Yesterday a new Claude skill dropped that changes how you think about prompt engineering.
Not a “prompt improver.” An actual prompt engineer. There’s a difference, and it matters more than most people realize. A prompt improver takes what you wrote and polishes the language. A prompt engineer looks at the structure underneath and asks why the output keeps missing the mark. One gives you better words. The other gives you a working system. Most tools calling themselves “prompt improvement” are in the first category. DailyForge is firmly in the second.
The project is called DailyForge, built by u/luxie47. Instead of rewriting your prompt and handing it back, it analyzes what’s structurally wrong: intent clarity, constraint definition, output spec. Then it fixes things deliberately, step by step. Think of it like a linter for your prompts. If you’ve written code, you know that feeling when ESLint or a compiler flags a bug you walked past a hundred times. DailyForge does that for natural language instructions. It catches the things you assume are obvious but aren’t obvious to the model at all, like when you say “be concise” but never specify what concise actually means in context, or when you describe the task but forget to describe what the output should look like. These are the small structural gaps you’ve been papering over by regenerating until something decent comes out. Now you can see them clearly and fix them once. It’s closer to having a senior prompt engineer audit your work than any rewrite or autocomplete feature you’ve tried before.
The twist nobody’s talking about yet: it converts GPT and Gemini prompts into Claude-optimized ones. If you’ve been copy-pasting prompts across AI platforms and wondering why they underperform in Claude, that’s your answer. Claude processes instructions differently than GPT-4 or Gemini. It responds better to explicit role framing, layered constraints, and clear output specs. A prompt that gets a great result in ChatGPT often produces a mediocre one in Claude not because Claude is worse at the task, but because the instruction style doesn’t match how Claude’s training shaped its expectations. Most people blame the model. The real issue is usually the prompt. DailyForge closes that gap by translating the actual logic of the prompt, not just swapping model names in the header and hoping it works.
How to use it:
- 🔧 Install DailyForge as a Claude skill (GitHub link below). Setup takes about two minutes and requires no API keys beyond your existing Claude access.
- Run
/dailyforge [your rough idea or failing prompt]. Throw in something half-formed if that’s what you have. Rough inputs reveal structural problems that polished inputs hide, and DailyForge handles both equally well. - It walks through intent, structure, constraints, and output quality. Each diagnostic pass is visible, so you can see exactly what it’s flagging and why. This alone is worth the install. Watching the analysis trains your eye to catch the same issues before you even run a prompt next time.
- 📊 Get a score from 1 to 10 with specific feedback on what’s weak. Not vague feedback like “improve clarity” but specific feedback: which part of the intent is ambiguous, which constraint is missing, what output format would reduce hallucinations and increase consistency across runs.
- Pick from 3 generated variations or take the polished final prompt. Three variations give you options based on your specific use case. One might lean more concise, another more rigidly structured. You’re not locked into a single interpretation of your original idea.
Pro tip: Run the scoring mode on prompts you already use regularly. You’ll find fixable issues you’ve been quietly working around. That system prompt you’ve been tweaking for months? Score it. The feedback will almost certainly point to one or two structural gaps that explain why it still produces inconsistent output on certain inputs. Also run it before building any multi-step workflow. A weak prompt at step one compounds into broken results by step four. Catching the problem early is dramatically cheaper than debugging a broken chain after you’ve already built around it. If you manage a team that uses AI, make DailyForge part of your prompt review process before anything ships to production.
Worth noting: The cross-platform conversion is the sleeper feature. Port a ChatGPT workflow to Claude properly instead of copy-pasting and hoping for the best. If your team has built a library of GPT prompts and is now experimenting with Claude, this is the migration tool you didn’t know you needed. You’re not rewriting from scratch. You’re converting with intent, one structural element at a time, with a score that tells you whether the conversion actually worked. The first time you run a converted prompt and see it outperform the original on Claude, it reframes how you think about why models respond differently to the same underlying task.
Open source and free: github.com/luxie47/DailyForge 🚀
I built a prompt engineering skill for Claude — debug, score, translate, and batch-build prompts with one command
by u/luxie47 in PromptEngineering