flask

bobmatnyc/claude-mpm-skills · updated Apr 8, 2026

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$npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill flask
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summary

Flask is a micro-framework for Python web development, designed for building microservices, REST APIs, and flexible web applications. Its minimalist core and extensive extension ecosystem make it ideal for projects requiring lightweight architecture, rapid development, and full control over components.

skill.md

Flask - Lightweight Python Web Framework

Overview

Flask is a micro-framework for Python web development, designed for building microservices, REST APIs, and flexible web applications. Its minimalist core and extensive extension ecosystem make it ideal for projects requiring lightweight architecture, rapid development, and full control over components.

Key Features:

  • Micro-framework philosophy (minimal core, extensible)
  • Flask-RESTful for API development
  • Blueprints for modular application structure
  • SQLAlchemy integration via Flask-SQLAlchemy
  • Jinja2 templating engine
  • Built-in development server with auto-reload
  • Werkzeug WSGI toolkit foundation
  • Large extension ecosystem (Flask-Login, Flask-JWT, Flask-CORS)
  • Production deployment with Gunicorn/uWSGI

Installation:

# Basic Flask
pip install flask

# Flask with common extensions
pip install flask flask-restful flask-sqlalchemy flask-login flask-cors

# With database support
pip install flask flask-sqlalchemy psycopg2-binary  # PostgreSQL

# Full microservices stack
pip install flask flask-restful marshmallow flask-jwt-extended redis

Basic Application Patterns

1. Minimal Flask App

# app.py
from flask import Flask, jsonify, request

app = Flask(__name__)

@app.route('/')
def hello():
    return jsonify({"message": "Hello, World!"})

@app.route('/api/users/<int:user_id>')
def get_user(user_id):
    return jsonify({"id": user_id, "name": f"User {user_id}"})

@app.route('/api/users', methods=['POST'])
def create_user():
    data = request.get_json()
    return jsonify({"id": 123, **data}), 201

if __name__ == '__main__':
    app.run(debug=True, host='0.0.0.0', port=5000)

Run:

# Development server
python app.py

# Or using flask CLI
export FLASK_APP=app.py
export FLASK_ENV=development
flask run

# Custom port
flask run --port 8000 --host 0.0.0.0

2. Application Factory Pattern

Recommended for production and testing:

# app/__init__.py
from flask import Flask
from app.config import Config
from app.extensions import db, migrate, jwt

def create_app(config_class=Config):
    """Application factory pattern."""
    app = Flask(__name__)
    app.config.from_object(config_class)

    # Initialize extensions
    db.init_app(app)
    migrate.init_app(app, db)
    jwt.init_app(app)

    # Register blueprints
    from app.routes import api_bp, auth_bp
    app.register_blueprint(api_bp, url_prefix='/api')
    app.register_blueprint(auth_bp, url_prefix='/auth')

    return app

# app/extensions.py
from flask_sqlalchemy import SQLAlchemy
from flask_migrate import Migrate
from flask_jwt_extended import JWTManager

db = SQLAlchemy()
migrate = Migrate()
jwt = JWTManager()

# app/config.py
import os

class Config:
    SECRET_KEY = os.environ.get('SECRET_KEY') or 'dev-secret-key'
    SQLALCHEMY_DATABASE_URI = os.environ.get('DATABASE_URL') or 'sqlite:///app.db'
    SQLALCHEMY_TRACK_MODIFICATIONS = False
    JWT_SECRET_KEY = os.environ.get('JWT_SECRET_KEY') or 'jwt-secret'

class DevelopmentConfig(Config):
    DEBUG = True
    TESTING = False

class ProductionConfig(Config):
    DEBUG = False
    TESTING = False

# run.py
from app import create_app

app = create_app()

if __name__ == '__main__':
    app.run()

Run:

export FLASK_APP=run.py
flask run

3. Request and Response Handling

from flask import Flask, request, jsonify, make_response, abort

app = Flask(__name__)

@app.route('/api/data', methods=['GET', 'POST'])
def handle_data():
    # GET request
    if request.method == 'GET':
        # Query parameters
        page = request.args.get('page', 1, type=int)
        limit = request.args.get('limit', 10, type=int)

        return jsonify({
            "page": page,
            "limit": limit,
            "data": [...]
        })

    # POST request
    if request.method == 'POST':
        # JSON body
        data = request.get_json()

        # Validation
        if not data or 'name' not in data:
            abort(400, description="Missing required field: name")

        # Custom response with headers
        response = make_response(jsonify({"id": 1, **data}), 201)
        response.headers['X-Custom-Header'] = 'value'
        return response

# Error handling
@app.errorhandler(404)
def not_found(error):
    return jsonify({"error": "Resource not found"
how to use flask

How to use flask on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add flask
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/bobmatnyc/claude-mpm-skills --skill flask

The skills CLI fetches flask from GitHub repository bobmatnyc/claude-mpm-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/flask

Reload or restart Cursor to activate flask. Access the skill through slash commands (e.g., /flask) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.660 reviews
  • Shikha Mishra· Dec 28, 2024

    flask reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Harper Thompson· Dec 28, 2024

    Registry listing for flask matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Harper Kapoor· Dec 28, 2024

    We added flask from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • Evelyn Taylor· Dec 12, 2024

    flask fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Arjun Chawla· Dec 8, 2024

    flask has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Yash Thakker· Nov 19, 2024

    I recommend flask for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Kwame Perez· Nov 19, 2024

    Useful defaults in flask — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Ama Bhatia· Nov 19, 2024

    Keeps context tight: flask is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sophia Robinson· Nov 3, 2024

    flask is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Mei Huang· Oct 22, 2024

    Keeps context tight: flask is the kind of skill you can hand to a new teammate without a long onboarding doc.

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