Many of these are better understood as prompt shortcuts, not official Claude commands.

The idea is still useful because most people underuse AI by repeatedly writing the same long instructions for tasks they perform over and over again. Instead, you can build your own command library around your workflow.

For example:

1. /focus

Complete only the requested task. Avoid unrelated suggestions, unnecessary explanations, and scope expansion.

1. /brief

Give me the answer in no more than three concise lines.

1. /matrix

Compare the available options in a structured table using clear decision criteria.

1. /devil

Challenge my current assumption. Present the strongest opposing argument and explain what evidence would support it.

1. /roast

Critically review the work. Identify weak reasoning, unsupported claims, missing evidence, and unnecessary complexity.

1. /silent

Deliver the finished output directly without explaining every intermediate step.

1. /humanize

Rewrite the content so it sounds natural, specific, conversational, and less formulaic.

1. /reverse

Deconstruct a successful result. Identify the decisions, assumptions, structure, and techniques that likely made it work.

1. /structured

Analyze the problem systematically. Consider the relevant factors before presenting the final recommendation.

1. /prototype

Turn the idea into a visual, interactive, or reusable artifact when the AI's capabilities support it.

Claude's Artifacts feature can create substantial standalone outputs, including documents, code, diagrams, websites, and interactive components.

But there is an important distinction. The commands shown in graphics like this should not automatically be treated as official built in Claude slash commands. The more reliable approach is to define the behavior yourself.

For example, instead of simply writing:

/devil

You could define it as:

"Challenge my proposal as a skeptical security architect. Identify the three strongest objections, explain the evidence behind each one, and tell me what evidence would change your conclusion."

That is far more reliable than expecting a single keyword to magically produce the same quality every time. The keyword becomes a shortcut for a much more detailed instruction. And that leads to the bigger lesson: AI productivity does not come from discovering magic commands. It comes from turning repeatable thinking patterns into repeatable instructions. If you regularly perform the same types of work, build a small command library around them.

Research.

Criticism.

Comparison.

Summarization.

Brainstorming.

Security review.

Decision making.

Content rewriting.

Data analysis.

You don't need 50 clever commands.

You need a small set of commands that capture how you want AI to approach the work you perform repeatedly.

That's when AI starts becoming less like a chatbot you have to repeatedly instruct and more like a system built around the way you actually work.

The biggest productivity gains from Claude do not come from getting better at chatting with AI. They come from turning repeated tasks into structured, reusable workflows.

10 Key Takeaways

1. Better prompts produce better results.
2. Reusable prompts save time on recurring tasks.
3. Projects help keep related work and context organized.
4. Artifacts allow you to create and improve actual work instead of just generating text.
5. AI can speed up research and simplify complex information.
6. Claude can be used as a thinking partner to challenge assumptions and improve decisions.
7. Connecting your business data makes AI more useful and context aware.
8. Repetitive tasks can be turned into automated workflows.
9. AI can support multiple areas of a business, including content, analysis, customer support, and operations.
10. The biggest productivity leap comes from building AI systems that make work repeatable, scalable, and less dependent on manual effort.

Spend 2 hours setting Claude up properly, and you could save yourself weeks of repetitive work.

The mistake most people make is treating Claude like a chatbot. They open it, type a prompt, get an answer, close it, and repeat the same process tomorrow. A better approach is to build an AI workspace that already understands your work, your preferences, your projects, and your workflows.

Here is a simple setup:

1. Install the Claude Desktop App

Use the desktop app when you want access to features such as Claude Code and local workflows.

1. Choose the Right Plan

If you use Claude heavily, a paid plan can give you higher usage limits and access to more capable models.

Use more capable models for complex work and faster models for simpler tasks.

1. Create a Workspace

Organize your files into simple folders such as:

• About Me
• Projects
• Templates
• Outputs
• References

A clean structure makes it much easier to find information and reuse previous work.

1. Give Claude Context

Don't expect Claude to understand your business from a single prompt.

Give it documents that explain:

• Who you are
• What your company does
• Your audience
• Your writing style
• Brand guidelines
• Your goals
• Your preferred workflows

Better context usually produces better results.

