevention.

Automatically applies when configuring Python project packaging. Ensures proper pyproject.toml setup, project layout, build configuration, metadata, and distribution best practices.

Automatically applies when optimizing database queries. Ensures EXPLAIN analysis, proper indexing, N+1 prevention, query performance monitoring, and efficient SQL patterns.

Automatically applies when building RAG (Retrieval Augmented Generation) systems. Ensures proper chunking strategies, vector database patterns, embedding management, reranking, and retrieval optimization.

Automatically applies when drafting or revising documentation to enforce repository voice, clarity, and navigation patterns.

Enforces OpenSpec authoring conventions including metadata blocks, section ordering, requirement/scenario structure, and validation steps.

Provides reusable markdown templates and outlines for OpenSpec proposals, design docs, ADRs, READMEs, and changelogs.

Automatically applies when creating FastAPI endpoints, routers, and API structures. Enforces best practices for endpoint definitions, dependency injection, error handling, and documentation.

Automatically applies when writing Python code to enforce comprehensive type hints. Ensures mypy compatibility, proper generic types, and type safety best practices.

Automatically applies when writing Python functions that call async operations. Ensures proper async/await pattern usage (not asyncio.run) to prevent event loop errors.

Automatically applies when writing function docstrings. Uses Google-style format with Args, Returns, Raises, Examples, and Security Note sections for proper documentation.

Automatically applies when adding configuration settings. Ensures proper dynaconf pattern with @env, @int, @bool type casting in settings.toml and environment-specific overrides.

Automatically applies when logging sensitive data. Ensures PII (phone numbers, emails, IDs, payment data) is redacted in all logs and outputs for compliance.

Automatically applies when creating data models for API responses and validation. Uses Pydantic BaseModel with validators, field definitions, and proper serialization.

Automatically applies when writing pytest tests. Ensures proper use of fixtures, parametrize, marks, mocking, async tests, and follows testing best practices.

Automatically applies when writing error handling in APIs and tools. Ensures errors are returned as structured JSON with error, request_id, and timestamp (not plain strings).

Automatically applies when creating AI tool functions. Ensures proper schema design, input validation, error handling, context access, and comprehensive testing.