Anti Detection Writing

Anti-detection writing

Make AI-written prose easier and more natural to read while preserving its meaning. Improve flow, specificity, and tone for the intended audience. When detector testing is requested, work toward lower scores through useful edits and measure the outcome; never trade accuracy or readability for a score. Detector results do not establish human authorship or permission to publish.

Establish the purpose and facts

1. Save the original text. Identify its reader, purpose, format, and requested tone or length from the supplied context. For a discussion reply, also read the question and existing replies. For public participation, respect the destination's rules, explicit objections to AI-assisted work, and disclosure requirements.
2. Treat supplied facts as the starting point for a self-contained edit. Verify substantive factual corrections against relevant sources; for technical advice, inspect official documentation or implementation and record the version when it matters. Distinguish source inspection, local tests, and deployment verification. Flag unsupported claims instead of adding invented evidence.
3. Inventory the substantive claims, commands, code, conditions, uncertainty, and reported symptoms. Record each claim's time scope: a dated observation and a limitation that still applies are different facts. For each claim record whether it is preserved, corrected, or removed, and why. Identify what the reader needs to understand, decide, or do.

Write and review

• Separate the source inventory from the answer inventory. For a direct question, keep the conclusion, necessary support, and qualifications that change the reader's decision; leave other background in the linked evidence. Record scope cuts rather than carrying every source fact into the reply.
• Rebuild the text around the reader's purpose rather than retaining the original sentence order. Put the main point where the format calls for it, connect each sentence to the next, and keep the detail needed to understand or act on it. After moving evidence, check paragraph density: a paragraph that combines the intervention, results, screenshot explanation and limitations may need a break even when every sentence is accurate.
• For a procedure or causal explanation, map each action to its component, prerequisite, and limiting condition. Arrange the answer in that dependency order. Place a prerequisite before the action that needs it and keep an exception beside the claim it limits. Replace an ambiguous pronoun with the existing component name. Keep unspecified actors unnamed, including in intermediate fact tables: “All 180 articles have been copied” does not establish that a migration team copied them. Preserve passive voice when naming an actor would add an unsupported fact. For ordinary polishing, leave already clear passages intact. When the user explicitly requests a measured alternative, retain the original as a control and allow a different reader-appropriate framing that preserves the facts and practical meaning.
• Prefer concrete nouns and verbs. Remove generic reassurance, repeated summaries, rhetorical contrasts, and irrelevant background. Use lists only where they make steps or comparisons easier to follow. After factual repair, check for stiff noun phrases and overloaded steps; moving unchanged sentences may leave those problems intact.
• For vocabulary cleanup, use the phrase review list. Avoid stock phrases when they add no meaning; review matches in context rather than banning words or swapping synonyms mechanically. Preserve precise terminology, intended tone, uncertainty and protected spans. An absent phrase needs no edit, and a cleaner vocabulary is not evidence of a lower detector score.
• Preserve operational qualifications and exact code unless evidence supports a correction. Record deliberate scope cuts; do not call an answer fully claim-preserving when useful details were removed.
• Polish the explanatory message independently of its code. Keep code blocks and standalone commands fixed during prose experiments; assess their correctness separately, and record any necessary technical correction. Keep inline identifiers and values where they are part of an explanation. The full message, including its code, still needs a consistency review.
• Match the requested register. If an authentic writing sample is supplied, identify its register, paragraph openings, and transitions before drafting; preserve useful habits during cleanup without importing the sample's facts, opinions, or personal history. Vary sentence length when it improves pacing; do not force informality or strip useful structure. Never invent personal experience, tests, certainty, identity, quotations, typos, or invisible characters to make text appear human-written.
• A short, exact asker quotation can identify the concern being answered. Include it only when it helps the reader. Verify context and attribution; do not pad or rotate quotations to hunt for a detector score.
• Compare the finished answer directly with the claim inventory. Check who acts, negation, cause and effect, scope, certainty, numbers, units, and prerequisites. Keep an ongoing limitation current; changing “has not been validated” to “had not been validated at that point” narrows the claim unless the evidence supports that change. Match protected code, commands, identifiers, URLs, and exact quotations byte-for-byte unless a verified correction is recorded. Account for every cut or correction and remove unsupported additions. This writer self-check does not replace separate review.
• When a separate reviewer is requested or available, give them the reader's purpose, relevant context and sources, baseline, final text, and claim inventory without detector scores. Request a verdict on correctness, relevance, readability, tone, and necessary qualifications, tied to the final file's SHA-256. Resolve factual objections even if an incorrect version has a lower score. If no separate reviewer is available, say so; a self-check is not independent review.

