Merge pull request #14052 from peopleworks/add-signs-of-ai

Add peopleworks/SignsofAI — explainable, bilingual (EN/ES) AI-writing detection + originality checking 🤖🤖🤖

README.md

@@ -1883,6 +1883,8 @@ MCP servers for learning management systems (LMS) and educational tools.
 - [anzy-renlab-ai/pronounce](https://github.com/anzy-renlab-ai/pronounce) [![pronounce MCP server](https://glama.ai/mcp/servers/anzy-renlab-ai/pronounce/badges/score.svg)](https://glama.ai/mcp/servers/anzy-renlab-ai/pronounce) 🐍 🏠 🍎 🪟 🐧 - Community pronunciation dictionary for developer project / product / jargon names (kubectl, nginx, GIF, JSON, Pydantic, Knative, LaTeX — 1,848 entries). Each entry carries IPA, a General-American respelling, alternate readings where the pronunciation is contested, a source citation (creator interview / project FAQ / Wikipedia § Pronunciation), and a confidence level. Tools: `pronounce(word)`, `search_pronunciations(query)`, `list_pronunciations(category)`. Install: `uvx pronounce-mcp`. Live browse: <https://pronounce.renlab.ai>.
 - [sf-shenfeng/learn-shell](https://github.com/sf-shenfeng/learn-shell) [![sf-shenfeng/learn-shell MCP server](https://glama.ai/mcp/servers/sf-shenfeng/learn-shell/badges/score.svg)](https://glama.ai/mcp/servers/sf-shenfeng/learn-shell) 📇 🏠 - Agent-native teaching OS: gives your AI a classroom that remembers. 50 tools for courses, lessons, FSRS flashcards, graded exercises, real-time teaching sessions, and teaching-memory read-back (`get_context`) — the agent authors and grades, reflects with evidence, and revises its own lessons; lesson completion belongs to the learner. Self-hosted (Postgres + web app), MIT.
 
+- [peopleworks/SignsofAI](https://github.com/peopleworks/SignsofAI/tree/main/src/SignsOfAI.Mcp) [![peopleworks/SignsofAI MCP server](https://glama.ai/mcp/servers/peopleworks/SignsofAI/badges/score.svg)](https://glama.ai/mcp/servers/peopleworks/SignsofAI) #️⃣ 🏠 🍎 🪟 🐧 - Explainable AI-writing detection and originality checking for teachers, students and writers, in English **and Spanish**. `analyze_ai_writing` returns a 0–100 score *and every tell behind it* — overused vocabulary, rhetorical crutches, syntactic patterns and sentence-rhythm burstiness — each with the matched text and a concrete fix, instead of a black-box percentage. `check_originality` returns the shared passages between documents as evidence; `check_citations` cross-checks a bibliography against the text offline; `compare_to_baseline` runs Burrows's Delta against the same writer's earlier work; `inspect_characters` finds hidden and lookalike characters; `write_report` exports the evidence as a document a person can take to a committee. Every tool but two runs entirely on the machine and the text never leaves it; the two server-backed ones (perplexity, cross-language paraphrase) disclose that they send text. Published false-positive rate at its 30/100 boundary: 2 of 296 texts written before 2022 (0.7%, 95% interval 0.2–2.4%). .NET 10, MIT. Install: `dnx SignsOfAI.Mcp --yes`. Browser demo: <https://peopleworks.github.io/SignsofAI/>
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 ### 🛒 <a name="e-commerce"></a>E-Commerce
 
 MCP servers for e-commerce platforms and online store management.