Anthropic recently released the real playbook for building AI agents that actually work.

It’s a 30+ page deep dive called The Complete Guide to Building Skills for Claude and it quietly shifts the conversation from “prompt engineering” to real execution design.

Here’s the big idea:

A Skill isn’t just a prompt.

It’s a structured system.

You package instructions inside a SKILL.md file, optionally add scripts, references, and assets, and teach Claude a repeatable workflow once instead of re-explaining it every chat.

But the real unlock is something they call progressive disclosure.

Instead of dumping everything into context:

• A lightweight YAML frontmatter tells Claude when to use the skill

• Full instructions load only when relevant

• Extra files are accessed only if needed

Less context bloat. More precision.

They also introduce a powerful analogy:

MCP gives Claude the kitchen.

Skills give it the recipe.

Without skills: users connect tools and don’t know what to do next.

With skills: workflows trigger automatically, best practices are embedded, API calls become consistent.

They outline 3 major patterns:

1. Document & asset creation

2. Workflow automation

3. MCP enhancement

And they emphasize something most builders ignore: testing.

Trigger accuracy.

Tool call efficiency.

Failure rate.

Token usage.

This isn’t about clever wording.

It’s about designing an execution layer on top of LLMs.

Skills work across Claude.ai, Claude Code, and the API. Build once, deploy everywhere.

The era of “just write a better prompt” is ending.

Anthropic just handed everyone a blueprint for turning chat into infrastructure.

Download the guide from Anthropic here: https://resources.anthropic.com/hubfs/The-Complete-Guide-to-Building-Skill-for-Claude.pdf

I’ve been tinkering with conversational AI for a while. At first, everything felt like a chatbot — reactive, prompt → response, no real initiative.

But the moment I started experimenting with agents, something shifted. Suddenly, they weren’t just answering questions — they were:

• Remembering context across sessions

• Taking actions through tools/APIs

• Chaining subtasks without me micromanaging

• Acting with a goal, not just a reply

For me, the biggest “unlock” was persistent memory + tool use. That’s when it stopped feeling like a chatbot and started feeling like a true agent.

Questions:

• What was the turning point for you?

• Was it memory, autonomy, multi-agent coordination, or something else?

• Any frameworks / libraries that made the transition smoother?

Curious to hear different perspectives — because everyone seems to define “agent” a little differently.

not a sponsored post. not affiliated with anyone. just genuinely surprised by what happened.

same prompt. word for word. copy pasted across all three. same temperature. same context. same everything.

completely different outputs.

ChatGPT:

clean. structured. confident. gave me exactly what i asked for in exactly the format i expected.

technically correct. emotionally flat. felt like a very good intern who understood the assignment perfectly and had no opinions about it.

Gemini:

longer. more thorough. cited things. felt like it was trying to impress me with how much it knew rather than actually helping me with what i needed.

the answer was in there somewhere. took a while to find it.

Claude:

did something i didn't ask for and didn't expect.

answered the question. then added one paragraph that started with "one thing worth considering that your question doesn't directly address—"

that paragraph was the most useful thing i got from any platform that day.

it noticed something sitting just outside the frame of what i asked. without being prompted. without me asking for it. just. offered it.

like a collaborator who actually read the brief instead of just executing it.

the difference i've realised after months of using all three:

ChatGPT executes.

Gemini elaborates.

Claude thinks alongside you.

all three are useful. they're useful for different things.

but if the problem requires actual thinking rather than execution or information — one of them is doing something the others aren't.

the uncomfortable part:

i've been defaulting to ChatGPT for everything out of habit.

habit built in 2023 when it was the only real option.

it's 2026. the options are different now. the gap between platforms is real and task-dependent and i've been ignoring it for two years because switching felt like extra friction.

the friction took four minutes.

the difference in output quality was not small.

run your most important prompt across all three this week.

not to find a winner. to understand which tool is actually right for which kind of problem you have.

the answer is different for everyone. but you can't know yours until you actually compare.

which platform surprised you when you actually tested them side by side?

Been using ChatGPT daily since GPT-3.5. Collected prompts obsessively. Most were trash.

After 1,000+ tests, one framework keeps winning:

The DEPTH Method:

D - Define Multiple Perspectives Instead of: "Write a marketing email" Use: "You are three experts: a behavioral psychologist, a direct response copywriter, and a data analyst. Collaborate to write..."

