julio-cesar-rodrigues/juliocesarrodrigues icon
public
Published on 8/21/2025
hexcp1.0

ia feita para programar bots e websites

Rules
Prompts
Models
Context
Data
ollama qwen2.5-coder 7b model icon

qwen2.5-coder 7b

ollama

gemini Gemini 2.0 Flash model icon

Gemini 2.0 Flash

gemini

1048kinput·8.192koutput
Você é uma inteligência artificial especialista em desenvolvimento de bots (para Discord, Telegram, etc) e websites (front-end e back-end). Seu papel é ser um programador assistente que **escreve, corrige e otimiza código com foco em performance, segurança e boas práticas modernas**. 

Você tem profundo conhecimento nas seguintes tecnologias:
- JavaScript, TypeScript, Python
- Node.js, Express.js, FastAPI
- HTML, CSS, Tailwind, React
- Discord.js, Telegraf, Puppeteer
- MongoDB, Firebase, PostgreSQL

Sempre que receber uma tarefa, siga este formato:

1. **Analise o objetivo**
2. **Gere o código funcional com comentários úteis**
3. **Explique o funcionamento de forma objetiva**
4. **Sugira melhorias ou extensões**

⚠️ Regras importantes:
- Sempre escreva código funcional e testável.
- Nunca gere códigos desatualizados ou inseguros.
- Se o usuário quiser algo simples, ainda assim ofereça uma versão otimizada.
- Quando possível, modularize e reutilize componentes.
- Ajude o usuário a entender o que está fazendo.

Seu objetivo é acelerar o desenvolvimento com foco profissional, sem depender de frameworks mágicos ou soluções prontas.
Matplotlibhttps://matplotlib.org/stable/

Prompts

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New Component
Create a new Angular component
Please create a new Angular component following these guidelines:
- Include JSDoc comments for component and inputs/outputs
- Implement proper lifecycle hooks
- Include TypeScript interfaces for models
- Follow container/presentational component pattern where appropriate
- Include unit tests with Jasmine/Karma in a separate test file
- Make sure to create separate files for any services, pipes, modules, and directives
New Module
Create a new PyTorch module
Please create a new PyTorch module following these guidelines:
- Include docstrings for the model class and methods
- Add type hints for all parameters
- Add basic validation in __init__
New Component
Create a new Svelte component
Please create a new Svelte component following these guidelines:
- Include JSDoc comments for component and props
- Include basic error handling and loading states
- ALWAYS add a TypeScript prop interface
Next.js Security Review
Check for any potential security vulnerabilities in your code
Please review my Next.js code with a focus on security issues.

Use the below as a starting point, but consider any other potential issues

You do not need to address every single area below, only what is relevant to the user's code.

1. Data Exposure:
- Verify Server Components aren't passing full database objects to Client Components
- Check for sensitive data in props passed to 'use client' components
- Look for direct database queries outside a Data Access Layer
- Ensure environment variables (non NEXT_PUBLIC_) aren't exposed to client

2. Server Actions ('use server'):
- Confirm input validation on all parameters
- Verify user authentication/authorization checks
- Check for unencrypted sensitive data in .bind() calls

3. Route Safety:
- Validate dynamic route parameters ([params])
- Check custom route handlers (route.ts) for proper CSRF protection
- Review middleware.ts for security bypass possibilities

4. Data Access:
- Ensure parameterized queries for database operations
- Verify proper authorization checks in data fetching functions
- Look for sensitive data exposure in error messages

Key files to focus on: files with 'use client', 'use server', route.ts, middleware.ts, and data access functions.
Page
Creates a new Nuxt.js page based on the description provided.
Create a new Nuxt.js page based on the following description.
Page
Creates a new Next.js page based on the description provided.
Create a new Next.js page based on the following description.
Exploratory Data Analysis
Initial data exploration and key insights
Create an exploratory data analysis workflow that includes:

Data Overview:
- Basic statistics (mean, median, std, quartiles)
- Missing values and data types
- Unique value distributions

Visualizations:
- Numerical: histograms, box plots
- Categorical: bar charts, frequency plots
- Relationships: correlation matrices
- Temporal patterns (if applicable)

Quality Assessment:
- Outlier detection
- Data inconsistencies
- Value range validation

Insights & Documentation:
- Key findings summary
- Data quality issues
- Variable relationships
- Next steps recommendations
- Reproducible Jupyter notebook

The user has provided the following information:
Review
Review changes
Please review the current code changes looking for:

- Memory leaks (unsubscribed observables)
- Proper change detection strategy
- Proper use of async pipe
- Proper error handling

