Research Claude Skills
2 269 research skills from 396 authors. They find sources and get you up to speed on unfamiliar ground. Half of them fit into 2 278 tokens or less — that is what one costs your context window when the agent loads it. 663 ship runnable scripts rather than instructions alone. 4 of them cannot work without an MCP server, most often rube. We also found 264 copies of these same skills sitting in other people's repositories — counted once here, not 264 times.
2 269 unique 396 authors 1 169 updated this month 93 from vendors
> Choose a model architecture for a medical-imaging research question before scaffolding. Maps the task (classification, segmentation, detection, transfer), modality and dimensionality, labelled-data scale, and class imbalance to a shortlist of architectures, each grounded in its source paper with a when-to-use, a medical-imaging use, a reference implementation, the typical validation setup, and the matching model-scaffold template. Covers the foundational curriculum (ResNet, DenseNet, EfficientNet, ViT, Swin; U-Net, 3-D U-Net, Attention/Residual U-Net, nnU-Net (+ ResEnc/MedNeXt/STU-Net), Mask R-CNN; SAM/MedSAM, nnInteractive/VISTA3D interactive-3D, TotalSegmentator, BiomedCLIP, DINO/MAE/SimCLR; and graph neural nets — GCN/GraphSAGE/GAT/GIN/BrainGNN — for brain connectomes). It teaches archetypes and the task-to-architecture logic (including the "scale the CNN, new≠better" rigour caveat), not a live SOTA leaderboard.
Check manuscript compliance with medical research reporting guidelines. Supports 47 guidelines including STROBE, STROBE-MR, RECORD, REMARK (prognostic tumor-marker studies), TARGET (target trial emulation), GATHER (burden-of-disease / health-estimate modeling), CONSORT, CONSORT-AI, STARD, STARD-AI, TRIPOD, TRIPOD+AI, TRIPOD-LLM, PGS-RS, ARRIVE, PRISMA, PRISMA-DTA, PRISMA-P, PRISMA-ScR (scoping reviews), CARE, SPIRIT, SPIRIT-AI, CLAIM, DECIDE-AI, MI-CLEAR-LLM, SQUIRE 2.0, CLEAR, MOOSE, GRRAS, SWiM, AMSTAR 2, CHEERS 2022, CROSS (survey studies), SRQR and COREQ (qualitative research), and risk of bias tools (QUADAS-2, QUADAS-C, RoB 2, ROBINS-I, ROBINS-E, ROBIS, ROB-ME, PROBAST, PROBAST+AI, NOS, COSMIN, RoB NMA). Generates item-by-item assessment with PRESENT/MISSING/PARTIAL status.
> Add a new journal to the MedSci Skills profile database. Extracts metadata from author guidelines, generates write-paper (detailed) and find-journal (compact) profiles in canonical format with quality gates.
> Interactive sample size calculator for medical research. Decision-tree guided test selection, reproducible R/Python code, effect size interpretation, and IRB-ready justification text. Supports diagnostic accuracy, agreement, proportions, continuous outcomes, survival, ANOVA, logistic regression, and non-inferiority/equivalence designs.
Medical AI paper optimization for AI search engines (Perplexity, ChatGPT web, Elicit, Consensus, SciSpace) and RAG-based literature tools. Applies when drafting or reviewing titles, abstracts, structured summary boxes (Key Points / Research in Context / Plain-Language Summary), manuscripts for high-impact medical AI journals (Lancet Digital Health, Radiology, Radiology-AI, npj Digital Medicine, Nature Medicine), preprints (medRxiv/arXiv), GitHub README + CITATION.cff + Zenodo archives, and Hugging Face model/dataset cards. Integrates TRIPOD+AI, CLAIM 2024, STARD-AI, TRIPOD-LLM, DECIDE-AI reporting requirements with generative engine optimization (GEO) principles. Produces a visible pass/fail checklist.
Statistical analysis for medical research papers. Generates reproducible Python/R code with publication-ready tables and figures. Supports diagnostic accuracy, inter-rater agreement, meta-analysis, survival analysis, survey data, group comparisons, regression, propensity score, and repeated measures.
End-to-end cross-national comparison study using KNHANES + NHANES + CHNS (or other parallel surveys). Variable harmonization, parallel weighted analysis, and comparison tables. Supports 2-country (KR+US) and 3-country (KR+US+CN) designs.
> Literature-grounded variable operationalization for observational research. Turns a data dictionary + research question into a citation-backed table of exposure/outcome/covariate definitions, cutoffs, and DB variable mappings. Prevents ad-hoc phenotype definitions that invite reviewer rejection. Bridges /search-lit output into /write-protocol Methods.
> De-identify clinical research data before LLM-assisted analysis. Standalone Python CLI detects PHI via regex + heuristics with 10 country locale packs (kr, us, jp, cn, de, uk, fr, ca, au, in). Interactive terminal review. No LLM touches raw data — the script runs locally without any network or AI calls.
