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𝗣𝗮𝗶𝗱 𝗖𝗼𝘂𝗿𝘀𝗲 𝗙𝗥𝗘𝗘 (PART - 1)
1. Artificial Intelligence
2. Machine Learning
3. Prompt Engineering
4. Claude,Chatgpt,Grok
5. Data Analytics
6. AWS Certified
7. Data Science
8. BIG DATA
9. Python
10. Ethical Hacking
(72 Hours only )
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Most people think using Claude Code is about writing better prompts.
It’s not.
The real unlock is structuring your repository so Claude can think like an engineer.
If your repo is messy, Claude behaves like a chatbot.
If your repo is structured, Claude behaves like a developer living inside your codebase.
Your project only needs 4 things:
• the why → what the system does
• the map → where things live
• the rules → what’s allowed / forbidden
• the workflows → how work gets done
I call this:
The Anatomy of a Claude Code Project 👇
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1️⃣ CLAUDE.md = Repo Memory (Keep it Short)
This file is the north star for Claude.
Not a massive document.
Just three things:
• Purpose → why the system exists
• Repo map → how the project is structured
• Rules + commands → how Claude should operate
If CLAUDE.md becomes too long, the model starts missing critical signals.
Clarity beats size.
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2️⃣ .claude/skills/ = Reusable Expert Modes
Stop repeating instructions in prompts.
Turn common workflows into reusable skills.
Examples:
• code review checklist
• refactoring playbook
• debugging workflow
• release procedures
Now Claude can switch into specialized modes instantly.
Result:
More consistent outputs across sessions and teammates.
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3️⃣ .claude/hooks/ = Guardrails
Models forget.
Hooks don’t.
Use hooks for things that must always happen automatically.
Examples:
• run formatters after edits
• trigger tests after core changes
• block sensitive directories (auth, billing, migrations)
Hooks turn AI workflows into reliable engineering systems.
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4️⃣ docs/ = Progressive Context
Don’t overload prompts with information.
Instead, let Claude navigate your documentation.
Examples:
• architecture overview
• ADRs (engineering decisions)
• operational runbooks
Claude doesn’t need everything in memory.
It just needs to know where truth lives.
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5️⃣ Local CLAUDE.md for Critical Modules
Some areas of your system have hidden complexity.
Add local context files there.
Example:
src/auth/CLAUDE.md
src/persistence/CLAUDE.md
infra/CLAUDE.md
Now Claude understands the danger zones exactly when it works in them.
This dramatically reduces mistakes.
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Here’s the shift most people miss:
Prompting is temporary.
Structure is permanent.
Once your repository is designed for AI:
Claude stops acting like a chatbot...
…and starts behaving like a project-native engineer. 🚀
🩺 Un total de 14 centros de salud de la @ComunidadMadrid se suman al proyecto IntERC-AP Madrid para reforzar el diagnóstico precoz de la enfermedad renal crónica.
✅ Se estima que dos de cada tres personas con esta patología no están diagnosticadas.
https://t.co/E8WhVIO0EZ
i have fully dropped Claude Code for OpenCode
i donʼt use Opus 4.5,
i use GLM-4.7 and MiniMax-M2.1
theyʼre opensource and can be self-hosted
nobody can nerf my models or rug pull me
nobody should be able to do that to your intelligence
p.s. buy a GPU and run your LLMs locally
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So I wrote down a better-formatted version of my post on Deep Agents.
I added it to the AI Agents section of the Prompt Engineering Guide.
If you are building with AI Agents, this is a must-read.
I also added links to other useful references.
promptingguide .ai/agents
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