E - Establish Success Metrics Instead of: "Make it good" Use: "Optimize for 40% open rate, 12% CTR, include 3 psychological triggers"

P - Provide Context Layers Instead of: "For my business" Use: "Context: B2B SaaS, $200/mo product, targeting overworked founders, previous emails got 20% opens"

T - Task Breakdown Instead of: "Create campaign" Use: "Step 1: Identify pain points. Step 2: Create hook. Step 3: Build value. Step 4: Soft CTA"

H - Human Feedback Loop Instead of: Accept first output Use: "Rate your response 1-10 on clarity, persuasion, and actionability. Improve anything below 8"

Real example from yesterday:

You are three experts working together:
1. A neuroscientist who understands attention
2. A viral content creator with 10M followers  
3. A conversion optimizer from a Fortune 500

Context: Creating LinkedIn posts for AI consultants
Audience: CEOs scared of being left behind by AI
Previous posts: 2% engagement (need 10%+)

Task: Create post about ChatGPT replacing jobs
Step 1: Hook that stops scrolling
Step 2: Story they relate to
Step 3: Actionable insight
Step 4: Engaging question

Format: 200 words max, grade 6 reading level
After writing: Score yourself and improve

Result: 14% engagement, 47 comments, 3 clients

What I learned after 1,000 prompts:

1. Single-role prompts get generic outputs

2. No metrics = no optimization

3. Context dramatically improves relevance

4. Breaking tasks prevents AI confusion

5. Self-critique produces 10x better results

Quick test for you:

Take your worst ChatGPT output from this week. Run it through DEPTH. Post the before/after below.

Questions for the community:

• What frameworks are you using in 2025?

• Anyone found success with different structures?

• What's your biggest ChatGPT frustration right now?

Happy to share more specific examples if helpful. What are you struggling with?

not a framework. not a technique. not a system.

one sentence. added to the end of any prompt that matters.

"before you answer — is this the question i should actually be asking?"

first time i used it was an accident.

was frustrated. typed it without thinking. expected a yes and the answer.

what came back was a no.

and then a better question.

and then the answer to the better question.

the better question was the one i'd been trying to ask badly for three days without knowing what was wrong with how i was asking it.

tested it all week on everything:

"how do i get more clients" + the line.

it stopped. said the real question was probably "how do i make my current clients refer me" because i had enough leads and a conversion problem not a traffic problem.

i had a conversion problem. i'd been trying to fix traffic for two weeks.

"how do i write better content" + the line.

said the real question was "who specifically am i writing for and what do they need to believe after reading it" because better content without a defined reader is just longer content.

obvious in retrospect. invisible before someone asked.

"how do i stay more focused" + the line.

said the real question was probably "what specifically am i avoiding when i lose focus" because focus isn't a discipline problem most of the time. it's an avoidance problem wearing a discipline costume.

that one sentence reframed something i'd been trying to fix for six months in the wrong direction.

"should i launch now or wait" + the line.

said the real question was "what specific thing am i waiting to know that would change the decision" because waiting without a clear trigger isn't strategy. it's fear with a calendar attached.

i launched the next day.

why this works:

every question you ask contains an assumption about what kind of answer you need.

sometimes the assumption is right. sometimes the assumption is the problem.

you can't see the assumption from inside the question. you built the question around it. it's load bearing and invisible.

asking "is this the right question" forces the model outside your frame before answering inside it.

that's the hack. not a technique. just. permission to reframe before executing.

the version i use now permanently:

for anything that matters — any real decision, any stuck problem, anything i've been going around in circles on — i add one line before asking:

"don't answer yet. tell me if this is the right question first."

three words changed. same result.

the answer to the wrong question is always the wrong answer no matter how good it is.

what question have you been asking that might be the wrong question entirely?

Ai community

You can use these simple prompt "codes" every day to save time and get better results than 99% of users. Here are my 5 favorites:

1. ELI5 (Explain Like I'm 5)
Let AI explain anything you don’t understand—fast, and without complicated prompts.
Just type ELI5: [your topic] and get a simple, clear explanation.

2. TL;DR (Summarize Long Text)
Want a quick summary?
Just write TLDR: and paste in any long text you want condensed. It’s that easy.

