Pydantic plugins

Centers Python backend development around async patterns, FastAPI, Django, and modern tooling, providing architectural guidance, testing strategies with pytest, and production best practices for scalable APIs and services.

Build and orchestrate AI agents using LangChain, LangGraph, and Deep Agents — scaffold, develop, deploy, and manage stateful agent workflows with memory, RAG pipelines, human-in-the-loop approval, and parallel task execution.

Scaffold new MCP servers in TypeScript or Python with configurable transports, Zod/Pydantic validation, error handling, and example tools/prompts, then add tools to existing servers via guided steps for schemas, handlers, registration, testing, and docs.

Profile Python performance bottlenecks with cProfile/py-spy, analyze pytest test suites for quality/coverage, check async code for issues/patterns, lint/fix with ruff, optimize algorithms/memory, generate unit/integration tests, and package/publish projects using uv/pyproject.toml.

Draft, analyze, and file USPTO, EPO, and PCT patent applications from invention disclosures. Conduct prior art searches across 100M+ patents via BigQuery, assess patentability and compliance (35 USC, EPC, MPEP), generate patent-style technical diagrams, and prepare IDS documents—all within Claude Code.

Build production-grade AI agents with Pydantic AI, including tools, structured output, streaming, testing, multi-agent patterns, hooks, lifecycle interception, and Logfire observability.

Establishes opinionated Python 3.11+ engineering standards with SOLID principles, strict typing, pytest testing, ruff linting; automates TDD workflows, routes to specialists for CLI apps (Typer/Rich/Textual), web APIs (FastAPI/Flask/Django), data pipelines, packaging, code reviews, and PyPI CI/CD deployment.

Develop DSPy applications end-to-end: construct typed programs, optimize via few-shot/MIPROv2/GEPA/SIMBA, evaluate with metrics, deploy with caching/streaming, and integrate RAG, ReAct agents, MCP tools, and Haystack.

Engineer robust ETL pipelines: clean messy CSVs/Parquet, infer schemas, profile datasets, detect anomalies, validate quality with Pydantic/Pandera/Great Expectations, implement incremental patterns, generate dbt models/SQL migrations/tests, and orchestrate autonomous backfills/pipeline testing via agents and CLI commands.

Build and review AI agent applications using PydanticAI, LangGraph, DeepAgents, and Vercel AI SDK. Guides architectural decisions, implements agents, reviews code for bugs and anti-patterns, and builds streaming chat interfaces.

Process pandas DataFrames with AI: rank, score, classify, deduplicate, merge, and filter rows using natural language instructions. Dispatch AI researchers to enrich datasets at scale via Python SDK or MCP server.

Build and maintain Python 3.11+ CLI applications with Typer/Rich, following TDD workflows with pytest, modern type hints, and pyproject.toml packaging. Includes code review, debugging, test analysis, CI/CD setup, and documentation generation.

Build modern Python web apps with Django and FastAPI, define SQLAlchemy models and Alembic migrations, write pytest tests, debug errors, and review code quality using specialized agents that orchestrate database tasks and full codebase reviews.

Author structured configuration files (CLAUDE.md, hooks, rules, skills, commands, and scripts) for Claude Code projects, with best practices for prompts, structured output, CLI design, multi-agent systems, and LLM evaluation.

Develop, configure, deploy, and debug serverless Python apps on Modal.com: set up GPU-accelerated functions (T4-H200) with autoscaling, create FastAPI/ASGI endpoints and cron schedules, manage sandboxes/volumes/secrets, integrate GitHub Actions CI/CD, and troubleshoot failures for AI/ML workloads.

Summon Python specialists to scaffold production Django/FastAPI projects with uv/Docker/PostgreSQL, enforce Mypy/PEP8/security reviews, audit codebases for multi-agent parallelization, implement Celery tasks/WebSockets, and generate pytest strategies—all using 2025 patterns and official docs.

Provides a comprehensive collection of DSPy skills for building, optimizing, debugging, and deploying AI systems—covering signatures, modules, pipelines, retrieval-augmented generation, agents, evaluations, guardrails, monitoring, and deployment patterns

Build, design, and troubleshoot Python CLI applications with Typer using type-hint driven workflows for command structures, input validation, pytest testing, and distribution packaging.

Secure Python-based MCP servers and multi-agent pipelines by implementing OAuth 2.0 flows with PKCE and providers like Google/GitHub, enforcing 5-layer defenses against prompt/SQL injections and unauthorized access via RBAC, and achieving GDPR/CCPA compliance with consent management, data minimization patterns, and regulatory checklists.

Build production-ready AI agents and multi-agent teams with the Agno framework by creating workflows and MCP integrations, adding tools, memory, debugging, and structured outputs, then deploy them via AgentOS using FastAPI.

Scaffold opinionated Django projects with one-file-per-model organization, Ninja APIs via domain-grouped routers and Pydantic schemas, Unfold admin with HTMX and Tailwind, pytest tests using factory_boy, Dynaconf config, uv deps, and Docker setup. Delegate code reviews to the agent to enforce patterns after changes.

Enforce strict code quality conventions across Python, TypeScript, and Go codebases — naming precision, casing rules, import discipline, declaration ordering, symmetry, dead code elimination, and magic value refactoring. Audit repositories or files for violations and generate fix reports.