🧠 Claude has 12 skills you're not using

Welcome back! Twelve Skills you can install today, and three pieces on what's actually running underneath every answer you get. 

In today's Generative AI Academy Newsletter:

Upload once. Claude fires them on its own.

Most people still paste the same long prompt every morning. 😅

A Skill is a small instruction file. Claude reads it and triggers it when your request matches.

𝐓𝐡𝐢𝐧𝐤 𝐛𝐞𝐟𝐨𝐫𝐞 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠

→ Grill me: 10 to 15 questions before it builds anything
→ Be a damn human: refuses a vague brief, asks first
→ Prompt maker: brain-dump in, clean spec out
→ How to: turns "I want to do X" into steps

𝐓𝐫𝐮𝐬𝐭 𝐭𝐡𝐞 𝐨𝐮𝐭𝐩𝐮𝐭

→ Fact checker: every claim against primary sources
→ Deep research analyzer: messy sources into a cited summary
→ Skill audit: finds overlaps in what you installed

𝐒𝐨𝐮𝐧𝐝 𝐥𝐢𝐤𝐞 𝐲𝐨𝐮

→ Humanizer: no em dashes, no robot tone
→ Personal voice: learns your samples, checks every draft against them

𝐒𝐡𝐢𝐩 𝐜𝐨𝐧𝐭𝐞𝐧𝐭

→ LinkedIn hook: 10 options for the two lines that decide everything
→ Viral recipe: reverse-engineers why a post worked
→ Infographic builder: text in, 1080x1350 image out

Half of these make Claude 𝐬𝐥𝐨𝐰𝐞𝐫. Ask first. Check claims. Audit itself.

That's the shift. Prompts are one-off requests. Skills are standards you set once.

Start with three: Grill me, Fact checker, Personal voice.

Seven Claude courses at GenAI Academy. All recorded, and all free.

Start with "What Can AI Actually Do For You?" if you're new, 30 minutes, no setup required. 

Then the Claude Starter Course gets you running in 35.

Past that, pick by the problem you have. Hitting usage limits in Claude.ai, Cowork or Claude Code has its own course. 

Building no-code agents has one. 

So does rolling Claude out across an ops team in 30 days, from pilot to adoption data to full deployment.

The AI Portfolio Builder turns whatever you built into a case study you can show someone.

World Wide Vibes Hackathon is live too, $5,000 prize pool, beginner-ready, 100% online.

One login. Watch them in any order.

Browse the free Claude courses.

You type: "Explain Kubernetes to me."

A few seconds later, an answer appears. Behind that one prompt, an entire distributed system comes alive.

𝗕𝗲𝗳𝗼𝗿𝗲 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹 𝘀𝗲𝗲𝘀 𝗮 𝘁𝗼𝗸𝗲𝗻

DNS resolves where it goes. A load balancer picks a destination. An API gateway authenticates. A router decides which model handles it. Your words become tokens. A scheduler may batch you with strangers.

𝗧𝗵𝗲 𝗲𝘅𝗽𝗲𝗻𝘀𝗶𝘃𝗲 𝗽𝗮𝗿𝘁

The GPU processes your input during prefill and builds the KV cache. Then decode: predict a token, update state, predict another, repeat.

𝗥𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝗹𝗼𝗻𝗴𝘀𝗶𝗱𝗲 𝗶𝘁

→ caching
→ safety checks
→ rate limits
→ observability
→ failures and retries
→ cost tracking
→ streaming

All of that before RAG, tools, multimodal inputs or reasoning loops enter the picture.

𝗪𝗵𝗮𝘁 𝗶𝘁 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗹𝗼𝗼𝗸𝘀 𝗹𝗶𝗸𝗲

Prompt → Answer

is really:

Network → Routing → Auth → Tokenization → Scheduling → GPU → Prefill → KV Cache → Decode → Safety → Streaming → Observability

The model gets the headlines. The infrastructure decides whether it feels instant or broken.

Next time a response starts appearing right away, think about what's sitting behind that blinking cursor.

The technical foundation, from tokens to adaptation.

