LucasJantschChitolina

Code Review Checklist

Overview

Comprehensive checklist for conducting thorough code reviews to ensure quality, security, and maintainability.

Don't run tests or linting, this is done automatically. Just inspect the code.

Review Categories

imWildCat

# from: <https://x.com/iannuttall/status/1965090297630826931>
cdx() {
  if [[ "$1" == "update" ]]; then
    npm install -g @openai/codex@latest
  else
    codex -m gpt-5-codex --yolo -c model_reasoning_effort="high" -c model_reasoning_summary_format=experimental --search "$@"
  fi
}

# Note: with auto confirmation. use at your own risk. thanks!

VictorTaelin

#include <ctype.h>
#include <stdbool.h>
#include <stdint.h>
#include <stdio.h>
#include <stdlib.h>
#include <string.h>

typedef struct {
  const char* input;
  size_t index;

agokrani

'system': 
[
  {
     'type': 'text', 
     'text': "You are Claude Code, Anthropic's official CLI for Claude.", 
     'cache_control': {'type': 'ephemeral'}
  }, 
  {
     'type': 'text',
     'text': 'You are an interactive CLI tool that helps users with software engineering tasks. 

vacmar01

# /// script
# dependencies = [
#   "dspy",
#   "rich"
# ]
# ///

import dspy
import os
import inspect

fabiodr

import {
  Tensor,
  TextEncoder,
  TextProcessor,
  ImageEncoder,
  env,
} from "./uform-encoder.js";
import imageUrl from "../assets/images/monalisa.png";

const model = "fp32"; // "v3", "fp16" or "fp32"

prrao87

System message:

Your input fields are:
1. `sample` (Scholar): A sample scholar record
2. `reference_records` (list[Reference]): A list of reference records from the official Nobel Prize API
Your output fields are:
1. `output` (int): Most similar reference record to the sample record
2. `confidence` (Literal['high', 'low']): The confidence level of mapping the sample record to one of the reference records
All interactions will be structured in the following way, with the appropriate values filled in.

kmad

# /// script
# dependencies = [
#   "requests<3",
#   "rich",
#   "dspy",
#   "python-dotenv", 
#   "pymupdf",
# ]
# ///

viksit

# Code for the blog post
# Optimizing Tool Selection for LLM Workflows: Differentiable Programming with PyTorch and DSPy

# How local, learnable routers can reduce token overhead, lower costs, and bring structure back to agentic workflows.

# https://viksit.substack.com/p/optimizing-tool-selection-for-llm
# Ping @viksit on X with feedback/questions

# ----------------------------------------------------
import torch, torch.nn as nn, torch.nn.functional as F

hamelsmu

#!/usr/bin/env python3
# /// script
# requires-python = ">=3.9"
# dependencies = [
#     "httpx",
#     "typer",
#     "rich",
# ]
# ///
"""