Write LaTeX ML/AI review articles for arXiv using the IEEEtran template and verified BibTeX citations.
Use when the user wants a comprehensive literature survey on a topic; outputs a PDF survey with LaTeX source, 60+ real citations, figures…
面向中国 AI / ML 研究者,主投 Nature Machine Intelligence、次投 Nature Communications / Nature Computational Science、必要时冲刺 Nature 的论文写作技能(craft + taste…
Find academic papers across up to 7 sources (OpenAlex, Semantic Scholar, CrossRef, PubMed, arXiv, plus native-Chinese NSSD and yiigle)…
Three modes for CS-conference papers (CVPR/ICCV/ECCV, ACL/EMNLP/NAACL, ICLR/NeurIPS/ICML/AAAI): direct LaTeX edit, adversarial…
Improve academic paper writing quality for ML/CV/NLP-style papers with clear section structure, paragraph flow, and reviewer-facing…
Chinese-first research paper writing, revision, polishing, section drafting, rebuttal, peer-review response, and manuscript argument…
Stage 2 of the ts-paper suite: build a complete, real refs.bib where every entry has full metadata (authors, year, venue, pages, DOI) and…
Generate a high-quality deep-reading note for a single paper and write it into an Obsidian-style vault, with strong structure…
Generate a structured literature review outline from a topic and optional paper set. Use when the user wants a review plan or survey…
Converts an arxiv paper into a minimal, citation-anchored Python implementation, flagging all ambiguities and never inventing details not…
Guide users through writing a systematic literature review (SLR) following the PRISMA 2020 framework, with the 27-item checklist, flow…
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final…
Step 3 of the PaperOrchestra pipeline: discover candidate papers via web search, verify them through Semantic Scholar, cross-corroborate…
Searches and discovers academic papers across multiple sources (Semantic Scholar, arXiv, Tavily, Exa, AMiner, Google Scholar) with…
Finds and organizes ML, CV, NLP, and AI research papers based on textual descriptions and keywords, with a persistent memory bank…
Research agent skills on mcprush, filed under one heading so the shelf can be read in one pass. The kinds are workflow, expertise, output format, voice & style and guardrail, and the one a skill belongs to is the fastest way to guess what it will do to your prompt.
What to look at before installing one is what it costs in context and what it takes away: a guardrail that refuses an action and a voice that rewrites a sentence are both skills, and only one of them will stop the agent doing something. Every skill here says which kind it is, what it costs in context, what it needs beside it to work and the licence it is published under, next to the publisher it came from and whether that publisher has been verified. The filters on the left narrow it further and the sort at the top decides what comes first.
A skill is not connected to anything: there is no endpoint to authorise and nothing routed through the gateway. Installing one drops a folder of instructions into the client’s own skill folder, and uninstalling it is deleting that folder. The exact line for each client is on the skill’s own page, because it differs by client rather than by skill.