Fresh off GitHub this week: Bugscalpel, a system prompt built to strip the soft behavior out of AI debugging.
No pleasantries. No abstract edge-case bullet list stapled to the bottom of every response. Just a fix with guard clauses already written into the code. If you have ever pasted broken code into ChatGPT or Claude and gotten back a confident three-paragraph explanation of what might theoretically go wrong, followed by a fix that only partially addresses the actual problem, you already know exactly what Bugscalpel is trying to solve. The soft behavior is real, it is consistent across models, and it is largely a product of how most people prompt their debuggers in the first place. Bugscalpel is a structured alternative built around a simple idea: make the model do the dependency analysis before it writes a single line of new code.
Here is the twist.
Most debugging prompts hand the model broken code and say fix it. Bugscalpel makes the model evaluate cross-file dependency impacts before it touches a single line. Different behavior. Different output.
That distinction sounds minor until you hit a bug that lives in the interaction between a utility function and three different callers. You paste the broken function, vanilla ChatGPT fixes it in isolation, and you ship it. Then two other parts of the codebase break in ways that feel completely unrelated. That is not a Claude or GPT problem specifically. That is a prompting problem. The model was never told to look upstream. It did exactly what you asked: it fixed the function. Bugscalpel changes the assignment from the start. Before any fix lands, the model is prompted to map out what else depends on the code in question, what behavior those dependents expect, and whether the proposed fix holds up at those touchpoints. You get a fix that was stress-tested against the broader codebase context, not just the snippet you pasted.
How to use it:
- 🔍 Pull the system prompt from github.com/MedHbibHlel/bugscalpel. The README is short. Read it once so you understand the prompt’s internal structure and logic. It helps you write tighter, more useful symptom descriptions in step three.
- 📂 Load it into your AI tool or IDE of choice. It works in ChatGPT’s custom instructions, Claude’s system prompt field, Cursor’s rules, or any setup that lets you prepend a system context before the conversation starts. If your tool supports persistent system prompts across sessions, set it once and never touch it again.
- ⚙️ Drop in the buggy code with a quick symptom description. Be specific about what is actually failing, not what you think is causing it. “This function returns undefined when the input array has one element” is a better input than “something is broken with the array handling.” The more concrete the symptom, the more targeted the diagnosis.
- ✅ Get back a fix with edge-case guards already baked into the code, not listed below it. The guard clauses are inline. The explanation is concise. There is no separate speculation section at the end listing what else could theoretically go wrong someday.
Pro tip: Save this for bugs that touch multiple files. That is where vanilla prompts produce confident nonsense, and where Bugscalpel earns its name. Classic candidates: bugs involving a shared config object, a middleware function, or a base class that several components inherit from. Those are the scenarios where a fix that looks correct in isolation silently breaks something downstream. Also works well for authentication logic, shared state in React context, or anything that flows through a central API client. Run those through Bugscalpel before you commit. For simple single-function bugs where the scope is obvious, any decent prompt will do. Bugscalpel’s real value scales with complexity.
Also worth noting: It kills the AI warm-up speech too. You know, the two sentences before every actual answer. Gone. The “Great question, let me break this down for you” opener that adds zero information but eats three seconds of your reading time on every single query. The “there are several things to consider here” setup that could have just been the first consideration. Bugscalpel’s prompt skips straight to the diagnosis. That sounds like a minor quality-of-life thing until you realize how much friction those preamble patterns accumulate across a full day of debugging sessions. The time adds up. The cognitive load adds up too. When the tool gets out of your way, you actually use it more.
👉 Open-source and free: github.com/MedHbibHlel/bugscalpel
Bugscalpel—a zero-fluff system prompt for deterministic debugging.
by u/OnlyPoint7950 in PromptEngineering