🏴‍☠️ The six-part skeleton behind 12 prompts

Most 'ultimate prompt' lists collapse into one tired line: act as an expert and help me with X. That advice is worthless. One Reddit contributor set out to prove why real structure beats vague flattery, and the examples make the case cleanly.

The original poster, a computer science master's student writing as u/ismse, dropped 12 ChatGPT prompts into r/ChatGPTPromptGenius and pulled 152 upvotes in the process. They are a free sample from a claimed 300-prompt library, with the remaining 288 sold through an ebook. Commenters called the whole thing an advert, and they had a fair point worth knowing before you read it.

Here is why I think this matters more than another prompt dump: the real lesson is the recipe, not the prompts themselves. Every single one follows the same six-part skeleton, so once you learn it you can rebuild any prompt you already lean on. That portable pattern is the value I want to hand you today.

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Line the 12 up and the same architecture repeats. A specific role, not 'expert' but 'staff engineer with 15 years in this language' or 'negotiation coach trained in the Chris Voss method.' Precision in the role sets the vocabulary the model reaches for.

Then come context slots: brackets for the job post, your BATNA, your goal, your source text. The model now works from your facts, not its guesses. That single shift kills most generic output.

Next, a numbered task with a fixed order, so results come back in a predictable shape. Then hard constraints: word limits, banned words, a target reading level. 'Never use passionate, hard-working, detail-oriented' does far more than 'sound professional.'

The last two parts are where I think most people's prompts fall apart. An output format pins the shape, with a subject line plus body, three variations, one final pick. And a closing forcing function like 'the one change I should make first if I only have 30 minutes' makes the model commit to a priority instead of dumping a list.

The creator splits them across four jobs. For outreach: a cold email engine capped at 90 words with one proof point and a yes or no call to action, a freelance proposal that opens on a specific detail from the job post, and a negotiation simulator that hands you exact words rather than generic advice.

For content, one source piece gets atomized into a thread, a LinkedIn post, a caption, hooks and a newsletter intro. A hook laboratory spits out five hooks, each on a different pattern. A ruthless editor cuts text by half and lists the three highest-impact cuts.

For thinking and work, the set covers first-principles learning, a layered explainer running from age eight to practitioner, a decision audit, a senior-engineer code review with severity labels, a resume rewriter, and a weekly operating review. That is a lot of surface area. The common thread: structure, not persona.

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Two details sold me here. The resume prompt says: if the metric isn't provided, ask me for it. That is a failure-mode instruction, and it is the single most useful line in the whole set.

It tells the model what to do when it lacks information, so it asks instead of inventing a number. You can bolt that onto almost any prompt you own. The review and audit prompts win for a related reason: ordered steps that end on one committed answer are far easier to act on.

Now the warnings. The '$10M in pipeline' and 'top-1% freelancer' personas flavor the tone but add no real expertise; the constraints do the heavy lifting. And when the decision audit asks what 'statistically works out,' ChatGPT may hand you confident numbers with no source.

So treat those figures as a starting point and verify them. The author claims 60 hours of testing but shares no before-and-after outputs. Run each prompt on your own material and judge for yourself.

Reading a six-part recipe is easy; reaching for it on reflex takes reps. 3 Minute AI is built for that: three-minute lessons that each end with a task you run in a built-in chat lab, including a chatgpt track where you rebuild prompts like these instead of just reading them. Daily lessons and two full courses are free.

If you take one, take the decision audit. You feed it two options and your real context, then it runs a weighted criteria matrix, a pre-mortem on each choice failing in two years, base rates, a reversibility check, and a regret-minimization pass through the eyes of your 70-year-old self. It closes on a single recommendation and the one piece of information to gather first.

Here is the move I'd make: add one sentence telling it to flag any base rate it is unsure about instead of guessing. That is the same failure-mode trick from the resume prompt, ported over. Then rebuild one of your own daily prompts with all six parts and run the comparison.

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Pick one prompt you already lean on every week and rebuild it with all six parts: role, context, a numbered task, constraints, an output format, and a closing line that tells the model to ask for missing data instead of guessing.

If you want the twelve examples laid out plus that failure-mode line you can paste straight in, here is the full breakdown and the six-part recipe.

Worth 10 minutes if you want to see your own prompt get sharper from structure alone, not from a longer wishlist of instructions.

Credits to the original creator.