1. Build Your Personal Profile

Ask Claude to interview you.

Let it ask about your work, responsibilities, audience, communication style, preferences, and goals.

Then save the useful information as reference material that Claude can use in future projects.

1. Configure Project Instructions

Give each project clear instructions about:

• Tone
• Formatting
• Audience
• Rules
• Required information
• Words and phrases to avoid

The advantage is simple: you define the rules once instead of repeating them in every conversation.

1. Match the Model to the Task

Don't use the most powerful model for everything.

Use stronger reasoning models for:

• Coding
• Research
• Strategy
• Complex analysis
• Difficult writing

Use faster models for:

• Emails
• Summaries
• Brainstorming
• Simple rewriting
• Everyday tasks

1. Organize Your Knowledge

Keep your notes, prompts, templates, research, and documentation organized in one place.

You can use Obsidian, Notion, or another knowledge management tool.

The specific app matters less than having a consistent system.

1. Start Every Project With Context

Instead of saying:

"Write a blog post."

Give Claude the information it needs first:

"Review my project files, understand my audience and goals, identify the important points, and then help me write the blog post."

That small change can dramatically improve the quality of the output.

1. Save Your Best Workflows

Every time Claude helps you solve a recurring problem, don't let that workflow disappear inside an old conversation.

Save the prompt, process, or template. Over time, you'll build your own AI playbook. Then you stop starting from zero every time you open Claude. The biggest productivity gain doesn't come from learning hundreds of prompts. It comes from giving AI better context and building systems around the work you repeatedly do. The more Claude understands about your work, the less you have to explain. And that's where the real productivity gains start.

Have you built a reusable AI workspace yet?

I’ve been running the same business through different AI search prompts, and I’m starting to think the idea of a single “AI ranking” is misleading.

Take a local company and test:

• “best company for X in Dallas”
• “reliable X company in Dallas”
• “who would you recommend for X in Dallas?”
• “X company for an emergency”
• “affordable X service near Dallas”

Same business. Same general service.

But ChatGPT, Gemini, and Perplexity can produce surprisingly different recommendations depending on the intent and wording.

What seems more useful to me is measuring appearance rate across a group of prompts.

That’s what I’ve been testing with Signal AEO: prompt-level visibility, platform differences, recommendation consistency, and which sources appear alongside the recommendation.

Instead of asking:

“What position do we rank?”

I’m starting to ask:

“Across 50 realistic ways a customer could ask for this service, how often are we actually part of the answer?”

That feels much closer to what AI visibility really means.

Curious if anyone else is measuring it this way. Have you found certain types of prompts to be significantly more stable than others?

You don't need to become an AI engineer.

But you do need to understand the AI skills that can save time, reduce costs, improve decisions, and help your business operate more efficiently.

Here are 10 worth learning.

1. Prompt Engineering

What it means: Giving AI the right instructions so you get useful, actionable results instead of generic answers.

Why it matters: The quality of your instructions often determines the quality of the output. Learn to use AI like a strategist, not just a chatbot.

Tools: ChatGPT, Claude, Gemini, Perplexity

2. AI Agents

What it means: AI that doesn't just answer questions. It can plan and complete tasks from start to finish.

Why it matters: Agents can handle work you might normally assign to a junior employee, such as lead research, data gathering, scheduling, and routine follow ups.

Tools: OpenAI Agents, CrewAI, LangChain

3. Workflow Automation

What it means: Connecting your software so repetitive work happens automatically.

Why it matters: Reporting, onboarding, data entry, notifications, and other repeatable processes can often run without manual intervention.

Rule: If your team does the same task repeatedly, ask whether it should be automated.

Tools: Make, Zapier, n8n, Bardeen

4. Agentic AI

What it means: AI that can plan, adapt, evaluate its progress, and adjust its approach instead of simply following a fixed sequence.

Why it matters: This becomes valuable when a task has multiple steps and the path to the answer isn't always predictable.

Best uses: Research, operations, quality assurance, analysis, and complex problem solving.

Tools: OpenAI o1, Claude, Reflexion, DSPy

5. Multimodal AI

What it means: AI that can work with multiple types of information, including text, images, audio, and code.

