everyone's arguing about whether ChatGPT or Claude is "smarter."
nobody noticed Anthropic just dropped something that makes the model debate irrelevant.
it's called Skills. and it's the first AI feature that actually solves the problem everyone complains about:
"why do I have to explain the same thing to AI every single time?"
here's what's different:
- you know how you've explained your brand guidelines to ChatGPT 47 times?
- or how you keep telling it "structure reports like this" over and over?
- or how every new chat means re-uploading context and re-explaining your process?
Skills ends that cycle.
you teach Claude your workflow once.
it applies it automatically. everywhere. forever.
but the real story isn't memory. it's how this changes what's possible with AI at work.
here's the technical unlock that makes this actually work:
Skills use "progressive disclosure" instead of dumping everything into context.
normal AI workflow:
→ shove everything into the prompt
→ hope the model finds what it needs
→ burn tokens
→ get inconsistent results
Skills workflow:
→ Claude sees skill names (30-50 tokens each)
→ you ask for something specific
→ it loads ONLY relevant skills
→ coordinates multiple skills automatically
→ executes
example: you ask for a quarterly investor deck
Claude detects it needs:
- brand guidelines skill
- financial reporting skill
- presentation formatting skill
loads all three. coordinates them. outputs a deck that's on-brand, accurate, and properly formatted.
you didn't specify which skills to use.
you didn't explain how they work together.
Claude figured it out.
this is why it scales where prompting doesn't.
let me show you what this looks like in real workflows.
• color codes (#FF6B35 coral, #004E89 navy)
• font rules (Montserrat headers, Open Sans body)
• logo placement rules (0.5" minimum spacing)
• template files
prompt: "create 10-slide deck for Q4 product launch"
- Claude auto-applies brand skill
- output matches guidelines first try
- 30 seconds instead of 4 hours
Rakuten (Japanese e-commerce giant) is already doing this.
finance workflows that took a full day? now 1 hour.
the repetitive nightmare:
- new lead comes in
- manually create CRM contact
- fill in 12 fields following "the naming convention"
- update opportunity stage
- log activity notes in specific format
- set follow-up reminder
- 8 minutes per lead × 30 leads/week = 4 hours gone
Skills implementation:
create "CRM_Automation" skill that knows:
- your naming conventions (FirstName_LastName_Company format)
- required fields and validation rules
- opportunity stages and when to use them
- note formatting structure
- follow-up timing rules
now: paste lead info → Claude structures everything correctly → done
time per lead: 30 seconds
weekly savings: 3.75 hours
monthly savings: 15 hours (almost 2 full workdays)
at $50/hour, that's $750/month saved per sales rep.
team of 10 reps? $90k/year in recovered time.
Step 3: Upload to Claude
Settings → Capabilities → enable "Code execution"
upload your .zip under Skills
test with: "create a presentation following brand guidelines"
pro tip: use the "skill-creator" skill just say "help me create a brand guidelines skill" and Claude interviews you, generates the folder structure, and formats everything automatically.
the companies dominating with AI aren't using better prompts.
they're building systems that codify how they work.
Traditional MBA programs can't keep up. They teach case studies from 2015 while you're building in 2025.
This prompt fixes that.
Copy this entire prompt into ChatGPT, Claude, or Gemini:
```
You are now an elite MBA professor with 20+ years of experience teaching at Stanford GSB and Harvard Business School. You've advised Fortune 500 CEOs and built three successful startups yourself.
Your teaching style combines:
- Socratic questioning that forces deeper thinking
- Real-world case analysis from current companies
- Practical frameworks over academic theory
- Contrarian perspectives that challenge assumptions
When I ask you business questions, you will:
1. Clarify the real problem - Ask 2-3 probing questions before giving answers. Most people ask the wrong questions.
2. Provide strategic framework - Give me 3-5 different mental models or frameworks I can apply (Porter's Five Forces, Jobs-to-be-Done, Blue Ocean Strategy, etc.)
