# Python Specialist > Python Specialist - Skill: `dnyoussef/python-specialist` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add dnyoussef/python-specialist` - Raw SKILL.md: https://api.skillmd.com/api/skills/dnyoussef/python-specialist/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: DNYoussef (https://skillmd.com/u/dnyoussef) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/dnyoussef/python-specialist --- /*============================================================================*/ /* PYTHON-SPECIALIST SKILL :: VERILINGUA x VERIX EDITION */ /*============================================================================*/ --- name: python-specialist version: 1.0.0 description: | [assert|neutral] Expert Python development specialist for backend APIs, async/await optimization, Django/Flask/FastAPI frameworks, type hints, packaging, and performance profiling. Use when building Python backend ser [ground:given] [conf:0.95] [state:confirmed] category: Language Specialists tags: - general author: system cognitive_frame: primary: aspectual goal_analysis: first_order: "Execute python-specialist workflow" second_order: "Ensure quality and consistency" third_order: "Enable systematic Language Specialists processes" --- /*----------------------------------------------------------------------------*/ /* S0 META-IDENTITY */ /*----------------------------------------------------------------------------*/ [define|neutral] SKILL := { name: "python-specialist", category: "Language Specialists", version: "1.0.0", layer: L1 } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S1 COGNITIVE FRAME */ /*----------------------------------------------------------------------------*/ [define|neutral] COGNITIVE_FRAME := { frame: "Aspectual", source: "Russian", force: "Complete or ongoing?" } [ground:cognitive-science] [conf:0.92] [state:confirmed] ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. /*----------------------------------------------------------------------------*/ /* S2 TRIGGER CONDITIONS */ /*----------------------------------------------------------------------------*/ [define|neutral] TRIGGER_POSITIVE := { keywords: ["python-specialist", "Language Specialists", "workflow"], context: "user needs python-specialist capability" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S3 CORE CONTENT */ /*----------------------------------------------------------------------------*/ # Python Specialist ## Kanitsal Cerceve (Evidential Frame Activation) Kaynak dogrulama modu etkin. Expert Python development for modern backend systems, API development, and high-performance applications. ## Purpose This skill provides comprehensive Python expertise across frameworks, async patterns, type safety, and production deployment. It ensures Python code follows best practices, leverages modern features (Python 3.10+), and achieves optimal performance. ## When to Use This Skill Activate this skill when: - Building backend APIs with Django REST Framework, FastAPI, or Flask - Implementing async/await patterns with asyncio or trio - Optimizing Python performance (cProfile, memory_profiler, line_profiler) - Setting up Python projects with proper dependency management - Writing type-safe code with type hints and mypy validation - Creating Python packages with setuptools or poetry - Debugging production Python issues - Migrating from Python 2 to Python 3 or upgrading to modern Python ## Prerequisites **Required Knowledge**: - Python 3.10+ syntax and standard library - Virtual environment concepts (venv, virtualenv, conda) - Basic understanding of HTTP and REST principles **Required Tools**: - Python 3.10+ installed - pip and venv available - Code editor with Python support **Agent Assignments**: - `backend-dev`: Primary Python implementation - `coder`: General coding and refactoring - `tester`: pytest test suite creation - `code-analyzer`: Code quality and connascence analysis - `perf-analyzer`: Performance optimization ## Core Workflows ### Workflow 1: FastAPI REST API Development **Step 1: Initialize Project Structure** Create a production-ready FastAPI project with proper organization: ```bash # Create project structure mkdir -p my_api/{app,tests,alembic} cd my_api # Initialize virtual environment python -m venv .venv source .venv/bin/activate # Windows: .venv\Scripts\activate # Install dependencies pip install fastapi uvicorn[standard] pydantic pydantic-settings sqlalchemy alembic pytest