# prompt-master: a Claude skill that builds prompts for other AI tools

> prompt-master is a Claude skill for writing prompts aimed at other AI tools, from Cursor to Midjourney. It is a prompt generator rather than a prompt library, and the README is explicit about the trade-offs it makes.

**nidhinjs/prompt-master** — A Claude skill that writes the accurate prompts for any AI tool. Zero tokens or credits wasted. Full context and memory retention

- Repository: https://github.com/nidhinjs/prompt-master
- Stars: 13,225 · Forks: 1,539
- Language: Unknown
- License: MIT
- Published: 2026-09-09 · Updated: 2026-09-09 · Language: en
- Canonical page: https://hysenlabs.com/projects/nidhinjs-prompt-master

## What prompt-master actually solves, and for whom

The README frames the problem as wasted attempts: you write a vague prompt, get the wrong output, re-prompt, and land on the answer you wanted on attempt four. Its stated claim is that this costs three extra API calls per prompt. That is the audience: people who pay per token or per credit and who notice the retries, rather than hobbyists chatting for free.

The skill is not a prompt library you copy from. It is a generator that runs inside Claude and produces a prompt for a different system. The README lists the targets it claims to handle: Claude, ChatGPT, Codex, Grok, Gemini, o1/o3, MiniMax, Cursor, Claude Code, GitHub Copilot, Windsurf, Bolt, v0, Lovable, Devin, Perplexity, Midjourney, DALL-E, Stable Diffusion, ComfyUI, Sora, Runway, ElevenLabs, Zapier and Make. That list is broad enough to be a positioning statement rather than a compatibility matrix, and the repository does not ship per-tool test fixtures, so treat the breadth as intent.

The one idea the README repeats is quoted directly: "The best prompt is not the longest. It's the one where every word is load-bearing." Everything else in the design follows from that. Most generators add tokens; this one is built to remove them.

## The eight-step pipeline behind the skill

The README describes a fixed sequence. First the skill detects the target tool and routes to the right approach without asking. Then it extracts nine dimensions of intent: task, input, output, constraints, context, audience, memory, success criteria and examples. If critical information is missing it asks at most three clarifying questions, and the README states that ceiling explicitly. It then selects a prompt framework and applies it without showing the choice to the user, drawing on role assignment, few-shot examples, XML structure, grounding anchors and a memory block. A recency check verifies exact models and controls against provider documentation when the request depends on "latest". A token efficiency audit strips words that do not change the output. The result is one copyable block plus a one-line strategy note.

Two of those steps are worth separating from the marketing. The three-question cap is a real constraint, and it means the skill will guess rather than interrogate you when your request is ambiguous in a fourth way. The recency check is the more interesting one: it implies the skill consults provider documentation at runtime, which the README does not explain further. Nothing in the repository layout (LICENSE, README.md, SKILL.md, references/) shows a bundled model index, so how that check resolves is unclear from the README.

The output format is visible in the two worked examples. The Midjourney example returns comma-separated descriptors with `--ar 16:9 --v 6 --style raw` and a separate negative prompt line. The Claude Code example returns a long structured brief with Objective, Stack, Design Spec, numbered sections, Animations, Constraints and Done When blocks.

## Installing prompt-master and writing your first prompt

The README gives two installation paths and marks one as recommended. The recommended route is a ZIP upload: download the repository as a ZIP, then in claude.ai go to Sidebar, Customize, Skills, and choose Upload a Skill. There is no package manager and no npm or pip step, because the artifact is a skill definition rather than a library.

The second route clones the repository into the Claude Code skills directory. The README labels this "Not Suggested", which is unusual enough to take at face value: the browser upload is the path the author expects most people to use.

```bash
mkdir -p ~/.claude/skills
git clone https://github.com/nidhinjs/prompt-master.git ~/.claude/skills/prompt-master
```

After either install, you invoke the skill in natural language. The README's first example is a one-line request naming the target tool:

```
Write me a prompt for Cursor to refactor my auth module
```

The README also shows an explicit invocation with the slash command, which is the form to use when you want to be certain the skill runs rather than Claude answering directly:

```
/prompt-master

I want to ask Claude Code to build a todo app with React and Supabase
```

What you should see is a single prompt block for the named tool, followed by the strategy note. The README's Midjourney example shows the shape: descriptors, then `--ar 16:9 --v 6 --style raw`, then a negative prompt line. If you get a conversational answer instead of a block, the skill did not trigger and the explicit `/prompt-master` form is the fallback.

## Where the token-efficiency claim gets thin

The promise is zero wasted tokens and credits. The mechanism is a stripping pass that removes words which do not change the output. That is a reasonable heuristic and it is testable in principle, but the repository does not ship a benchmark, a token count comparison, or a set of before-and-after pairs beyond the two examples. The README's own claim of three wasted API calls per prompt is an illustration, not a measurement.