> Study design and validity review for radiology and medical AI research. Identifies analysis unit, cohort logic, leakage risks, comparator design, validation strategy, and reporting guideline fit before drafting or submission.
> Research gap finder for longitudinal cohort databases. Profiles cohort strengths, matches PI expertise, scans literature saturation, and outputs ranked topic proposals checkup registries, or disease-specific registries.
Journal recommendation engine for medical manuscripts. 2-pass matching against a curated public profile library plus any user-local private profiles, enriched with detailed write-paper profiles for top-5 output. Returns ranked recommendations with scope fit rationale, AI disclosure policy, and homepage links. Impact-factor and APC figures in a profile are point-in-time and may be stale — verify current metrics at the journal site. A pre-ranking acceptance-readiness pre-flight scans the manuscript for design-ceiling, unfixable-defect, and importance-risk signals to add an acceptance-feasibility axis alongside scope fit, and the output includes a reject-fallback cascade plan.
Detect and remove AI writing patterns from academic manuscripts and response-to-reviewers letters. Scans for 27 common AI-generated text patterns and rewrites flagged passages to sound naturally human-written while preserving technical accuracy, bounding how much of the text a rewrite is allowed to touch.
> Intake and normalize a new radiology research project. Classifies project type, summarizes current state, identifies missing inputs, recommends next steps, and scaffolds lightweight project memory files.
Sync research references from .bib files to Zotero library + Obsidian literature notes. Extract cross-cutting concept notes when enough literature accumulates. Works after /search-lit or standalone.
> Cross-cutting reference manager for medical manuscripts. Single entry point for citation-key validation, journal-CSL pandoc rendering, manuscript ↔ DOCX cross-reference QC, marker conversion (``[N]`` ↔ ``[@key]``), and native Zotero CWYW field-code injection. Replaces the inline reference-handling that previously lived in ``/write-paper`` Phase 7.6 and is reused by ``/revise``, ``/peer-review``, ``/sync-submission``, and any skill that produces a journal submission. Audit-only verification stays in ``/verify-refs`` — this skill writes (renders, injects, converts); that skill only reads.
Research project management for medical manuscripts. Scaffold project structure, track writing progress across phases, maintain project memory files, generate submission checklists and backwards timelines. Commands: init, status, sync-memory, checklist, timeline.
Generate publication-ready figures and visual abstracts for medical research papers. Supports ROC curves, forest plots, CONSORT/STARD/PRISMA flow diagrams, calibration plots, Kaplan-Meier curves, Bland-Altman plots, confusion matrices, pipeline diagrams, and journal-specific visual/graphical abstracts (python-pptx template-based).
Systematic review and meta-analysis pipeline for medical research. Covers protocol registration (PROSPERO), search strategy, screening, data extraction, risk of bias assessment (QUADAS-2/ROBINS-I), statistical synthesis (bivariate/HSROC for DTA, random-effects for intervention), and PRISMA-compliant reporting. Supports both DTA and intervention meta-analyses.
> General-purpose research orchestrator. Routes ambiguous or multi-step requests to the right skill(s) from the medsci-skills bundle. Use when the user describes a research goal without naming a specific skill, or when a task spans multiple skills.
Academic English consistency linting and non-native (ESL) language polish for medical manuscripts. Deterministically flags abbreviation define-once violations, US/UK spelling drift, hyphen-vs-en-dash numeric ranges, P/p case, hyphenation variants, small-number style, and value/unit spacing, then guides a style-only clarity pass that never alters numbers, citations, or scientific meaning. Distinct from humanize (AI-tell removal) and check-reporting (guideline items).
> Academic presentation preparation — paper-driven (journal club, grand rounds, seminar) and lecture/teaching decks (course material, workshop slides, conference talks). Analyzes source material, finds supporting references, drafts audience-adapted speaker scripts, generates or augments PPTX with speaker notes, and prepares Q&A.
Replicate an existing cohort study's methodology on a different database. Extracts study design from a source paper, maps variables to the target DB via harmonization table, generates analysis code, and produces a replication difference report.
> Scaffold and draft medical/AI literature reviews (narrative, scoping PRISMA-ScR, or systematic). Asks for the spine axis, builds a 7-part skeleton with a required Intro scope/non-overlap block, a summary-table stub, an evaluation-metrics critique subsection, and reporting-guideline wiring. Reuses the self-review RV1-RV9 narrative-review probes for QC. Does not invent citations.
Literature search and citation management for medical research. Searches PubMed, Semantic Scholar, and bioRxiv/medRxiv with verified citations. Anti-hallucination — every reference verified via API before inclusion. Generates BibTeX entries.
Pre-submission self-review for the user's own manuscripts, applying a reviewer perspective. Systematic check across 10 categories with research-type branching. Outputs Anticipated Major/Minor Comments with severity framing and optional R0 numbering for /revise pipeline integration.
