Claude has a hidden superpower most beginners never use.
Not better prompts.
Skills.
I found 26 free Claude Skills that can turn Claude into a specialized AI workflow engine:
🧠 /my-viral-post → Create content in your style
🔬 /deep-research-synthesizer → Research with citations
🎨 /infographic-builder → Create social infographics
📊 /xlsx → Work with spreadsheets
🧹 /humanizer → Make AI writing sound natural
🔎 /fact-checker → Verify claims
🎯 /prompt-master → Turn messy ideas into structured prompts
🗣️ /personal-voice → Learn your writing style
📑 /meeting-notes → Turn notes into action items
🚀 /deck-builder → Build presentation decks
🔗 /viral-recipe → Reverse-engineer viral content
🧩 /write-a-skill → Create your own Claude Skill
And that's just the start.
The interesting part?
Skills are reusable workflows, not one-off prompts.
Install once. Reuse whenever the task fits.
📌 Bookmark this cheat sheet.
♻️ Repost it for someone using Claude.
Which Claude Skill would you use first?
#ClaudeAI #Claude #AI #AITools #PromptEngineering #ArtificialIntelligence #GenerativeAI #Productivity #AIEngineering #Tech
If you read books and forget everything, this is for you.
NotebookLM can turn any book into action plans, memory notes, and usable insights.
Here are 12 prompts:
Back-to-school season is here and starting today, eligible college students can get a full year of Gemini on us:
- US students: 1 year of Google AI Pro at no cost
- 140+ countries: 1 year of Google AI Plus at no cost
Here’s what’s new for students �Back-to-school season is here and starting today, eligible college students can get a full year of Gemini on us:
- US students: 1 year of Google AI Pro at no cost
- 140+ countries: 1 year of Google AI Plus at no cost
Here’s what’s new for students �Back-to-school season is here and starting today, eligible college students can get a full year of Gemini on us:
- US students: 1 year of Google AI Pro at no cost
- 140+ countries: 1 year of Google AI Plus at no cost
Here’s what’s new for students �Back-to-school season is here and starting today, eligible college students can get a full year of Gemini on us:
- US students: 1 year of Google AI Pro at no cost
- 140+ countries: 1 year of Google AI Plus at no cost
Here’s what’s new for students �Back-to-school season is here and starting today, eligible college students can get a full year of Gemini on us:
- US students: 1 year of Google AI Pro at no cost
- 140+ countries: 1 year of Google AI Plus at no cost
Here’s what’s new for students �Back-to-school season is here and starting today, eligible college students can get a full year of Gemini on us:
- US students: 1 year of Google AI Pro at no cost
- 140+ countries: 1 year of Google AI Plus at no cost
Here’s what’s new for students 👇
A couple in Chennai booked a flat in 2019.
Paid ₹46 lakh.
The builder promised possession in 2022.
2022 came. No flat.
2023. No flat.
2024. No flat.
They kept paying EMIs on a home loan.
And rent the house they were living in.
For 6 years they paid double.
In 2025 they approached RERA.
The builder kept asking for extensions.
They moved to the consumer court.
2025 ruling:
The builder was ordered to refund ₹46 lakh.
Plus 19% interest from 2019.
That’s ₹46 lakh becoming ₹1.06 crore.
The builder was delayed by 6 years.
The court added ₹60 lakh in interest.
If your builder has delayed possession – you have two options: RERA or consumer court.
Both can order interest on your money.
Most homebuyers don’t know they can get interest.
How to become an AI Engineer in 6 months 🚀
MONTH 1 — Foundations 🐍
Learn:
• Python properly
• NumPy + Pandas
• SQL
• Git/GitHub
• APIs + JSON
• Linux/CLI basics
• Linear algebra
• Probability + statistics
Build:
→ Data analysis project
→ ML API with FastAPI
Goal: Become comfortable writing production-ish Python, not just notebooks.
---
MONTH 2 — Machine Learning 🧠
Master:
• Linear + logistic regression
• Decision trees
• Random Forest
• XGBoost
• SVM
• KNN
• Clustering
• Feature engineering
• Train/validation/test
• Cross-validation
• Precision/Recall/F1/AUC
• Hyperparameter tuning
Learn:
→ scikit-learn
Build:
→ End-to-end ML prediction system
Goal: Understand WHY a model works, not just ".fit()" it.