The second limitation is scope. The skill runs inside Claude. If your team's work happens in Cursor, Copilot or an internal agent, prompt-master can help you author the prompt, but it adds a step: you leave your tool, ask Claude, then paste the result back. For a single prompt that is fine. For a pipeline that generates prompts programmatically, it is the wrong shape entirely, because there is no CLI, no API and no importable module in the repository layout. The README's Claude Code example is also a reminder that the generated output can be long. A structured brief with eight sections is not token-light, and the efficiency audit is about removing dead words, not about keeping prompts short.

Finally, the three-question cap is a genuine failure mode. Ask for something underspecified in four dimensions and the skill will fill the gaps with defaults you never approved, then hand you a confident prompt built on them.

## prompt-master against a plain prompt library

The obvious alternative is a curated prompt library: a repository of tested prompts you copy, adapt and keep. The difference in approach is generation versus selection. A library gives you a known-good prompt for a known task and nothing for the task it has not seen. prompt-master gives you a prompt for any task you can describe, but the quality depends on the pipeline's inference about your intent.

That trade shows up in what you can audit. A library prompt is static; you can read it, diff it and pin a version. A generated prompt is a function of your phrasing, the nine extracted dimensions and whatever clarifying questions the skill chose to ask, so two runs on similar requests can diverge. For a team that needs reproducibility, a library plus a review step is the safer choice. For one-off requests where no suitable prompt exists yet, generation wins on coverage.

The README's own framing supports that split. The Midjourney example is essentially a library entry the skill produced on demand: descriptors, aspect ratio, version, negative prompt. The Claude Code example is the opposite case, a long brief no library would ship because it is specific to one landing page.

## Maintenance, licence and what to check before adopting

The repository is not archived, and the last push was on 2026-08-24. That is recent enough that the project is not abandoned, but the repository ships no releases, so there is no version number to pin and no changelog to read. Upgrading means pulling the default branch again, either re-uploading the ZIP on claude.ai or running a `git pull` inside `~/.claude/skills/prompt-master`. The cost of an upgrade is therefore low in effort and low in visibility: you will not get a diff summary telling you what changed in SKILL.md.

The licence is MIT, which permits commercial use, modification and redistribution provided the copyright notice and permission notice are retained. That is the plain reading of the identifier; for anything involving redistribution inside a product, read the LICENSE file itself rather than relying on the SPDX label.

The maintenance risk is not the code, it is the target list. The skill's value depends on knowing how Cursor, Midjourney, Claude Code and the rest expect prompts to be shaped, and those interfaces change without notice. The README's recency check is the mitigation, but since the repository has no release cadence, you are trusting a single branch to track a moving set of providers.

## Conclusion

Adopt prompt-master if you already work inside Claude and want a repeatable way to turn a rough request into a prompt aimed at a specific tool, especially for coding agents and image generators. Skip it if you need a programmatic prompt API, a versioned prompt registry, or anything that runs outside Claude; the repository is a skill definition plus references, not a service. Before relying on it, verify two things yourself: that the SKILL.md pipeline matches the tool you actually use, and that the shipped examples produce output you would accept, because the README shows Midjourney and Claude Code examples but no evaluation of how often the clarifying questions fire.

## FAQ

### What is prompt-master?

It is a Claude skill that writes prompts for other AI tools, from Cursor and Claude Code to Midjourney and Stable Diffusion. The README describes it as a generator that runs a structured pipeline on each request and returns one copyable prompt block with a one-line strategy note.

### How do I use prompt-master in Claude?

Install it first, either by uploading the repository ZIP through claude.ai under Sidebar, Customize, Skills, Upload a Skill, or by cloning it into ~/.claude/skills/prompt-master. Then ask in natural language, for example "Write me a prompt for Cursor to refactor my auth module", or invoke it explicitly with /prompt-master.

### What is the Claude Code Skill prompt Master?

It is the same skill used from Claude Code rather than the browser. The README's alternative install clones the repository into the Claude Code skills directory, a route it labels "Not Suggested" in favour of the ZIP upload on claude.ai.

### What does a master prompt do?

In this project the term refers to a prompt where every word changes the output, which is the README's stated goal: "The best prompt is not the longest. It's the one where every word is load-bearing." The skill reaches that by extracting nine dimensions of intent and then stripping words that do not affect the result.

### What are master prompts in ChatGPT?

prompt-master does not define that term for ChatGPT specifically. It lists ChatGPT among the tools it can target, and its pipeline is the same regardless of which tool you name, so a prompt written for ChatGPT goes through the same nine-dimension extraction and token audit as one written for Cursor.

## Sources

- [Issues](https://github.com/nidhinjs/prompt-master/issues)
- [License: MIT](https://github.com/nidhinjs/prompt-master/blob/main/LICENSE)
- [nidhinjs/prompt-master on GitHub](https://github.com/nidhinjs/prompt-master)
- [README](https://github.com/nidhinjs/prompt-master/blob/main/README.md)

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Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/nidhinjs-prompt-master