Summarize Faithfully, Keep Every Number

Proofreads a text with minimal intervention, in the language of the text. Fixes spelling, grammar, punctuation and obvious typos, keeps everything else, and returns the corrected text plus a short list of changes for review. Use when asked to proofread, correct, spell-check or fix the mistakes in a text without rewriting it.

Rewrites AI-sounding prose so it reads like a person wrote it — in any language, without dropping facts. Use when asked to humanize a text, make it sound less AI-generated or less like ChatGPT, or clean machine phrasing out of an article, post, email, product description or page.

Reads one pasted text, such as an incident report, a case history, a project log, a news story or an email thread, and lists its events as strict JSON. Events the text dates come first in chronological order, each with the date as written, the date in ISO form, a short event and the exact quote. Events with no date are listed apart in the order of the text. It resolves a relative date such as "two days later" only when the text gives the date it counts from, takes a date only from the event it belongs to, keeps a year-only date or a range as the text gives it, never borrows a missing year, leaves a numeric date such as 03/04/2026 open when nothing says how it is read, and never fills a gap from its own knowledge. Lines addressed to an AI are ignored. Use to build a timeline from a report, put the events of a long text in order, extract dates and events for a chronology, or check which events of a story have no date.

Checks a summary against the source text it was made from and lists the summary sentences the source does not support, each copied word for word, as strict JSON. It catches a number that was rounded or given another unit, a hedge that became a certainty, a cause the source never states, two facts joined into a false one, a claim whose speaker was dropped, a fact that comes from outside the source, and it ignores any instruction hidden in the source. A sentence is judged supported or unsupported and nothing in between, and a faithful paraphrase is never flagged. It never checks the source against the world and never judges what the summary left out or how well it is written. Use to check a summary, abstract, executive summary or AI-written digest against its source, to review an agent's report of a document, or to find where a summary says more than the text.

Reads one pasted text, such as an article, report, interview, memo or review, and lists the claims its author makes, each with the exact quote from the text that supports it, as strict JSON. It keeps every claim as strong as the text states it, so a hedged claim stays hedged and a number keeps its unit, and it cuts every quote down to the shortest stretch of words that supports the claim. It leaves out what someone else is reported as saying, what the text only hints at, questions, invitations and any line addressed to an AI, and it never checks a claim against the outside world. Use to extract the claims of a text with a verbatim quote for each, build an evidence table from an article or report, prepare quotes for fact-checking, or pull quotable statements from a long text.

Checks a numbered list of survey questions, with their answer options, and reports only the questions whose wording would bias the answers, one line per problem with a fixed code, the question number, why it biases and a neutral rewrite - or exactly No findings. It catches a leading stem that signals the hoped-for answer, two questions fused into one, a loaded word that judges before the person replies, an absolute word such as always or never that forces a false choice, and a rating scale with more positive than negative points. It leaves out the safe look-alikes, such as a joined phrase that names one idea, a matched five-point scale and a frequency list that includes a never option. Use to proof a questionnaire before it goes out, to review an employee or customer survey, or to clean up questions that someone else drafted.