David Ichim ichim-david

Comparing Approaches for GitHub Copilot + Java

Overview

When GitHub Copilot analyzes or generates Java code, the approach used for code intelligence directly impacts performance (speed of interaction), output quality (correctness and idiomatic usage of Java, Spring, and related frameworks), resource consumption (tokens and premium requests), and cost.

This document compares six approaches across these dimensions.

Twitter Mention Blocklist Policy

Public-safe version. Redacted. No handles, no links, no callouts.

Block immediately

Strongly derogatory replies
Crypto spam, token shills, wallet/contract junk
Clear AI slop or reply-bot behavior
Repetitive low-context spam

description	Mini Beast

<tool_preambles>

Always begin by rephrasing the user's goal in a friendly, clear, and concise manner, before calling any tools.
Each time you call a tool, provide the user with a one-sentence narration of why you are calling the tool. You do NOT need to tell them WHAT you are doing, just WHY you are doing it.
- CORRECT: "First, let me open the webview template to see how to add a UI control for showing the "refresh available" indicator and trigger refresh from the webview."
- INCORRECT: "I'll open the webview template to see how to add a UI control for showing the "refresh available" indicator and trigger refresh from the webview. I'm going to read settingsWebview.html."
ALWAYS use a todo list to track your progress using the todo list tool.

layout	post
title	We need practical AI workflows
date	2025-11-17 08:40:00 +0000
categories	posts
permalink	/posts/promptfiles-vs-instructions-vs-agents/

In VS Code, there are 3 main ways that you can guide Copilot AI to help you with software development tasks: Prompt Files, Custom Instructions, and Agents. Each of these has slightly different use cases, and in this post I want to try and clear up when you might want to use each one because it's not always obvious.

description

Researches topics in depth with comprehensive source analysis and synthesis

argument-hint

What topic or question would you like researched?

tools

search

new

Azure MCP/search

github/search_code

github/search_issues

github/search_repositories

youtube/*

runSubagent

usages

vscodeAPI

problems

changes

testFailure

fetch

githubRepo

handoffs

label	agent	prompt
Save Research	agent	Save the research findings to a markdown file, as is

GitHub Copilot CLI SKILL

Use models like Gemini 3 Pro, GPT-5.1, and GPT-5.1-Codex from within Claude by invoking GitHub Coplit CLI.

Installation

Create ~/.claude/skills/github-copilot
Save SKILL.md to ~/.claude/skills/github-copilot/SKILL.md

AGENTS.md — Tool Selection

When you need to call tools from the shell, use this rubric:

Find files by file name: fd
Find files with path name: fd -p <file-path>
List files in a directory: fd . <directory>
Find files with extension and pattern: fd -e <extension> <pattern>
Find Text: rg (ripgrep)
Find Code Structure: ast-grep

Do You like to YOLO?

You can auto-approve tool calls and terminal commands in Copilot’s Agent mode.

With ❤️ from Eleanor < @intellectronica > ( OKIGU Consulting | Elite AI-assisted Coding )

	#!/usr/bin/env bash
	# Atomic commit helper to prevent AI's from accidentally deleting
	# changes from dangerous commands. I only allow AI agents to commit with this flow.
	# I disallow AI agents from git reset, git checkout, and git revert because
	# they are dangerous operations when multiple agents (or I) are editing
	# files in the same work tree.
	set -euo pipefail

	usage() {
	cat >&2 <<'EOF'

	import { useState, useEffect } from 'preact/hooks';

	const cache = new Map<string, any>();
	const inflight = new Map<string, Promise<any>>();

	export interface SWRResponse<T> {
	data: T \| undefined;
	error: Error \| null;
	loading: boolean;
	}