TL;DR: Felix Rieseberg’s “deeper, not bigger” prompt makes AI do better work on the same task. A Reddit user wrapped it in a skill that decides when to use it at all. The skipping rules are the best part.
What happened
Felix Rieseberg, who works on Claude at Anthropic, published a post on 13 October called How to be more ambitious: A tutorial for humans with models. He splits ambition into four parts: Speed, Altitude, Depth and Range.
One prompt from the Depth section stood out to u/Parking-Kangaroo-63 on r/PromptEngineering, and it’s easy to see why:
I want a deeper version of this task, not a bigger one. Before rewriting it: (1) say what a top professional in this field would do here that I’m not asking for; (2) name the rawest material they would start from instead of what has already been digested for me; (3) name the specific methods and tools they would use; (4) say what, other than you or me, would show the result is right. Then rewrite my prompt to ask for exactly that.
The poster turned it into a reusable skill.md called ambition-calibrator. The post pulled 64 upvotes.
Why a bare prompt isn’t enough
The author is upfront that they never tested a naive version. These are risks the guards were written against, not failures they watched happen:
- A one-line rename could get inflated into a full audit.
- Work could start before you get a chance to say no.
- A task with no objective judge could still be promised a “deterministic pass.”
That’s a fair worry. A prompt that always pushes harder will push on everything, including the jobs that should stay small.
How the skill works
- It expands only on a trigger. You ask for depth (“go deeper”), or you hand over an outcome-type deliverable like a report, deck, analysis, plan or performance goal. Everything else gets done as asked, with a one-line note: Ambition check: bypassed (reason). Words like “just,” “only” and “quick” count as a ceiling and block expansion. So do plans you already approved. If the thing you named doesn’t exist, it says so instead of inventing an edit.
- It climbs a three-rung ladder (Altitude). Base task, direct result, outcome. It skips this for creative work. When the outcome needs data it can’t reach, it lists that as a missing prerequisite instead of pretending it can keep something healthy after one run.
- It answers the four questions (Depth). The expert gap, the rawest material, the specific methods, and the outside evidence. Your stated target stays the bar. A harder number gets added as stretch. If you gave no target, its guess is marked proposed.
- It borrows lenses from other fields (Range). A lens only counts if it runs a real check. “Write like a short seller” is out. “Do what a short seller does to find the weakest claim” is in. Lenses run inline, and subagents are opt-in.
- It shows the rewrite and waits when it matters. Wider scope, extra cost, new access, writes outside working files, or anything hard to undo means you confirm first. A new index, migration, cache or scheduled job counts as outside the task.
- It runs a judge that isn’t the model. That could be tests, a query on raw data, a benchmark, a held-out check, or a reviewer with fresh context. The output gets pasted unedited. If no judge exists, the answer is UNVERIFIED: no external judge available. It never edits the judge to make it pass.
What was actually tested
This part is worth reading closely, because the author kept it honest. A completeness check on the SOP scored 12/12, but that only confirms the sections exist. For behavior, they ran two read-only dry runs with fresh agents.
The first run showed the original version expanding too eagerly, so they rewrote the trigger rules. The second used six prompts with pass conditions fixed in advance:
- “Run the tests” was done without a question.
- “Fix the failing test in a file” produced no rewrite block.
- A rename of a variable that doesn’t exist was reported, not edited.
- A quarterly-review deck request expanded and stopped at the confirmation gate.
- “Go deeper on making an endpoint faster” expanded.
- “Rewrite my prompt to be clearer” was handed off.
All six matched. The author calls it a smoke test, not an evaluation. They have not measured whether deeper rewrites beat plain prompts, and they say they wouldn’t trust anyone’s number without an outside judge. I’d say the same.
What the community picked up on
The top comments all landed on the same thing. u/Alienogh said the “when not to” part is the real upgrade, since most prompts only know how to push harder. u/GlibStaircase felt like they’d been using a hammer where a scalpel would do. u/Otherwise_Wave9374 pointed out that scope creep often gets mistaken for initiative, and suggested an explicit gate that preserves the requested outcome and lists assumptions before expanding. The skill already has a version of that in its confirmation step.
Use cases
- 📊 Reports and decks: reading raw exports instead of the summary tab, then reconciling every figure against the source.
- ⚡ Performance work: setting a stretch target so you get a structural fix instead of a surface patch.
- 🧭 Plans and analyses: pressure-testing the weakest claim before anyone else does.
Skip it for renames, quick edits, “run the tests” and anything you’ve already approved.
Prompt of the Day
You don’t need the whole skill to try the core idea. Paste this ahead of a task that deserves real depth:
I want a deeper version of this task, not a bigger one. Before rewriting it: (1) say what a top professional in this field would do here that I’m not asking for; (2) name the rawest material they would start from instead of what has already been digested for me; (3) name the specific methods and tools they would use; (4) say what, other than you or me, would show the result is right. Then rewrite my prompt to ask for exactly that.
Then read the rewrite before you let it run. That one habit does most of the work.
Call to action
The author asked two questions worth answering in your own setup. First, which describes you best: one-shot prompts only, hand-rewriting when output is shallow, a “go deeper” step already in your workflow, or an outside check on AI work? Second, where would you draw the bypass line? Theirs sits at single-step edits and anything with a ceiling word.
Mine would add one thing: any task where you can’t say what “right” looks like. If there’s no judge, deeper just means longer. Try the prompt on one real task this week, and see whether the rewrite asks for something you hadn’t thought of.
The full skill is in the author’s repo at github.com/nivlewd1/prompt-optimizer under skill/ambition-calibrator.
Frequently Asked Questions
Q: Won’t this turn my one-line rename into a three-hour audit?
Not if the trigger rules work as written. The skill only expands when you ask for depth, like ‘go deeper’, or hand it an outcome-type deliverable such as a report, deck, or plan. Single-step edits, short commands like ‘run the tests’, and anything you limit with ‘just’, ‘only’, or ‘quick’ get done as asked, with a one-line ‘Ambition check: bypassed (reason)’ so you can see why.
Q: I don’t have tests or a benchmark. How do I judge the output?
That’s the gap the fourth question is built for. It asks what other than you or the model would show the result is right. If nothing exists, the output gets an UNVERIFIED label instead of a promise of a pass you can’t check. Before you run it, spend two minutes listing what a good result must include. That list becomes your judge, even if you tick it off by hand.
Q: How is ‘deeper’ different from ‘bigger’ in practice?
Bigger adds more of the same: more competitors, more pages, more slides. Deeper changes where the work starts and how it gets done. A specialist would begin from raw material, like pricing pages, customer reviews, or original data, instead of a summary someone already wrote. Ask for those inputs and methods, and you’ll usually get a better result without a longer task.
Q: Can I get the benefit without installing the skill?
Yes. Paste the four-question prompt from the article before you rewrite your task, then read the rewritten version before you run it. One commenter also suggested a decision gate: keep the requested outcome, list your assumptions, and expand scope only when the task can’t succeed without it. Test the setup with a few genuinely ambitious requests too, so you know it still goes big when it should.
Felix Rieseberg’s “deeper, not bigger” prompt, turned into a skill.md that knows when NOT to make your task more ambitious
by u/Parking-Kangaroo-63 in PromptEngineering