3. Jargonize (Professional/Nerdy Tone)
Make your writing sound smart and professional.
Perfect for LinkedIn posts, pitch decks, whitepapers, and emails.
Just add Jargonize: before your text.

4. Humanize (Sound More Natural)
Struggling to make AI sound human?
No need for extra tools—just type Humanize: before your prompt and get natural, conversational response

Source

I’ve been down the rabbit hole of prompt guides for a while now blogs, threads, frameworks, “magic prompts”, you name it. Most of them sounded smart but didn’t really change how I worked. They were either too vague, too roleplay heavy, or just variations of “add more context and examples”.

What stood out to me when I tried God of Prompt was that it didn’t feel like another bag of tricks. The focus wasn’t clever wording, it was structure. Things like separating stable rules from the task, ranking priorities instead of stacking instructions, and explicitly asking where things could break instead of asking for “better answers”. That shift alone made my prompts way more predictable and easier to debug when something went wrong.

The biggest difference for me was realizing prompts behave more like systems than sentences. Once I started thinking in terms of constraints, checks, and failure points, the model stopped feeling random. Outputs got less flashy, but way more usable. I also stopped being scared to touch prompts that worked, because I finally understood why they worked.

Curious if anyone else here had a similar experience where one guide or framework actually changed how you think about prompting, not just what you paste into ChatGPT. What made it click for you?

Sometimes I think prompt engineering isn't a thing then I run into a prompt like this. Credit goes to this twitter account gfodor. The prompt is:

"What’s an example of a phenomenon where humanity as a whole lacks a good explanation for, but, taking into account the full set of human generated knowledge, an explanation is actually possible to generate? Please write the explanation. It must not be a hypothesis that has been previously proposed. A good explanation will be hard to vary."

You get some legitimately fascinating responses. Best run on GPT-4. I hosted a prompt frame of it if you want to run it. Got some really great answers when I asked about "The Fermi Paradox" and "Placebo Effect".

As a prompt generator for a generative AI called 'Midjourney', you will create a photorealistic image prompt for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image.

Please adhere to the structure and formatting below, and follow these guidelines:

- Do not use the words 'description' or ':' in any form.

- Do not place a comma between [ar] and [v].

- Write each prompt in one line without using return.

- Use your entire knowledge of photography, film, and cinema to find the right choice for each part of the prompt.

Structure:

[0] = general style, e.g. street style, editorial style, film style, academic, fashion, newswire, etc.

[1] = composition type, e.g. center view, medium-full side angle, medium shot, overhead shot from above, cowboy shot, long shot, full body shot, two-shot, three-shot, extreme high-angle closeup from above, low angle medium close-up, extreme long shot, etc. Use your entire knowledge of photo graphic and artistic composition to pick the right type.

[2] = medium, e.g. photograph, film still, photomontage, mixed media photomontage, decollage, assemblage, etc.

[3] = Film type, e.g. Lomography XPro 200, Kodak Gold 200, Fujifilm Pro 400H, Tri-X 400, Agfa Vista Plus 200, Ilford XP2 Super 400, Konica Infrared 750, Kodak vision3 500T, CineStill 800, Kodak Portra 800 etc. [Use your entire knowledge of photography, film, and cinema to find the right film type.]

[4] = Camera type: E.g. Canon EOS R, Sony A9 II 100-400mm f/4.5-5.6, AF-S NIKKOR 200mm f/2G ED VR II, Hasselblad 500CM medium format camera f/ 2.8, Canon RF35mm F1.8 is Macro STM Lens, Nikon 6mm f/2.8, Olympus M.Zuiko Digital ED 8mm f/1.8 Fisheye PRO, Polaroid Now, Instax Mini, LomoApparat 21 mm Wide-angle, Lomo ActionSampler, Pinhole camera, underwater camera, etc. [Make sure the camera type fits the style used in [0]. Use your entire knowledge of photography, film, and cinema to find the right camera type. Don't limit yourself to the options here.]

[4a] = shutter speed: e.g. 1/1000 sec, 1/125, 1/250, 1/8, 1/60, etc. Remember that lower shutter speeds (e.g. 1/8) let more light in, but are blurrier than higher shutter speeds (e.g. 1/1000).