---
MONTH 3 — Deep Learning 🔥
Learn:
• Neural networks
• Backpropagation
• Activation functions
• Loss functions
• Optimizers
• Regularization
• CNNs
• Sequence models
• Attention
• Transformers
Learn:
→ PyTorch
Build:
→ Image/text classification system
Then understand:
Attention → Transformers → LLMs
This is the bridge into modern AI engineering.
---
MONTH 4 — LLM Engineering 🤖
Now go deep into GenAI:
• Tokens
• Embeddings
• Transformers
• LLM APIs
• Prompting
• Structured outputs
• Function/tool calling
• Streaming
• Model selection
• Context windows
• Cost + latency optimization
• Hugging Face
Then learn:
RAG
→ Chunking
→ Embeddings
→ Vector databases
→ Retrieval
→ Reranking
→ Grounded generation
→ RAG evaluation
Build:
🔥 “Chat with your documents” system
But don't stop at a basic chatbot.
---
MONTH 5 — Agents + Production ⚙️
Learn how modern AI systems actually DO things.
• Tool calling
• Agent loops
• Planning
• Memory
• Workflows
• Multi-step agents
• Agent evaluation
• Guardrails
• Context engineering
• MCP
• LangGraph / equivalent orchestration
Build:
🔥 An autonomous research/automation agent
Example:
User → Agent → Search → Tools → Data → Reason → Final answer
The important skill is not “using LangChain.”
It's understanding the architecture underneath it.
---
MONTH 6 — Production AI Engineer 🚀
Learn:
• FastAPI
• Docker
• Cloud deployment
• CI/CD
• MLflow
• Logging
• Monitoring
• Model evaluation
• LLM observability
• Rate limiting
• Caching
• Authentication
• Security
• Cost optimization
Deploy everything.
Your GitHub should now contain:
1️⃣ Classical ML project
2️⃣ Deep learning project
3️⃣ Production RAG application
4️⃣ Agentic AI application
At least 2 should be genuinely impressive.
---
EVERY WEEK
Don't spend 100% of your time watching courses.
Use:
30% → Learning
50% → Building
10% → Reading papers/docs
10% → Interview preparation
And code EVERY DAY.
---
THE INTERVIEW STACK 🎯
Alongside AI, keep your SWE fundamentals alive:
• DSA
• OOP
• DBMS
• OS
• Computer Networks
• SQL
• System design basics
• Python
AI Engineer ≠ “person who knows ChatGPT.”
You still need to be an engineer.
---
WHAT NOT TO DO ❌
Don't spend 6 months collecting:
• 20 certificates
• 50 prompt-engineering courses
• 15 frameworks
• 100 tutorials
• Copy-pasted GitHub projects
Instead:
Learn → Build → Break → Debug → Deploy → Explain
Repeat.
---
YOUR 6-MONTH END GOAL 🎯
You should be able to:
→ Train an ML model
→ Build a neural network
→ Understand Transformers
→ Work with LLM APIs
→ Build RAG systems
→ Build AI agents
→ Evaluate AI systems
→ Deploy AI applications
→ Monitor production systems
→ Explain your architecture in an interview
That's the difference between:
“I know AI”
and
“I can build AI systems.”
6 months is enough to become job-ready, not an AI research scientist.
The roadmap is simple.
The execution isn't. 🔥
என்ன கருமம் டா இது...
எப்பேர்ப்பட்ட படம் இது... அய்யா கே.எஸ்.ரவிகுமார் அய்யா உங்களுக்கெல்லாம் இதயமே இல்லையா ..!? ரீமேக் பண்ணதும் பண்ணீங்க ஒரு யங் ஹீரோ ஹீரோயின் கிடைக்கலையா ??
ஹீரோ 70 வயது தாத்தா ... ஹீரோயின் 50 வயது பாட்டி மாதிரி இருக்காங்க...
படம் பெயர் நீங்களே கண்டுபிடிங்க பார்ப்போம் 😭🤡
1. If you speak too much - You will lie.
2.If you think too much - You will be depressed.
3. If you care too much - You will taken for granted.
4. If you trust too much - You will be betrayed.
5. If you work too much - You will lose your life.
Don't be too much.
Because that too much can hurt
You so much.
Day 08/180 of Learning Python
What I Learned Today
~>Python string methods
~>find() & count()
~> Validation methods (isalpha(),
isdigit(), etc.)
~>Built a simple Text Analyzer
Learning by building every day. 🌱
#Python#100DaysOfCode#Coding#LearningInPublic