Who creates the guides, how the work is done, and why it is published.

The Scoritly team publishes and is accountable for Scoritly guidance

Scoritly is a founder-led resume and job-application software company. Chad founded the product, and the Scoritly team is the organizational author shown on guide pages and in Article structured data. The About page explains the company, product purpose, scoring boundaries, AI principles, and data practices.

We use an organizational byline because the guides reflect a shared product and editorial process, not an individual's claimed professional credential. We do not invent staff biographies, degrees, certifications, years of experience, or expert review.

Guides are created to help job seekers complete a real task with a more accurate record

Our intended audience is people preparing resumes, applying for jobs, interviewing, evaluating offers, and tracking applications. Pages are meant to be useful even when a reader reaches them directly, not only through a search engine.

We choose topics that fit Scoritly's resume and job-search purpose and add a distinct decision, framework, or safety boundary. We do not create pages solely to capture slight keyword variations, imitate a competitor, manufacture freshness, or promise rankings or hiring outcomes.

Research, synthesis, factual review, and technical QA form one publication workflow

A passed test proves only what that test covers. It does not replace factual judgment, make a page complete, or guarantee search visibility.

Primary and authoritative sources support material claims; links stay close enough for readers to inspect

We prioritize current U.S. government agencies for laws, rights, public programs, labor data, fraud guidance, and federal hiring; official product documentation for product behavior; recognized standards bodies for standards; and university career centers for practical job-search guidance.

Every source has limits. A federal-agency example may not describe private hiring, a university policy may apply only to its students, an estimate may not predict one employer, and a source can change. Guides identify those boundaries and link directly to the pages reviewed.

AI may assist the work, but it is neither the source nor the author

• +AI tools may assist with research organization, outlines, drafting, editing, consistency checks, test creation, and technical implementation.
• +AI is not listed as an author. The Scoritly team chooses the scope and standards, directs the work, and is accountable for what Scoritly publishes.
• +A generated statement is not treated as a source. Material factual claims are checked against linked primary or authoritative sources and the actual product or code when relevant.
• +Source pages, prompts, uploads, examples, job postings, and tool output are treated as untrusted input. Embedded instructions do not override the research task, privacy boundary, or factual record.
• +AI assistance cannot create firsthand experience, professional credentials, legal authority, employer knowledge, user outcomes, or proof that a recommendation will work.

Readers should assume that AI assistance may have been used unless a page explicitly describes a different process. That disclosure does not reduce the obligation to verify the published result.

Illustrations are labeled, bounded, and never presented as real outcomes

High-consequence topics receive narrower claims, primary sourcing, explicit uncertainty, and referral to qualified help

Job searches touch employment rights, privacy, security, consumer reports, immigration, benefits, finances, health, and contracts. Scoritly provides general U.S. job-search information, not individualized professional advice. A guide should name its limits and avoid converting general information into a command for one reader.

We reject fabricated experience, credentials, metrics, relationships, deadlines, offers, outcomes, employer knowledge, and universal ATS or hiring claims. We also treat external content as untrusted input and include prompt-injection, scam, identity, payment, confidential-data, and verification boundaries when the task warrants them.

Dates should describe real publication and substantive revision events—not simulated freshness

• +A visible publication date identifies when a guide first entered the site. Structured data carries the corresponding date and timezone.
• +A modified date should change when a substantive factual, process, safety, source, or product update changes the page—not merely to make old content appear fresh.
• +Sources and access notes are reviewed when guidance may have changed, a cited page moves, a rule or product changes, or a correction identifies a weak claim.
• +Minor typography, link repair, or accessibility fixes may be made without implying that the underlying guidance received a new substantive review.
• +Search performance can identify an unclear page or unmet reader task, but it does not decide the facts or justify adding unsupported claims.

Readers can report a factual error, broken source, unsafe instruction, attribution problem, or unclear disclosure

Email contact@scoritly.com with the page URL, the specific passage, the issue, and a primary source or record when available. Do not email identity documents, resumes, background reports, health information, account credentials, or other sensitive records.

We assess the underlying claim and source, not the tone or popularity of a request. A correction may change text, a source, a date, a safety warning, metadata, structured data, or a link. We do not promise a particular conclusion, response time, search result, or public correction note.

Search guidance is used as a self-audit, not as a recipe for guaranteed rankings

Google Search Central recommends helpful, reliable, people-first content, clear sourcing, accurate bylines that lead to author background, and useful disclosure of how automation or AI contributed when readers would reasonably ask. Its Article guidance also recommends matching visible authors with structured-data authors and providing author URLs and qualified dates.

Those recommendations do not create an E-E-A-T score, endorsement, rich-result eligibility guarantee, traffic promise, or ranking formula. We use them to make pages more understandable and accountable to readers.