Prompt Readability Checker
Diagnose a prompt before you send it: explainable scores for clarity, ambiguity, specificity, and structure, with observations that name where it is vague.
Answer the question directly: a clarity score plus the specific reasons a prompt reads as unclear — vague terms, unstated objective, missing specifics.
"Is my prompt clear?" usually gets answered by gut feel, then disproven by the output. This answers it with evidence: it loads a deliberately vague prompt — "make this better and more engaging, keep it nice and clean" — and shows exactly why it scores low. No stated objective, broad quality words, hedges, no concrete anchors. The diagnosis names each reason so the answer is not just "no" but "no, and here is where." It reports the problems; it does not fix them.
Paste the prompt in doubt
The one you are not sure reads clearly.
Get the score and reasons
Not just a number, but why it scores that way.
See where it is vague
Each unclear spot named, with no rewrite imposed.
You get an overall clarity score like 27/100 with a band such as Unclear, then four sub-scores on a 0 to 100 scale: Clarity, Ambiguity (higher is clearer), Specificity, and Structure. Specificity counts concrete anchors as 'N of 6' (number, length limit, format, audience, examples, criteria), and OBSERVATION NOTES name each hedge, vague quality term, and unstated objective behind the number.
No on both counts. The report states 'This is a read-only diagnosis — the prompt is not modified,' so it flags where clarity suffers but never rewrites a single word. A strong score is a clarity signal, not a guarantee the prompt performs well when you run it in ChatGPT or Claude. To produce a stronger version, use the Prompt Rewriter instead.
Act on each named reason yourself, then route to the right tool. If OBSERVATION NOTES report 6 vague quality terms (good, better, nice, engaging) or only '1 of 6' concrete anchors, use the Prompt Rewriter to strengthen it, the Prompt Cleaner to strip redundancy, or the Prompt Formatter to reshape structure. The diagnosis names the fixes; you decide and apply them.
Diagnose a prompt before you send it: explainable scores for clarity, ambiguity, specificity, and structure, with observations that name where it is vague.
Surface the ambiguity a model will exploit: vague quantities, hedges, undefined time, open-ended lists, and conflicting instructions — flagged, not fixed.
Words like "good", "engaging", and "professional" feel like instructions but specify nothing. Find them so you know where the prompt leaves things open.
A set of before-and-after examples showing exactly what prompt cleanup removes — and what it deliberately leaves alone.
'Make it good', 'be detailed', 'keep it interesting' — vague prompts get vague output. The fix is mechanical: replace every fuzzy word with a checkable instruction.
Formats fuzzy agent instructions into a structured prompt with objective, available tools, constraints, success criteria, and failure handling.
Diagnose a prompt's clarity before you send it — explainable scores for clarity, ambiguity, specificity, and structure. Read-only; it never rewrites.
Fix an unreliable prompt the methodical way instead of poking at it — find what's actually unclear, rewrite for specificity, cut the noise, then prove the new version beats the old one.
Design a system prompt that holds up in production — define the role precisely, engineer the behavior and guardrails on top of it, then check it reads clearly before you ship.