Measure only when requested

Read measurement instructions before live scanning and evidence limits before interpreting results. Use the user's requested service, mode, and supported browser tools. This skill supplies no browser controls and does not authorize subscriptions, paid scans, public posting, or new account access.

When explaining detector mechanisms or planning another batch after unchanged results, read detector research.

When the user explicitly asks for an autoresearch workflow to improve this skill, read the bounded research workflow. It separates skill proposals, preference-based selection, quality review and measured confirmation; ordinary writing does not need this workflow.

For a comparison, select cases and candidates before seeing scores. Record fresh baseline and candidate scans using the same service, mode, and displayed model. Retain failures and unchanged results. Compare a fixed body with a relevant quotation separately from prose rewriting; track content and length changes. Distinguish first-pass trials from adaptive iterations. Record an unchanged candidate as a no-op. Comparing it with a separately rewritten baseline measures preservation versus that rewrite, not a benefit from edits the candidate did not make.

By default, the detector target is the message's prose. Declare prose_only before testing, exclude only independently identified code blocks or standalone command lines, and retain all explanatory prose, qualifications and inline identifiers. Save the full reviewed message, exact prose input and code-range mapping separately. Use the same scope for both arms; keep earlier whole-message scores under their original scope. An explicit whole-message request uses full_text instead.

Confirm the effective editor text, completed scan, fresh-result indicator, model, native result labels and metrics, timestamp, and warnings. Save exact inputs and private visible evidence. A stale displayed number is not a result for changed text. Never pad short prose, add code back merely to reach a detector minimum, or drop difficult explanatory passages to lower a score.

The optional checker in scripts/validate_receipt.py checks strict plain-text receipt consistency. Its receipt format rejects unrecorded editor changes; it does not support rendered-Markdown transformations or authenticate the service. A valid receipt can record 100% AI. Apply a numeric threshold only when the user explicitly requests one.

Publish and report

Honor existing user authorization; do not invent authorization or ask again for an already authorized action. For a public reply, refresh the discussion before posting and reassess if context changed. Match the full final body to the reviewed version and the declared detector input to its scan and requested threshold. A prose-only result describes the prose, not the complete message or its code. Under prose_only, changing only code requires renewed full-message review and an updated prose mapping; if the measured prose bytes stay identical, its receipt still describes those bytes. Under full_text, a code change also requires a new scan. Update the owned answer when editing, then independently read back its author, exact body, and URL. Do not post a duplicate or claim an answer was accepted without checking.

Report the actual quality findings, scores and warnings when measured, and edit or keep decisions. Missing required review or measurement leaves a candidate unpublished. A detector threshold applies only if requested. A successful local receipt check never authorizes publication.

For a showcase, include only quality-passing candidates with a measured decrease against a saved before-text using comparable completed scans. Link exact inputs, identify the intervention and tested version, and keep the full comparison denominator visible. Put unchanged, worse, rejected, unpaired, and unmeasured outcomes in a separate limitations section while retaining all records. A quotation effect is not a rewriting effect, and an untested skill revision is not an established improvement. If a case does not improve, document when the approach may not help or test a specific new edit within a bounded experiment.