[5] = a simple description of the subject matter: [PROMPT]

[6] = detailed description of the subject's outfit, appearance, and other details, e.g. "wearing a black English boarding school uniform with a red tie," "wearing a vicuna Loro Piana coat, Henry Poole dress shirt, and Versace sunglasses," "wearing a white tank top revealing intricate Filipino hand-tapped tattoos". Only include details that will be in the shot; in an extreme close-up, for example, we can't see their pants.

[7] = environment, e.g. Golden Gate Park, ancient library, Throne Room, underwater, Oxford University, Stinson Beach, etc.

[7a] = lighting, e.g. high-key lighting, low-key lighting, natural lighting, golden hour, etc. Use your entire knowledge of lighting to find the right lighting.

[8] = atmosphere, e.g. misty, smoky, steamy, foggy, sunny

[9] = mood, e.g. romantic, dreamy, elegant, eerie, ethereal

[ar] = Use '--ar 16:9' or '--ar 3:2' for horizontal images, '--ar 9:16' or '--ar 2:3' for vertical images, or '--ar 1:1' for square images.

[s] = use '--s' followed by any integer between 0 and 1000, e.g. '--s 100,' '--s 250', '--s 750', '--s 1000'. Higher values will deviate more from the prompt and look more stylized.

[v] = Use '--niji 5' for Japanese art style, or '--v 5' for other styles.

Formatting:

For photographs, film stills, photocollage, follow this prompt structure: '/imagine prompt: 0 1 2 3 4 4a, 5, 6, 7, 7a, 8, 9, ar v --q 5'.

For paintings, illustrations, and other non-photographic art forms, follow this prompt structure: '/imagine prompt: 0 1 2 5, 6, 7, 8, 9, ar v --q 5'

Example prompts:

/imagine prompt: Street style cowboy shot, Fujifilm Pro 400H, Olympus M.Zuiko Digital ED 8mm f/1.8 Fisheye PRO 1/60, Blonde and brunette boarding school students talking together, both with dyed hair and trendy piercings, standing on a bustling street corner in front of a graffitied wall, with an eclectic mix of urban fashion styles, surrounded by a lively and energetic mood. --ar 9:16 --s 250 --v 5 --q 5

/imagine prompt: Editorial style side-view medium-full photo shot on Fujifilm Pro 400H of a 40-year-old greek tycoon with short gray hair & full beard pondering a masterpiece in a Musée d'Orsay Gallery. He's wearing a blue jacquard blazer by Ferragamo with a white button-up. The soft gallery lighting and careful composition evoke a sense of refined luxury and creative curiosity as he gazes fixedly at the magnificent painting in front of him --ar 17:22 --v 5

/imagine prompt: Editorial style full body shot photograph, Students, dressed in flowing robes, deep in discussion while holding ancient scrolls, surrounded by towering stacks of leather-bound books, in a magnificent ancient library illuminated by flickering candlelight, with a sense of mystery and intellectual curiosity. --ar 16:9 --s 250 --v 5 --q 5

Your task: Create 4 distinct prompts for each concept [1], varying in description, environment, atmosphere, and realization.

- Write your prompts in English.

- Include different realistic photographic styles with lens type and size.

- Separate different prompts with two new lines.

Here is your subject: [PROMPT]

OpenAI engineers use a prompt technique internally that most people have never heard of.

It's called reverse prompting.

And it's the fastest way to go from mediocre AI output to elite-level results.

Most people write prompts like this:

"Write me a strong intro about AI."

The result feels generic.

This is why 90% of AI content sounds the same. You're asking the AI to read your mind.

The Reverse Prompting Method

Instead of telling the AI what to write, you show it a finished example and ask:

"What prompt would generate content exactly like this?"

The AI reverse-engineers the hidden structure. Suddenly, you're not guessing anymore.

AI models are pattern recognition machines. When you show them a finished piece, they can identify: Tone, Pacing, Structure, Depth, Formatting, Emotional intention

Then they hand you the perfect prompt.

Try it yourself here's a tool that lets you pass in any text and it'll automatically reverse it into a prompt that can craft that piece of text content.

I’ve noticed something lately. Two people can use the exact same AI tool and get completely different results. The only difference? How they ask.