Format the review as:
```
## <FILENAME>
- <ISSUE>
...
- <ISSUE>
```
New LanceDB
Create a new LanceDB table
Create a new LanceDB table with the description given below. It should follow these rules:
  - Explicitly define the schema of the table with PyArrow
  - Use dataframes to store and manipulate data
  - If there is a column with embeddings, call it "vector"

Here is a basic example: ```python import lancedb import pandas as pd import pyarrow as pa
# Connect to the database db = lancedb.connect("data/sample-lancedb")
# Create a table with an empty schema schema = pa.schema([pa.field("vector", pa.list_(pa.float32(), list_size=2))]) tbl = db.create_table("empty_table", schema=schema)
# Insert data into the table data = pd.DataFrame({"vector": [[1.0, 2.0], [3.0, 4.0]]}) tbl.add(data) ```
Data Pipeline Development
Create robust and scalable data processing pipelines
Generate a data processing pipeline with these requirements:

Input:
- Data loading from multiple sources (CSV, SQL, APIs)
- Input validation and schema checks
- Error logging for data quality issues

Processing:
- Standardized cleaning (missing values, outliers, types)
- Memory-efficient operations for large datasets
- Numerical transformations using NumPy
- Feature engineering and aggregations

Quality & Monitoring:
- Data quality checks at key stages
- Validation visualizations with Matplotlib
- Performance monitoring

Structure:
- Modular, documented code with error handling
- Configuration management
- Reproducible in Jupyter notebooks
- Example usage and tests

The user has provided the following information:
Client component
Create a client component.
Create a client component with the following functionality. If writing this as a server component is not possible, explain why.
API route inspection
Analyzes API routes for security issues
Review this API route for security vulnerabilities. Ask questions about the context, data flow, and potential attack vectors. Be thorough in your investigation.
Write Unit Test
Write Laravel Unit Tests for attached code
Use Laravel to write a comprehensive suite of unit tests for the attached code.
Ensure that your responses are concise and technical, providing precise PHP examples that adhere to Laravel best practices and conventions. Apply object-oriented programming principles with a focus on SOLID design, prioritizing code iteration and modularization over duplication.
When writing unit tests, select descriptive names for test methods and variables, and use directories in lowercase with dashes following Laravel's conventions (e.g., app/Http/Controllers). Prioritize the use of dependency injection and service containers to create maintainable code that leverages PHP 8.1+ features.
Conform to PSR-12 coding standards and enforce strict typing using declare(strict_types=1);. Utilize Laravel's testing tools, particularly PHPUnit, to efficiently construct tests that validate the code functionality. Implement error handling and logging in your tests using Laravel's built-in features, and employ middleware testing techniques for request filtering and modification validation.
Ensure that your test cases cover the interactions using Laravel's Eloquent ORM and query builder, applying suitable practices for database migrations and seeders in a testing environment. Manage dependencies using the latest stable versions of Laravel and Composer, and rely on Eloquent ORM over raw SQL queries wherever applicable.
Adopt the Repository pattern for testing the data access layer, utilize Laravel's built-in authentication and authorization features in your tests, and implement job queue scenarios for long-running task verifications. Incorporate API versioning checks for endpoint tests and use Laravel's localization features to simulate multi-language support.
Use Laravel Mix in your testing workflow for asset handling and ensure efficient indexing for database operations tested within your suite. Leverage Laravel's pagination features and implement comprehensive error logging and monitoring in your test scenarios. Follow Laravel's MVC architecture, ensure route definitions are verified through tests, and employ Form Requests for validating request data.
Utilize Laravel's Blade engine during the testing of view components and confirm the establishment of database relationships through Eloquent. Implement API resource transformations and mock event and listener systems to maintain decoupled code functionality in your tests. Finally, utilize database transactions during tests to ensure data integrity, and use Laravel's scheduling features to validate recurring tasks.

Context

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@diff
Reference all of the changes you've made to your current branch
@codebase
Reference the most relevant snippets from your codebase
@url
Reference the markdown converted contents of a given URL
@folder
Uses the same retrieval mechanism as @Codebase, but only on a single folder
@terminal
Reference the last command you ran in your IDE's terminal and its output
@code
Reference specific functions or classes from throughout your project
@file
Reference any file in your current workspace

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${{ secrets.julio-cesar-rodrigues/juliocesarrodrigues/continuedev/s3-dev-data/AWS_SERVER_URL }}

MCP Servers

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Exa

npx -y exa-mcp-server