Инструменты

End-to-end academic paper plugin: deep research and literature review, writing, multi-perspective simulated peer review, revision and finalization, pausing for your approval at each stage, with built-in citation verification and integrity checks. Licensed CC BY-NC.

HR and compliance plugin with HR and legal advisor subagents plus employment contract and GDPR skills, for drafting hiring materials, onboarding, offboarding and performance policies, employment contracts, privacy policies, terms of service and DPAs. Mainly US and EU rules.

Codex-native version of Academic Research Skills: one skill chains topic selection and literature review, drafting, simulated peer review, revision guidance and experiment planning, with citation verification and integrity checks. For grad students and faculty. Needs Codex; CC BY-NC 4.0 license.

OpenAI's official Codex plugin with about 50 skills that route questions to genetics, omics, pathway, protein structure, chemistry and clinical evidence sources, can split the work across parallel subagents, and synthesize sourced conclusions.

Two-stage deep research: drafts an editable outline for the topic, then parallel subagents search the web item by item and compile a Markdown report. Good for literature reviews, competitor research and due diligence. Install by copying files per the README, plus pip install pyyaml.

Translates entire books: splits PDF, DOCX or EPUB into chunks for parallel subagents, keeps terminology consistent, resumes interrupted runs and verifies with hashes, then outputs HTML, DOCX, EPUB or PDF. Needs Calibre, Pandoc and Python.

Econometrician Pedro Sant'Anna's academic Claude Code setup (fork the repo to use it): Beamer/Quarto lecture slides, paper review and mock refereeing, R/Python analysis and replication packages, with specialist subagents and quality gates. Needs Git and Python; slides need XeLaTeX and Quarto.

A research plugin for Claude Code: commands like /omp:survey, /omp:experiment and /omp:write chain literature review, topic selection, experiments, writing and self-review, with 5 specialized agents and 34 research skills that remember project progress across sessions. Experiments lean toward ML.