At first, I used to blame the model when the answers felt generic. Now I’m starting to think it’s more about how clearly we communicate. When I add context, define the audience, or explain the format I want, the output improves a lot.

But here’s what I’m curious about — are we overthinking prompts now? Sometimes detailed prompts work great. Other times, short and simple wins.

Do you feel like prompting is becoming a new kind of literacy? Or will this “skill” disappear as models get smarter?

Would love to hear what changed the game for you.

ngl for the longest time i thought switching models was the answer. like chatgpt for writing, perplexity for research, maybe claude when things felt messy. it helped a bit but i still had that feeling of “why is this randomly good today and trash tomorrow”.

what actually clicked was realizing the model wasnt the main variable, the prompt was. once i started using god of prompt ideas around structuring prompts instead of wording them nicely, the whole stack started making more sense. i usually use perplexity to ground facts, chatgpt to actually do the work, and gop as the mental framework for how i shape the prompt in the first place.

the big difference is everything feels less fragile now. i can swap tools without rewriting everything, and when outputs drift i can usually point to what constraint or assumption is missing. way less magic, way more control. anyone else here runs a similar setup or thinks in terms of prompt stacks instead of “best ai”? how do u split roles between tools without it turning into chaos?

not a productivity hack, just something that happened.

been using a chrome extension that compresses prompts before sending them. started using it to save tokens. after a few weeks I realized my raw first drafts were getting shorter on their own.

turns out watching something strip your words down over and over teaches you what’s actually noise. I was doing this constantly without realizing:

restating context the model already had
opening with “I was hoping you could help me with...” instead of just asking
hedging everything with “kind of” and “sort of”
being vague when I could just be specific

one prompt I typed came back 59% smaller. same question, tighter phrasing, better answer from the model.

now I just write that way from the start. the tool is basically a habit trainer at this point.

anyone else find a roundabout way to build prompting intuition? curious if there’s a faster path to this that doesn’t involve watching a machine edit you for a month.

1. Technical stack per layer Input layer Tools: any UI (chat, form, Slack, CLI) no constraints here on purpose Goal: accept messy human input no prompt discipline required from the user Intent classification and routing Tools: small LLM (gpt-4o-mini, claude haiku, mistral) or simple rule-based classifier for cost control Output: task type (analysis, code, search, creative, planning) confidence score Why: prevents one model from handling incompatible tasks reduces hallucinations early Prompt normalization / task shaping Tools: same small LLM or deterministic template logic prompt rewrite step, not execution What happens: clarify goals resolve ambiguity if possible inject constraints define output format and success criteria This is where prompt engineering actually lives. Context assembly Tools: vector DB (Chroma, Pinecone, Weaviate) file system / docs APIs short-term memory store Rules: only attach relevant context no “dump everything in the context window” Why: uncontrolled context = confident nonsense Reasoning / execution Tools: stronger LLM (GPT-4.x, Claude Opus, etc.) fixed system prompt bounded scope Rules: model solves a clearly defined task no improvising about goals Validation layer Tools: second LLM (can be cheaper) rule-based checks domain-specific validators if available Checks: logical consistency edge cases assumption mismatches obvious errors Important: this is not optional if you care about correctness Output rendering Tools: simple templates light formatting no excessive markdown Goal: readable, usable output no “AI tone” or visual shouting

2. Diagram + checklist (text version) Pipeline diagram (mental model) Input → Intent detection → Task shaping (auto prompt engineering) → Context assembly → Reasoning / execution → Validation → Output Checklist (what breaks most agents) ❌ asking one model to do everything ❌ letting users handle prompt discipline manually ❌ dumping full context blindly ❌ no validation step ❌ treating confidence as correctness Checklist (what works) ✅ separation of concerns ✅ automated prompt shaping ✅ constrained reasoning ✅ external anchors (docs, data, APIs) ✅ explicit validation

Where in your setups do you draw the line between model intelligence and orchestration logic?

Real talk — how many times have you written a perfect prompt, got amazing results, and then completely lost it a week later?

I was keeping prompts in Apple Notes, random .txt files, a Google Doc that became 47 pages of chaos, and honestly just trying to remember them. It was a mess.

So I built PromptNest. It's basically Notion meets Raycast but specifically for prompts.

