Yesterday, a builder on r/PromptEngineering dropped something worth installing. The skill is called prompt-master. You give it a rough idea, it asks 1 to 3 clarifying questions if anything is unclear, then outputs a precision prompt for your target AI tool. Not a generic template. Not a fill-in-the-blank structure you found in a YouTube video. An actual working prompt, shaped for the specific AI you are about to run it in.
Here is the twist: it knows which tool you are targeting.
Claude, GPT, Cursor, Midjourney, Claude Code. prompt-master detects the target and picks the right prompt framework automatically. CO-STAR for business writing. ReAct with stop conditions for agentic tasks. Visual Descriptor for image generation. No more copy-pasting a generic prompt structure and hoping it translates across tools. This matters more than most people realize, because the same prompt that works beautifully in Claude will produce mediocre output in Midjourney, and vice versa. Each model has a different “native language” for instructions, and prompt-master speaks all of them.
How It Works 🔧
- Feed it your rough idea in plain language
- Answer 1 to 3 targeted questions (only when something is ambiguous)
- Get a clean, tool-specific prompt back
- Paste and run. First attempt, not fourth.
Behind the scenes, it pulls 9 dimensions out of your request: task, output format, constraints, context, audience, memory from prior messages, success criteria, examples, and more. When your conversation already has history, it adds a Memory Block so the AI does not contradict decisions you made earlier in the thread. That Memory Block alone solves one of the most frustrating problems in longer sessions, where the model forgets that you already decided on a tech stack, a tone, or a scope boundary three messages ago and starts drifting in a different direction.
The clarifying questions are worth paying attention to. prompt-master does not ask for the sake of asking. If your input is specific enough, it skips the questions entirely and goes straight to the output. When it does ask, the questions are surgical. Things like “Are you targeting a general audience or technical readers?” or “Should the output be a single block or broken into steps?” Answering those takes ten seconds and saves you two revision cycles.
Pro tip: before running prompt-master, spend 30 seconds writing your rough idea as if you are explaining it to a colleague in Slack. Informal, incomplete, with context. That gives the skill enough signal to ask smarter questions and produce a tighter output. Perfectly polished input actually gives it less to work with.
35 Patterns That Are Draining Your Credits 💡
The skill ships with 35 documented credit-killing patterns, each with before and after examples. A few that hit hard:
- No file path when prompting in Cursor? Output is almost always wrong.
- Adding chain-of-thought instructions to o1 models? It makes results worse, not better.
- Building an entire app in one prompt? You get chaos, not code.
- No stop conditions on agentic tasks? The agent runs until something breaks.
These are not theoretical warnings. They are patterns the builder identified from real failure runs and encoded directly into the skill so you stop paying for the same mistakes twice.
A few more from the list that developers consistently underestimate: prompting without specifying an output length leads to wildly inconsistent results across runs, making it impossible to build reliable pipelines around the output. Asking for “improvements” without defining what good looks like gives the model permission to change things you did not want changed. And providing an example in the wrong format for the target tool teaches the model the wrong pattern before it even starts generating.
The before-and-after format for each pattern is where the real value is. You are not just reading a rule, you are seeing the exact weak version of the prompt, the specific failure it causes, and the corrected version side by side. That format turns passive knowledge into something you can immediately apply. Bookmark the patterns doc and scan it before you start any new prompting session with a tool you have not used in a while.
Go Try It 🚀
The repo is live at github.com/nidhinjs/prompt-master. Five minutes to install as a Claude skill. The setup follows the standard Claude skill pattern, so if you have installed any skill before, this will feel familiar. If you have not, the repo includes a straightforward walkthrough that gets you running without digging through documentation.
If you are regularly hitting attempt three or four before getting usable output from any AI tool, this is the workflow fix worth testing this week. Start with one prompt you have been struggling with. Run it through prompt-master, paste the output, and compare the result to what you were getting before. That single test run will tell you everything you need to know.
Which credit-killing prompt mistake do you keep repeating? Drop it in the comments.
Frequently Asked Questions
Q: Do I really need all these prompt frameworks?
Not necessarily, the real lever is structure, not fancy techniques. Breaking your prompt into clear sections (intent, context, constraints, output format) matters more than advanced methods like Chain-of-Thought, which can sometimes backfire. Start simple and well-organized; add complexity only if needed for bigger workflows.
Q: Won’t invoking the skill add friction to my workflow?
That’s a valid trade-off. The skill requires manual invocation, whereas some automated plugins detect vague prompts without interrupting conversation flow. If you prefer natural interaction without interruptions, auto-detect tools might feel smoother. If you want conscious control over each refinement step, manual invocation gives you that choice.
Q: Will these advanced prompt techniques make models hallucinate?
Modern models can struggle with aggressive chaining and multi-step reasoning at high complexity, they may take shortcuts or fabricate answers. The skill helps you avoid risky anti-patterns, but stay cautious with exotic frameworks and always verify Claude’s output if something seems off, especially in complex chains.
Q: How should I organize a prompt so the model actually reads all of it?
Models tend to focus deeply on the first 20-30%, skim the middle section, then pay attention to success criteria. Put your most critical instructions upfront, separate competing frameworks (don’t throw all techniques together), and keep expected output format clear at the end. This prevents context bleed and ensures important instructions land.
I built a Claude skill that writes prompts for any AI tool. Tired of running of of credits.
by u/CompetitionTrick2836 in PromptEngineering