Check that quotations resolve in the sources they cite, and keep a library of the ones that do
5 on GitHubby Elliot Towerv0.7.0MITupdated yesterday
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Plugin citations from marketplace reproducible-science in elliottower/reproducible-science. Commands read from the repo's manifest — yesterday.
This plugin runs code on your machine — its hooks fire automatically on events like tool use. The exact commands are listed below; read them before installing.
What it installs
Skills (1)
citations
Verify that quotations resolve in the sources they cite, and manage a library of verified quotations. Use before quoting a paper, when adding a citation, when asked to check whether a quote is real, when a bibliography needs DOIs backfilled, or when checking whether the authors and years in a refere
Slash commands (1)
/citations:citations-check
Do the quotations in this project resolve in the sources they cite?
Freeze a plan before running, seal a run's inputs, bind every number and quotation in a manuscript to what backs it, and check that each still resolves before submitting. Bundles prereg, citations, results and repro.
Holds a Claude Code research session to its records: refuses an edit to a frozen plan, says when a manuscript or a run has no ledger behind it, and shows what the checks report.
You.com agent skills for web search, research with citations, and content extraction. Guided integrations for Vercel AI SDK, Claude Agent SDK, OpenAI Agents SDK, crewAI, LangChain, Microsoft Teams.ai, direct REST API, and bash CLI.
HADS (Human-AI Document Standard) — semantic tagging convention for docs that work for both humans and AI models — plus the grounded-vault pattern for provenance-linked, drift-checked knowledge stores.
Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.
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