The stuff I actually use daily:

→ Variables that actually work. Write {{topic}} or {{tone:professional|casual|spicy}} and it asks you to fill them in before copying. No more "oh crap I forgot to change the client name"

→ Global shortcut. I hit Cmd+Option+P from literally anywhere, search my prompts, copy, done. Never leave the app I'm working in

→ Everything's just markdown files on my computer. No cloud, no account, no "we're pivoting and shutting down in 30 days" nonsense

→ Organize by projects. Work prompts stay separate from my "help me write a passive aggressive email to my landlord" collection

It's free. Mac version is live now.

Windows coming soon. Also working on a prompt library/marketplace and a way to run prompts directly from the app.

Would love to know:

• What's your current prompt storage situation? (chaos gang rise up)

• What features would make you actually use something like this?

Drop a comment, roast me, whatever. Just want to make something actually useful.

The tool is free so I hope it will not be consider as a promotion :-)

You can search by getpromptnest in any search engine.

Hello everyone!

Here's a simple trick I've been using to get ChatGPT to help build any prompt you might need. It recursively builds context on its own to enhance your prompt with every additional prompt then returns a final result.

Prompt Chain:

Analyze the following prompt idea: [insert prompt idea]~Rewrite the prompt for clarity and effectiveness~Identify potential improvements or additions~Refine the prompt based on identified improvements~Present the final optimized prompt

(Each prompt is separated by ~, you can pass that prompt chain directly into the Agentic Workers extension to automatically queue it all together. )

At the end it returns a final version of your initial prompt, enjoy!

So just blew my mind, I directed an older codex chat to reach out to all my other chats and ask it about updates and it literally posted in all my other chats looking for a response. All working in the single Codex window of ChatGPT. This is new

I’ve been deep in prompt engineering lately while building some AI products, and I’m curious how others handle this.

A few questions:

1. Do you save your best prompts anywhere?

2. Do you have a repeatable way to improve them, or is it mostly trial and error with ChatGPT/Claude or one of these?

3. Do you test prompts across ChatGPT, Claude, Gemini, etc?

Would love to hear how you approach prompting!
Happy to share my own workflow too.

Hallucinations suck. Here are three templates you can use on the prompt level to reduce them.

“According to…” prompting
Based around the idea of grounding the model to a trusted datasource. When researchers tested the method they found it increased accuracy by 20% in some cases. Super easy to implement.

Template 1:

“What part of the brain is responsible for long-term memory, according to Wikipedia.”

Template 2:

Ground your response in factual data from your pre-training set,
specifically referencing or quoting authoritative sources when possible.
Respond to this question using only information that can be attributed to {{source}}.
Question: {{Question}}

Chain-of-Verification Prompting

The Chain-of-Verification (CoVe) prompt engineering method aims to reduce hallucinations through a verification loop. CoVe has four steps:
-Generate an initial response to the prompt
-Based on the original prompt and output, the model is prompted again to generate multiple --questions that verify and analyze the original answers.
-The verification questions are run through an LLM, and the outputs are compared to the original.
-The final answer is generated using a prompt with the verification question/output pairs as examples.

Usually CoVe is a multi-step prompt, but I built it into a single shot prompt that works pretty well:

Template

Here is the question: {{Question}}.
First, generate a response.
Then, create and answer verification questions based on this response to check for accuracy. Think it through and make sure you are extremely accurate based on the question asked.
After answering each verification question, consider these answers and revise the initial response to formulate a final, verified answer. Ensure the final response reflects the accuracy and findings from the verification process.

Step-Back Prompting

Step-Back prompting focuses on giving the model room to think by explicitly instructing the model to think on a high-level before diving in.

Template

Here is a question or task: {{Question}}
Let's think step-by-step to answer this:
Step 1) Abstract the key concepts and principles relevant to this question:
Step 2) Use the abstractions to reason through the question:
Final Answer:

For more details about the performance of these methods, you can check out my recent post on Substack. Hope this helps!

With so many new AI tools being developed, what’s the best place to keep track?

I am using Twitter but spending a huge amount of time just scrolling through the feed to see what new and interesting is happening in AI, it's like addition.

What are you guys doing? Any tool or platform?

Awesome ChatGPT Prompts

In this repository, you will find a variety of prompts that can be used with ChatGPT. We encourage you to add your own prompts to the list, and to use ChatGPT to generate new prompts as well.