1️⃣ 𝗧𝗢𝗞𝗘𝗡𝗜𝗭𝗔𝗧𝗜𝗢𝗡
Text → tokens → token IDs. BPE, WordPiece and SentencePiece decide how text gets split, which drives context length and cost.

2️⃣ 𝗘𝗠𝗕𝗘𝗗𝗗𝗜𝗡𝗚𝗦
Text → dense vectors. Captures meaning, powers semantic search, RAG and clustering.

3️⃣ 𝗧𝗥𝗔𝗡𝗦𝗙𝗢𝗥𝗠𝗘𝗥𝗦
The architecture underneath all of it. Self-attention, multi-head attention, feed-forward layers and positional information.

4️⃣ 𝗔𝗧𝗧𝗘𝗡𝗧𝗜𝗢𝗡
Query, key and value produce a context-aware representation. Know self, causal and cross-attention, plus why the complexity matters.

5️⃣ 𝗣𝗥𝗘-𝗧𝗥𝗔𝗜𝗡𝗜𝗡𝗚
Huge corpus, next-token prediction, resulting weights. This is where general language ability comes from.

6️⃣ 𝗙𝗜𝗡𝗘-𝗧𝗨𝗡𝗜𝗡𝗚
Pre-trained model plus task data equals a specialist. Know full fine-tuning, instruction tuning, PEFT and when none of them beat a good prompt.

7️⃣ 𝗥𝗟𝗛𝗙 / 𝗗𝗣𝗢
Human preferences into alignment. RLHF trains a reward model then runs RL. DPO learns straight from preferred and rejected pairs.

8️⃣ 𝗥𝗔𝗚
Documents → chunking → embeddings → retrieval → context → model. Most failures here are chunking and reranking rather than the vector database.

9️⃣ 𝗟𝗼𝗥𝗔 / 𝗣𝗘𝗙𝗧
Freeze the base weights, train small adapters. Same adaptation, a fraction of the trainable parameters.

𝗛𝗼𝘄 𝘁𝗵𝗲𝘆 𝗰𝗼𝗻𝗻𝗲𝗰𝘁
Tokenization → Embeddings → Transformer → Attention → Pre-training → Fine-tuning → RAG → PEFT

Your CV gets ten seconds.

That's how long a hiring manager spends matching you against a job description. They scan for their language, not yours. Miss it and you're skipped, however qualified you are.

So the rejection usually wasn't about your experience.

The fix is rewriting the CV per application, which nobody does across ten roles. ChatGPT closes that gap. Paste in your CV and the job description, and twenty minutes gets you a tailored version, a LinkedIn summary and a clear read on where you're strong.

Four ways to push it further

Run it on a role you already got rejected from, using the CV you actually sent. The difference usually explains the outcome.

If three job descriptions name the same missing skill, that's a pattern worth acting on.

Changing industries? Use it to translate your experience into the new field's vocabulary.

Feed the tailored CV and job description back in for a cover letter draft. It won't be finished, and it beats a blank page.

One caution. Reframing in their language is the point. Inventing experience is a different thing, and it surfaces in the interview.

An LLM works one token at a time. Then another. Then another.

A lot happens before each one reaches your screen.

The pipeline:

Your prompt gets split into tokens. Each becomes a numerical embedding. Transformer layers process context through attention and feed-forward networks. The model produces a probability distribution across its entire vocabulary. A decoding strategy picks the next token. That token joins the context and the whole cycle runs again. Tokens convert back to text and stream to you.

Two phases, very different behavior:

Prefill handles input tokens largely in parallel.

Decode generates sequentially, which makes memory movement, KV cache size and bandwidth the things that matter.

Which explains:

→ Longer prompts increase prefill work
→ Longer conversations grow the KV cache
→ Quantization cuts memory requirements
→ FlashAttention reduces expensive memory reads
→ Speculative decoding speeds up generation
→ Temperature, Top-K and Top-P change how the next token gets picked

It looks like writing. Underneath it's arithmetic, a token selection, a context update, and the same thing again.

Full flow in the diagram. Save it if you're learning how inference works.

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