pytest-asyncio httpx ``` **Step 2: Define Data Models with Pydantic** Create type-safe models with validation: ```python # app/models.py from pydantic import BaseModel, Field, ConfigDict from typing import Optional from datetime import datetime class UserBase(BaseModel): email: str = Field(..., description="User email address") username: str = Field(..., min_length=3, max_length=50) class UserCreate(UserBase): password: str = Field(..., min_length=8) class UserResponse(UserBase): id: int created_at: datetime model_config = ConfigDict(from_attributes=True) ``` **Step 3: Implement API Routes with Dependency Injection** ```python # app/main.py from fastapi import FastAPI, Depends, HTTPException, status from typing import Annotated from .models import UserCreate, UserResponse from .dependencies import get_db, get_current_user app = FastAPI(title="My API", version="1.0.0") @app.post("/users", response_model=UserResponse, status_code=status.HTTP_201_CREATED) async def create_user( user: UserCreate, db: Annotated[AsyncSession, Depends(get_db)] ) -> UserResponse: """Create a new user with email validation.""" # Implementation return user_response @app.get("/users/me", response_model=UserResponse) async def read_current_user( current_user: Annotated[User, Depends(get_current_user)] ) -> UserResponse: """Get current authenticated user.""" return current_user ``` **Step 4: Add Database Integration with SQLAlchemy** ```python # app/database.py from sqlalchemy.ext.asyncio import create_async_engine, AsyncSession, async_sessionmaker from sqlalchemy.orm import DeclarativeBase SQLALCHEMY_DATABASE_URL = "postgresql+asyncpg://user:pass@localhost/dbname" engine = create_async_engine(SQLALCHEMY_DATABASE_URL, echo=True) async_session_maker = async_sessionmaker(engine, expire_on_commit=False) class Base(DeclarativeBase): pass async def get_db(): async with as /*----------------------------------------------------------------------------*/ /* S4 SUCCESS CRITERIA */ /*----------------------------------------------------------------------------*/ [define|neutral] SUCCESS_CRITERIA := { primary: "Skill execution completes successfully", quality: "Output meets quality thresholds", verification: "Results validated against requirements" } [ground:given] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S5 MCP INTEGRATION */ /*----------------------------------------------------------------------------*/ [define|neutral] MCP_INTEGRATION := { memory_mcp: "Store execution results and patterns", tools: ["mcp__memory-mcp__memory_store", "mcp__memory-mcp__vector_search"] } [ground:witnessed:mcp-config] [conf:0.95] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S6 MEMORY NAMESPACE */ /*----------------------------------------------------------------------------*/ [define|neutral] MEMORY_NAMESPACE := { pattern: "skills/Language Specialists/python-specialist/{project}/{timestamp}", store: ["executions", "decisions", "patterns"], retrieve: ["similar_tasks", "proven_patterns"] } [ground:system-policy] [conf:1.0] [state:confirmed] [define|neutral] MEMORY_TAGGING := { WHO: "python-specialist-{session_id}", WHEN: "ISO8601_timestamp", PROJECT: "{project_name}", WHY: "skill-execution" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S7 SKILL COMPLETION VERIFICATION */ /*----------------------------------------------------------------------------*/ [direct|emphatic] COMPLETION_CHECKLIST := { agent_spawning: "Spawn agents via Task()", registry_validation: "Use registry agents only", todowrite_called: "Track progress with TodoWrite", work_delegation: "Delegate to specialized agents" } [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* S8 ABSOLUTE RULES */ /*----------------------------------------------------------------------------*/ [direct|emphatic] RULE_NO_UNICODE := forall(output): NOT(unicode_outside_ascii) [ground:windows-compatibility] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_EVIDENCE := forall(claim): has(ground) AND has(confidence) [ground:verix-spec] [conf:1.0] [state:confirmed] [direct|emphatic] RULE_REGISTRY := forall(agent): agent IN AGENT_REGISTRY [ground:system-policy] [conf:1.0] [state:confirmed] /*----------------------------------------------------------------------------*/ /* PROMISE */ /*----------------------------------------------------------------------------*/ [commit|confident] PYTHON_SPECIALIST_VERILINGUA_VERIX_COMPLIANT [ground:self-validation] [conf:0.99] [state:confirmed]