Audit answer-first definitions and explanations
Find sentences that can stand on their own when lifted from a page, then check whether the subject, answer, entity, and supporting detail remain clear.
Free, no signup. Find the sentences that can survive being lifted out of your page: direct, self-contained, specific answers with enough information density to cite.
| Signal | Working interpretation |
|---|---|
| Flesch Reading Ease | 60–70 is commonly considered plain English |
| Grade formulas | Use as audience estimates, not requirements |
| Quotability | A sentence-level writing heuristic; not a rank prediction |
Pasted content is analyzed in your browser. A URL is sent only to the protected fetch endpoint; paste content takes priority. After a successful URL check, only its domain is remembered locally.
Runs entirely in your browser — nothing you paste is uploaded or stored. Pasted text stays local; only an optional URL is fetched. Anonymous run-level outcome counters may be used for aggregate research; URLs, domains, IPs, and identifiers are never included, and no statistic is released below 100 runs.
Saved targets, named lists, and recent check summaries remain only in this browser.
The interactive canonical-tag example contains 48 words and three sentences. The current engine returns:
Readability for the full example is Flesch 39, Flesch–Kincaid grade 11.8, with the audience label “High-school to early college reader.” These are formula outputs, not quality verdicts.
Quotability is the weighted total of six visible factors. Density compares concrete-information cues with sentence length. The readability summary reports six established formulas and maps Flesch–Kincaid grade to a broad audience label. A high result identifies a review candidate; it cannot establish accuracy, originality, retrieval, or citation likelihood.
The extractor converts pasted text or HTML into content blocks, joins their text, and splits it into sentences. The scorer applies fixed weights: self-containment 25%, direct answer 25%, entity clarity 15%, structure 15%, specificity 10%, and lead position 10%. Separate readability formulas use local counts of words, sentences, syllables, letters, and polysyllabic words.
No. The scorer is a transparent writing heuristic. It highlights sentences that stand alone and carry useful detail; it does not predict a particular model, citationAn AI citation is the visible source link an AI answer engine shows next to its generated text — the clickable reference that credits the web page it used. A citation's presence is a separate thing from whether the cited page actually supports the statement, and from being retrieved (read behind the scenes) or merely mentioned (named without a link); citation is driven more by brand mentions and being retrievable than by traditional ranking., or ranking.
No. This tool analyzes only the text or HTML you paste, entirely in your browser.
The fixed factors reward a self-contained subject, direct-answer language, a clear entity, useful sentence structure, concrete specificity, and appearing early in the supplied passage.
The factors use a few discrete values and fixed weights, so different sentences can land on the same total. Read the factor breakdown and sentence itself instead of treating rank order as precise.
They are established formula outputs based on sentence length, word length, syllables, or letters. They are audience estimates, not requirements and not AI retrieval signals.
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Where this tool helps
Find sentences that can stand on their own when lifted from a page, then check whether the subject, answer, entity, and supporting detail remain clear.
Compare sentence-level scores and factor breakdowns so editors can start with passages that lack self-containment, directness, specificity, or useful structure.
Use six familiar readability formulas and the broad audience estimate as discussion inputs without treating them as requirements or AI retrieval signals.
Analyze a page section, article draft, or HTML fragment in the browser; only the optional URL-fetch workflow sends a request.
Download the ranked sentences, density values, and visible factors as CSV so factual review and rewriting can continue in a shared workflow.
Watch the full workflow
This tool helps you find clear, self-contained sentences that are easier for a reader or answer engine to reuse. I will run the built-in example, explain the readability summary and every score, show the editing prompt and CSV export, and clarify what the tool cannot prove.
A quotable sentence can be understood without the paragraph around it. It clearly names the subject, gives a direct answer, and includes enough useful detail to stand alone. This tool finds sentences with those writing qualities. It does not know whether a search engine will rank, retrieve, or cite them.
Use it when reviewing definitions, answer-first paragraphs, help content, research summaries, or any section that should make sense when quoted. It can also help an editor choose which sentences to rewrite first and export the candidates into a review worksheet.
Paste plain text or HTML into the large field. Pasted content stays in your browser. The URL field is optional and sends that URL to a protected fetch service, so paste mode is the simplest private workflow. Then select Find quotable sentences, or use Try an example to learn the result format.
Select Try an example. The tool inserts three sentences about canonical tags, extracts the text, calculates six readability formulas, and ranks each sentence. The example always contains forty-eight words and three sentences, so you can reproduce the same output while learning how the tool works.
The summary starts with word count, sentence count, and a broad audience estimate. Flesch, Flesch-Kincaid, Fog, SMOG, Coleman-Liau, and A R I are established readability formulas. They use sentence length, word length, syllables, letters, or complex-word counts. They estimate reading difficulty; they do not measure accuracy or citation value.
The first definition scores ninety-three for quotability and one hundred for information density. It names a canonical tag, defines what it is, and explains what it identifies. That makes it a strong sentence to review or reuse, but the score has not checked whether the claim is correct, original, or supported by evidence.
The small line exposes all six factors. Self-contained asks whether the sentence can stand alone. Direct asks whether it answers clearly. Entity checks whether the subject is clear. Structure rewards readable construction. Specific rewards concrete detail. Lead rewards early placement. These visible factors matter more than the total alone.
The second sentence also scores ninety-three even though it says something different. Fixed weights and a small set of factor values can create ties. Read the actual sentence and factor line instead of assuming the first row is meaningfully better. Preserve important nuance, such as the difference between a hint and a directive.
The third sentence scores sixty-six. Its direct-answer and structure factors are lower because it gives an instruction without much explanation. A lower score is a review prompt, not an order to make the sentence longer. Add a clear subject or missing detail only when the sentence truly needs to stand alone.
A useful workflow is to identify the weakest relevant factor, edit the original passage for clarity and factual completeness, and run it again. Do not chase a perfect total by deleting qualifications or adding unsupported numbers. The sentence still needs to be accurate and helpful in its real page context.
Copy Sharpen prompt creates an editing instruction around the exact sentence and copies it to your clipboard. You can use that prompt with a writer or editing assistant, but treat the rewritten sentence as a draft. Review facts, sources, and nuance before replacing anything on the page.
Download CSV saves every candidate with its quotability label, density, sentence, and visible factor line. In a shared worksheet, add columns for the proposed revision, factual-review status, evidence, owner, and final decision. That turns the tool output into an editorial queue instead of a list of unexplained scores.
The method converts pasted text or HTML into content blocks, joins the visible text, and splits it into sentences. Local rules calculate the six quotability factors. Separate formulas count words, sentences, syllables, letters, and longer words for readability. The system uses surface patterns, not semantic understanding.
The rules are designed for English and can misread names, abbreviations, fragments, and specialist terms. Specificity rewards visible cues such as numbers but does not fact-check them. No answer engine, retrieval system, ranking system, or citation database is tested. Use the tool for writing review, then verify claims and real citation performance separately.
Start with a sentence whose weakest factor matches a real writing problem. Revise the original passage, keep any necessary context, rerun the analysis, and fact-check every claim. Use actual search or citation evidence for retrieval questions because this local score does not test an answer engine.