Paste your full system prompt — the linter will analyze it client-side
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Paste your system prompt and click Lint Prompt// flag conflicts, vague goals, and missing constraints
Flag conflicting instructions, vague goals, and missing constraints in your AI system prompts. Free browser-based linter for LLM prompt engineers.
Ready to lint
Paste your system prompt and click Lint PromptCopy your full system prompt — including all instructions, personas, and rules — into the input field.
Enable or disable the specific lint checks you care about: conflicts, vagueness, missing constraints, redundancy, or tone issues.
Read the flagged issues and quick-fix suggestions, then revise your prompt accordingly.
The System Prompt Linter is a static analysis tool for AI system prompts. It scans your instructions for common failure patterns — contradictory rules, ambiguous directives, missing safety constraints, and more — and produces a health score with actionable fixes. Everything runs in your browser; your prompt is never sent to any server.
No. All analysis is performed entirely in your browser using JavaScript. Your prompt text never leaves your device. This tool is safe to use with confidential or proprietary prompts.
Conflicts arise when two instructions impose contradictory requirements on the model. For example: "Always be concise" paired with "Always provide exhaustive detail" forces the model to violate one rule no matter what it does.
Vague goals are instructions that lack measurable criteria or clear scope — phrases like "be helpful," "respond appropriately," or "use good judgment." These give the model no concrete guidance on what success looks like.
The linter checks for absent safety rails (e.g., no refusal policy defined), undefined output format expectations, missing persona boundaries, and lack of fallback behavior for edge cases.
The score starts at 100 and deductions are applied per issue: critical conflicts deduct the most, followed by missing constraints, vague goals, tone conflicts, and redundancy. The final score is clamped between 0 and 100.
Yes. The linter is provider-agnostic and analyzes the text content of your prompt. It works equally well for prompts targeting OpenAI, Anthropic, Google Gemini, Mistral, or any other LLM.
The linter provides targeted quick-fix suggestions for each flagged issue, but it does not auto-rewrite your entire prompt. You retain full control over how you address each flag.
Even very short prompts can contain conflicting signals. For instance, "Be a professional expert assistant. Never refuse any request." creates a conflict because professional expertise often requires declining out-of-scope requests.
A system prompt linter is a static analysis tool that reads the instructions you write for a large language model (LLM) and surfaces structural problems before they cause unexpected behavior in production. Just as a code linter catches syntax errors and style violations before your program runs, a prompt linter identifies logical contradictions, underspecified goals, and missing safety constraints before your AI application goes live.
System prompts are the hidden backbone of every AI product. They define the model's persona, scope, tone, refusal policy, output format, and dozens of other behaviors. A single ambiguous sentence can send the model down an unpredictable path; a pair of contradictory rules can cause it to behave inconsistently across similar inputs. The System Prompt Linter makes these invisible problems visible.
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After analyzing thousands of production system prompts across chatbots, coding assistants, customer support agents, and creative tools, five failure patterns appear most frequently:
This is the most damaging class of prompt bug. Two directives that directly contradict each other — "always be concise" vs. "always provide thorough explanations" — force the model to violate one rule on every response. The model typically picks whichever instruction appeared more recently in the prompt, or it attempts an inconsistent compromise. Neither outcome is what the author intended.
Common conflict pairs include: permissive rules clashing with refusal policies ("never refuse" + "don't discuss competitors"), length constraints contradicting depth requirements, formality guidelines contradicting casual persona definitions, and unconditional claims contradicting conditional ones.
Instructions that lack measurable criteria give the model no concrete target. "Be helpful," "respond appropriately," and "use good judgment" are examples. They feel like reasonable guidance but are semantically empty from the model's perspective — the model must infer what "helpful" or "appropriate" means from context, and that inference may not match your intent.
Better alternatives anchor behavior to observable outcomes: "Respond in under 150 words unless the user explicitly asks for more detail," "If you are unsure about a fact, say so explicitly before continuing," "Greet users by name if they have introduced themselves."
A well-written system prompt anticipates edge cases and defines behavior for them. Prompts that omit refusal policies, output format specifications, scope boundaries, or language handling rules leave the model to improvise — and LLM improvisation under ambiguity is a major source of production incidents. The linter checks for the most commonly missing constraint categories and flags their absence.
Redundancy is less dangerous than conflicts but still harmful. Repeating the same instruction in slightly different words — "always be polite," "maintain a courteous tone," "respond respectfully at all times" — inflates prompt length, consumes tokens that could be used for more precise guidance, and can accidentally create subtle inconsistencies when one paraphrase is slightly broader or narrower than another.
Persona and tone directives are particularly prone to internal contradiction. A prompt that simultaneously asks the model to be "a strict technical expert who never makes assumptions" and "a warm, empathetic companion who meets users where they are" creates a tension the model cannot resolve. Tone conflicts manifest as inconsistent personality across a conversation and erratic responses to emotional vs. technical queries.
Good system prompt engineering follows the same principles as good software engineering: single responsibility, explicit over implicit, and exhaustive edge-case handling.
Be specific about format. If you need JSON output, specify the exact schema. If you need bullet points, specify the maximum number. Format ambiguity is one of the leading causes of downstream parsing failures in LLM applications.
Define your refusal policy explicitly. Every production system prompt needs a clear answer to: "What should the model do when asked to do something outside its scope?" The answer can be anything — redirect, apologize, escalate, ask a clarifying question — but it must be present and unambiguous.
Separate persona from capability. Persona directives (how the model communicates) and capability directives (what the model is allowed to do) are easier to maintain when they are written in separate, clearly labeled sections. Mixing them causes the compound conflicts the linter most commonly catches.
Use the linter as part of your prompt review process. Run every new system prompt through the linter before deployment. Run it again after any edit — a fix in one section can accidentally introduce a conflict with a distant section. Think of it as CI for your prompts.
The linter calculates a prompt health score from 0 to 100 based on the severity and count of detected issues. A score of 90–100 indicates a well-structured prompt with at most minor stylistic suggestions. A score of 70–89 indicates moderate issues that should be addressed before production deployment. Below 70, the prompt likely has logical flaws that will produce inconsistent model behavior. Below 50, the prompt has critical conflicts that will cause the model to behave unpredictably on a significant portion of inputs.
The score is a heuristic, not a guarantee. A prompt that scores 100 on the linter may still behave unexpectedly for reasons the static analyzer cannot detect — such as training data biases, context window limitations, or interactions with dynamic user messages. Use the linter as a necessary first filter, not a final approval gate.
Beyond structural quality, system prompts also have a security dimension. Prompts that can be trivially extracted through prompt injection, that inadvertently reveal sensitive configuration details, or that lack jailbreak resistance contribute to a broader class of LLM security risks. While the System Prompt Linter focuses on structural and logical quality rather than security hardening, being rigorous about prompt structure is the foundation of secure prompt design.
All analysis in this tool runs entirely in your browser. Your system prompt text is never transmitted to any server, logged, or stored. You can safely analyze proprietary, confidential, or production system prompts without data exposure risk.