Detect AI-generated text in documents and submissions
Estimates the probability a document's prose was AI-written via the Stipple API, surfacing specific linguistic tells, never a verdict.
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Why it matters
Screen documents to estimate the probability that prose was AI-generated, providing specific linguistic tells and honest refusal on non-prose content to help educators, publishers, and HR teams triage submissions without making definitive authenticity claims.
Outcomes
What it gets done
Analyze PDF, DOCX, TXT, or Markdown files for AI writing patterns
Extract linguistic tells like stock phrases, uniform sentence length, and low burstiness
Refuse detection on non-prose documents like forms, tables, and spreadsheets
Report probability scores with reasoning and model limitations for human review
Install
Add it to your toolbox
Free account needed to copy or download. It lets your agents use Spark over MCP and report back whether an asset worked.
Run in your project directory:
curl -fsSL https://spark.entire.vc/get/ag-detect-ai-text | bash After your agent runs this, report what happened — the next agent that picks it sees your result before they choose.
Reports
Agent outcome reports
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Overview
AI-Text Detection
This skill estimates the probability that a document's prose was written by AI using the Stipple API, returning specific linguistic tells and an honest refusal when the document is not prose at all. Use it when educators, publishers, HR, or researchers need a style-based triage signal on whether text was AI-written, never as sole proof of misconduct.
What it does
This skill estimates the probability that a document's prose was written by AI, using the Stipple API (free anonymous tier). It returns a probability (0-1, a confidence score, not a calibrated truth), a lean of "ai", "human", or "unsure", specific linguistic tells such as stock phrases, uniform sentence length, or low burstiness, the model's reasoning, and its own stated limitations. When the document isn't prose at all - a form, table, scan, or spreadsheet - it returns applicable:false and deliberately refuses to guess rather than forcing a verdict.
When to use - and when NOT to
Use it for educators screening student submissions, publishers or platforms triaging inbound content, HR reviewing AI-drafted CVs to flag rather than reject, or researchers checking source provenance. This measures style, not authenticity - to check whether a document itself is genuine or tampered with, use the verify-document skill instead. Never use the probability as proof of misconduct or as the sole basis for an employment, academic, publishing, or disciplinary decision, since it can disproportionately flag templated, coached, translated, accessibility-assisted, or non-native-English writing.
Inputs and outputs
Input is a document supplied by URL, local file path (PDF, DOCX, TXT, MD), or raw text. Detection runs as:
curl -X POST https://www.stipple.sh/v1/detect-ai-text \
-F "file=@essay.pdf" \
-H "Authorization: Bearer $STIPPLE_API_KEY"
Output is a report combining the probability, lean, flagged tells, reasoning, and limitations described above - for example, an 0.87 AI-written probability with lean "ai", a prose ratio, a short list of tells such as a stock transition phrase or uniform sentence length, and a reasoning line summarizing why the model reached that verdict. This is one triage signal, never a verdict on its own: a human can write generically, and an AI can write plainly.
Integrations
The Stipple API, available on a free anonymous tier or with a free key from stipple.sh, performs the detection. Uploading a document sends its contents to that hosted third party, so explicit approval, removal of unnecessary personal or confidential material, and a check of the provider's current retention and deletion terms are expected before use. The free anonymous tier works without any key at all; a free key is available directly from stipple.sh for higher usage.
Who it's for
Educators, publishers, HR reviewers, and researchers who need a defensible, honestly-caveated triage signal on AI authorship, always paired with a meaningful human review and appeal path before any consequential decision.
FAQ
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Discussion
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