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Say 'before 2022', and stop the entry counting its own tools

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Two corrections to the pending entry, both mine.

The corpus line said the texts were written 'before generative models were in
use'. GPT-3's API was in beta from mid-2020, so that is not true of a 2021
revision; the claim that holds is 'before 2022', which is weaker and correct.
It is the wording the project itself uses in Docs/CALIBRATION.md.

And the entry counted tools -- 'ten in all', 'eight of the ten'. An entry that
enumerates goes stale the next time one ships; one that characterises does not.
This now says 'every tool but two runs entirely on the machine', which stays
true without anybody editing it.

Also adds the install command, since the package is on NuGet.

Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_015PEbbiYSNPw7jE3LrPNhyF
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Pedro Hernández
2026-09-09 19:37:08 -04:00
co-authored by Claude Opus 5
parent 8e1e5817c2
commit b87d9234f9
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@@ -1829,7 +1829,7 @@ 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. Ten tools in all: `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. Eight of the ten run entirely on the machine and the text never leaves it; two optional ones (perplexity, cross-language paraphrase) call a server and say so. Published false-positive rate at its 30/100 boundary: 2 of 296 texts written before generative models were in use (0.7%, 95% interval 0.2–2.4%). .NET 10, MIT. Browser demo: <https://peopleworks.github.io/SignsofAI/>
- [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/>
### 🛒 <a name="e-commerce"></a>E-Commerce