Audit-only verification of manuscript references against PubMed and CrossRef. Detects fabricated or mismatched citations and writes qc/reference_audit.json. Does not modify references/ or refs.bib.
Full-pipeline medical/scientific paper writing. 8-phase IMRAD workflow from outline to submission-ready manuscript. Supports original articles, case reports, case series, meta-analyses, AI validation studies, animal studies, and technical notes. Do NOT trigger for self-checking (use self-review instead).
>- 撰写机构级个股投资研究报告（二级市场深度研究）。Use whenever the user wants to research, analyze, or value a specific publicly-traded stock — e.g. "研究/分析一下某只股票（公司名或代码）"、"帮我看看 NVDA 值不值得买"、"给某只股票写一份投研报告/研报"、"is this stock a buy / overvalued / fairly valued"，或针对某个具名上市公司询问 估值/护城河/财报/目标价/多空逻辑/投资建议（valuation, moat, fundamentals, fair value, price target, bull/bear case），以及财报/季报/年报/业绩会/电话会/指引更新/earnings review/results/10-Q/10-K/earnings call/guidance update。只要出现「公司名或股票代码 + 任何投研意图」就应触发，即使用户没有明确说"报告"二字。覆盖美股、港股、A股，含 A/H 双重上市与中概 VIE/ADR 结构分析。技能会跑完整流程：按一手披露优先级探测数据并自动降级标注；以预期差（市场隐含 vs 独立预期）为分析主线，产出结构化九章研报——估值一律由脚本计算，反向 DCF+PVGO、三情景概率加权、EPV、EVA/剩余收益多法交叉验证并可跑蒙特卡洛；财报质量做财报质量核查检查（应计/M-Score）；结论按预注册标定规则映射并经反方论证复核；财报请求自动进入同等深度财报模式——并保存为带来源与时间戳的文件。Do NOT use for 单纯的一句话报价、宏观/大盘评论、组合层面的资产配置、或非股票类工具（债券/期货/外汇本身）。
Skill for discovering and researching autonomous AI agents, tools, and ecosystems using the AgentFolio directory.
Analyze market conditions, geographic opportunities, pricing, consumer behavior, and product validation across Google Maps, Facebook, Instagram, Booking.com, and TripAdvisor.
Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.
Research uncertain questions with an explicit, user-approved web search or ChatGPT consultation, then present options and wait for implementation approval.
Manage citations systematically throughout the research and writing process.
Scientific research and analysis skills
Use when the user explicitly invokes CrossFrame Suite for Chinese structural diagnosis workflows across relationships, organizations, public issues, philosophy, research, or essay output.
When the user wants to conduct, analyze, or synthesize customer research. Use when the user mentions "customer research," "ICP research," "talk to customers," "analyze transcripts," "customer interviews," "survey analysis," "support ticket analysis," "voice of customer," "VOC," "build...
When the user wants to do developer advocacy activities including conference talks, live coding, podcasts, and building in public. Trigger phrases include "developer advocacy," "devrel," "conference talk," "CFP," "call for papers," "live coding," "podcast," "building in public,"...
> Protocol for token-efficient web research. Use when accessing URLs, GitHub repos, or running search queries. Prevents full-page fetching waste.
Exam preparation assistant that converts syllabi, past papers, or notes into a ranked High Score Roadmap. Covers theory, numericals, MCQs, coding, and lab prep, ordered Easy → Medium → Hard. Use for last-minute revision, important topics, and question prediction.
A practical, jargon-free guide to functional programming - the 80/20 approach that gets results without the academic overhead
Connect to Helium's MCP server for news research, media bias analysis, balanced perspectives, stock/options data, and semantic meme search across 3.2M+ articles and 5,000+ sources
Publish and manage research papers on Hugging Face Hub. Supports creating paper pages, linking papers to models/datasets, claiming authorship, and generating professional markdown-based research articles.
Look up and read Hugging Face paper pages in markdown, and use the papers API for structured metadata such as authors, linked models/datasets/spaces, Github repo and project page. Use when the user shares a Hugging Face paper page URL, an arXiv URL or ID, or asks to summarize, explain,...
Detect and rewrite AI-like Chinese text with a practical workflow for scoring, humanization, academic AIGC reduction, and style conversion. Use when the user asks to 去AI味, 降AIGC, 去除AI痕迹, 论文降重, 知网检测, 维普检测, humanize chinese, detect AI text, or make Chinese text sound more natural.
Five-phase pipeline (triage → clarify → research → PRD → plan) that turns a raw idea into four linked files: clarifying questions, deep research, a PRD with non-goals and metrics, and a phased execution plan with mermaid user journey and kill criteria.
Deterministic search across arXiv, PubMed/PMC, and US policy corpora with daily freshness cutoffs.
Research a topic from the last 30 days on Reddit + X + Web, become an expert, and write copy-paste-ready prompts for the user's target tool.