Why it matters: You can move from one idea to an entire campaign without switching between as many tools.

For example, you could develop the copy, create visuals, generate video concepts, and produce voiceover from the same workflow.

Tools: Gemini, Claude, OpenAI Vision, Stable Audio

6. RAG (Retrieval Augmented Generation)

What it means: Connecting AI to your own information so it can retrieve relevant data before generating an answer.

Why it matters: Instead of relying only on what the model already knows, AI can work from your company's documents, knowledge base, product information, or internal data.

Best uses: Customer support, sales enablement, internal knowledge, and document based research.

Tools: Pinecone, LlamaIndex, Haystack, Elastic

7. AEO / GEO

Answer Engine Optimization / Generative Engine Optimization

What it means: Optimizing your brand and content so AI systems can understand, mention, and potentially recommend your business when users ask questions.

Why it matters: Search is changing. People are increasingly asking AI systems questions instead of relying only on traditional search engines.

If your customers are asking ChatGPT, Gemini, Claude, or Perplexity about products and services in your industry, your brand needs to be visible there too.

8. AI Tool Stacking

What it means: Combining multiple AI tools and connecting them into one workflow.

Why it matters: Instead of using AI tools separately, you can build systems where information moves automatically from one tool to another.

The goal is simple: reduce manual work, lower operating costs, and give your team more time for higher value work.

Tools: Notion AI, ClickUp AI, Airtable AI, Zapier AI

9. AI Content Generation

What it means: Using AI to produce and repurpose content at scale without building a huge content team.

Why it matters: One piece of content can become multiple assets across different channels.

For example:

One article → social posts → short videos → podcast content → email → Q&A answers.

Tools: Descript, SayWhat, OpusClip, ElevenLabs

10. LLM Management

What it means: Managing the cost, quality, accuracy, reliability, and performance of the AI models your business uses.

Why it matters: Once AI becomes part of your core operations, you need to know what you're spending, how well the systems perform, and whether they're actually producing a return.

Tools: Arize AI, TruLens, Helicone, Weights & Biases

You Don't Need To Master All 10

Don't try to learn everything at once. Pick one skill this week. Apply it to a real business problem. Measure the result. Keep what works. Discard what doesn't.

The businesses that benefit most from AI won't necessarily be the ones using the most tools.

They'll be the ones that know which problems to automate, which processes to improve, and where AI actually creates measurable value.

Seven human capabilities become more valuable as automation spreads.
Treat them as operating disciplines, not personality traits.

1. Emotional Intelligence
   → Before a difficult decision, name who is affected and what they may fear.
   → In one-to-ones, ask what is unsaid before offering a solution.
   AI can summarize sentiment, but it cannot earn trust through consistent care.

2. Creative Problem-Solving
   → Run a constraint exercise: solve the problem with half the budget or time.
   → Ask the team for three options before using an LLM to expand them.
   Models remix patterns; leaders must frame the problem worth solving.

3. Ethical Judgement
   → Keep a decision log for choices involving people, privacy, safety, or access.
   → Red-team the outcome by asking who benefits, who carries risk, and who can appeal.
   A technically valid answer can still be commercially or morally wrong.

4. Relationship Building
   → Spend one conversation each week learning a stakeholder's real priorities.
   → Measure follow-through, not contact volume, after every important commitment.
   An LLM can draft a message; only reliable action builds durable relationships.

5. Strategic Vision
   → Define a 12-month direction before selecting tools or automating workflows.
   → Review quarterly signals that could invalidate the plan, not just confirm it.
   AI can optimize a route, but leadership still chooses the destination.

6. Cultural Intelligence
   → Test language, assumptions, and incentives with people from each affected market.
   → Invite local challenge before launching a global policy or customer experience.
   A workflow that succeeds in one culture can damage trust in another.

7. Adaptive Learning
   → Run a 30-day learning loop: test, measure, reflect, then change one behaviour.
   → Keep a failure log that records the assumption, evidence, and next experiment.
   Teams that learn openly adapt faster than teams that defend yesterday's plan.