3. Use current examples - Reference companies and strategies from the last 12 months, not decades-old case studies.
4. Challenge my assumptions - Point out blind spots in my thinking and offer alternative perspectives.
5. Give actionable steps - End every response with 3 concrete actions I can take this week.
6. Teach through questions - When appropriate, don't just give answers. Ask questions that help me arrive at insights myself.
Your expertise covers:
- Business strategy and competitive positioning
- Growth tactics and customer acquisition
- Pricing psychology and revenue models
- Product-market fit and go-to-market strategy
- Financial modeling and unit economics
- Organizational design and leadership
- Market analysis and competitive intelligence
Always be direct. No corporate speak. No obvious advice. Challenge me like you're a $2,000/hour advisor who doesn't have patience for surface-level thinking.
Stanford just built a system where an AI learns how to think about thinking.
It invents abstractions like internal cheat codes for logic problems and reuses them later.
They call it RLAD.
Here's the full breakdown:
The idea is brutally simple:
Instead of making LLMs extend their chain-of-thought endlessly,
make them summarize what worked and what didn’t across attempts
then reason using those summaries.
They call those summaries reasoning abstractions.
Think: “lemmas, heuristics, and warnings” written in plain language by the model itself.
Example (from their math tasks):
After multiple failed attempts, the model abstracts:
“Check the existence of a multiplicative inverse before using x⁻¹ in a congruence.”
Then in the next try, it uses that abstraction and solves the problem cleanly.
That’s not prompt engineering. That’s meta-reasoning.
But here’s the truth: prompt engineering is not the future - problem framing is.
You can’t “hack” your way into great outputs if you don’t understand the input problem.
The smartest AI teams don’t ask “what’s the best prompt?” - they ask “what exactly are we solving?”
Before typing anything into ChatGPT, do this:
1️⃣ Define the goal - what outcome do you actually want?
2️⃣ Map constraints - time, data, resources, accuracy.
3️⃣ Identify levers - what can you change, what can’t you?
4️⃣ Translate context into structure - who’s involved, what matters most, what failure looks like.
5️⃣ Then prompt - not for an answer, but for exploration.
AI isn’t a genie. It’s a mirror for your thinking.
If your question is shallow, your output will be too.
The best “prompt engineers” aren’t writers - they’re problem architects.
They understand psychology, systems, and tradeoffs.
Their secret isn’t phrasing - it’s clarity.
Prompting is the last step, not the first.
⚙️ Meta-prompt for problem formulation:
#Role: World-class strategic consultant combining McKinsey-level analysis, systems thinking, and first-principles reasoning
#Method: Interview user with precision questions, then apply elite expert reasoning
#Interview_Process
(Ask user ONE question at a time)
1. Context: What's the situation? Why does it matter now? 2. Objective: What specific, measurable outcome do you need? 3. Constraints: What's fixed? (budget/time/resources/tradeoffs/non-negotiables) 4. Success Metrics: How will you know you succeeded? What numbers matter? 5. Stakeholders: Who's affected? What do they each want/need? 6. Root Cause: What's actually causing this problem? (not symptoms)
Analysis Framework (after gathering info)
Step 1: Problem Decomposition
First principles: Break down to fundamental truths
Separate symptoms from root causes
Map dependencies and feedback loops
Step 2: Systems Thinking
Identify: causes → key variables → second-order effects → outcomes
Spot constraints that unlock vs. constraints that block
Find leverage points (20% effort → 80% impact)
Step 3: Strategic Reasoning
What's the highest-value intervention?
What are critical risks and failure modes?
What assumptions must be true for success?
Step 4: Expert Synthesis
Output:
Core Problem: [one sentence]
Critical Insight: [what others miss]
Top 3 Actions: [prioritized by impact/feasibility]
Key Risks: [what could go wrong]
Success Looks Like: [specific, measurable]