Make each skill observable inside real work.
Set one monthly practice, one accountable owner, and one feedback signal.
Ask managers to coach the behaviour, then review evidence in operating meetings.
If a skill cannot be seen, it will not improve through intention alone.

AI will raise the baseline for analysis, content, and execution.
That makes human judgement, trust, imagination, and learning more visible.

Which of these seven skills does your team most need to strengthen now?

I've worked as a software engineer for about 5 years, so I've learned a lot about building software products. Marketing, sales, distribution, and customer acquisition are areas where I still have a lot to learn.

I built an AI product that's actually been useful for me: https://passiveagents.com/

The other day, before going to a friend's birthday, I added around 10 tasks to a project board and let it run. When I came back, it had made progress on those tasks, which was pretty nice.

I've heard Alex Hormozi talk about dominating a puddle before trying to dominate a pond, a lake, or an ocean. Instead of trying to market to everyone, I'm thinking about focusing on small business owners in Columbus, Ohio and personally helping them get set up.

My hypothesis is that the big AI companies are focusing on small business owners and they also need help getting set up and learn about how to use these AI tools in the most effective ways. I could target a need where small business owners would like to get more done than they have time for and need help getting started. Things like competitor research, content creation, marketing planning, documenting processes, and other projects that keep getting pushed back.

I'm considering cold emailing local businesses and offering to set everything up for them so they can see whether having AI agents work on tasks in the background is actually useful.

If you were trying to get the first paying customers for this product, how would you approach it?

Would you focus on cold email, local networking, a specific niche, free onboarding, something else, or am I thinking about this entirely the wrong way?

For anyone running SEO or marketing campaigns:

Are your clients starting to ask whether their business appears in ChatGPT, Gemini, or Perplexity?

I'm seeing more interest in this because being #1 on Google doesn't automatically mean you're the business an AI assistant recommends.

We've been exploring this from the AEO side with Signal AEO, particularly around tracking business visibility across answer engines.

Feels like this could eventually become a normal part of reporting alongside traditional rankings.

Anyone already including AI visibility in client reports?

There are a million tools for traditional SEO, but the AEO/AI visibility side still feels pretty early.
I'm specifically interested in tracking questions where someone asks AI for a business recommendation rather than just checking whether a page gets cited.
I've been working with Signal AEO, so that's naturally one of the platforms I'm familiar with.
What other tools or manual processes are people using to measure this reliably?

1. Getting rich and staying rich require different skills.
   Making money often takes risk, optimism, and hard work. Keeping money requires patience, discipline, and avoiding unnecessary risks.

2. Luck and risk influence every financial outcome.
   Success isn't always the result of intelligence, and failure isn't always caused by poor decisions. Never judge yourself or others based only on outcomes.

3. Saving money matters more than earning a high income.
   You don't need a massive salary to build wealth. Consistently saving a portion of your income creates financial security over time.

4. Compounding is the greatest wealth building tool.
   Small, consistent gains over long periods produce extraordinary results. Time is often more valuable than chasing higher returns.

5. Wealth is what you don't see.
   Luxury cars, expensive watches, and designer clothes aren't proof of wealth. Real wealth is the money you've saved and invested but haven't spent.

6. Know when you have enough.
   Many successful people lose everything because they keep chasing more. Defining what "enough" means protects you from greed and unnecessary risk.

7. The highest form of wealth is control over your time.
   Money should buy freedom, not just possessions. Being able to decide how you spend your day is one of the greatest financial rewards.

8. Build a margin of safety into your financial life.
   Unexpected events happen to everyone. Emergency savings and avoiding excessive debt allow you to survive setbacks without starting over.

9. Long term consistency beats short term brilliance.
   You don't need to make perfect financial decisions. You simply need to make good decisions consistently and avoid catastrophic mistakes.

10. Financial success depends more on behavior than intelligence.
    The smartest investor doesn't always win. People who stay calm, remain patient, control their emotions, and stick to a long term plan usually achieve better results.

My favorite quote from the book:

"The hardest financial skill is getting the goalpost to stop moving."

The moment you define what "enough" means, money stops controlling you, and you start controlling your money.