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
> Query the PharmKG knowledge graph (180k entities, 39 relation types, >1M triples). Use whenever the user asks about biomedical relationships among genes, drugs/chemicals, and diseases — e.g. drug–gene interactions, drug–disease associations, gene–disease links, or drug–drug relationships derived from literature and curated databases.
> Query the PHEE pharmacovigilance event extraction dataset. Use whenever the user asks about annotated adverse drug events, pharmacovigilance case reports, drug–effect associations from medical literature, or wants to find PHEE examples mentioning a drug name, adverse effect, or condition.
Use this skill for creating or refining an academic slide deck and the talk built around it: structuring a conference talk, thesis defense, lab meeting, or paper-to-slides deck; deciding the narrative arc and slide breakdown; improving slide design and visual hierarchy; planning rehearsal, timing, Q&A, and backup slides; or generating the .pptx. Reach for it when the user is shaping the presentation itself. Do not use for writing the paper, producing standalone speaker notes/scripts/transcripts, making posters, creating isolated figures/charts outside a slide deck, or building non-academic presentations.
Manages persistent research memory across ideation and experimentation cycles. Maintains two stores: Ideation Memory M_I (feasible/unsuccessful directions) and Experimentation Memory M_E (reusable strategies for data processing, model training, architecture, debugging). Three evolution mechanisms: IDE (after research-ideation), IVE (after experiment failure — classifies failures as implementation vs fundamental), ESE (after experiment success — extracts reusable strategies). Use when: updating memory after completing research-ideation cycles or experiment pipelines, classifying why a method failed (implementation vs fundamental failure), starting a new research cycle needing prior knowledge, user mentions 'update memory', 'classify failure', 'what worked before', 'research history', 'evolution'. Do NOT use for running experiments (use experiment-pipeline), debugging experiment code (use experiment-craft), or generating ideas (use research-ideation).
Use this skill whenever the user submits a non-trivial mathematical claim that needs a rigorous proof or audit. Trigger on IMO/Putnam/USAMO/Olympiad-style problems, ML/AI theoretical statements, research conjectures, suspected-false claims, multi-step proofs the user already failed on, proof drafts with possible hidden assumptions, or any request containing 'prove rigorously', 'verify this', 'is this true', 'find the gap', 'audit my proof', 'find a counterexample', or 'use EvoMath' that targets a mathematical claim. Activate also when the problem requires more than three reasoning steps. Do NOT use for single-step calculations, definition lookups, textbook exercises with a known recipe, code analysis tasks, literature survey questions, pure symbolic manipulation, or non-mathematical applications of those trigger phrases (e.g., 'is it true that GPT-4 can solve math?', 'verify this LaTeX syntax'); hand those back instead.
Guides structured 4-stage experiment execution with attempt budgets and gate conditions: Stage 1 initial implementation (reproduce baseline), Stage 2 hyperparameter tuning, Stage 3 proposed method validation, Stage 4 ablation study. Integrates with evo-memory (load prior strategies, trigger IVE/ESE) and experiment-craft (5-step diagnostic on failure). Use when: user has a planned experiment, needs to reproduce baselines, organize experiment workflow, or systematically validate a method. Do NOT use for debugging a specific experiment failure (use experiment-craft) or designing which experiments to run (use paper-planning).
Find, read, download, and locally cache academic papers. Disambiguate ambiguous queries, discover via keyword search / citation traversal / recommendations / arXiv monitoring / trending / GitHub search, evaluate (TLDR, citations, code, SOTA), read using a 3-level strategy, and save PDFs to a local library for offline reuse. Use when finding a specific paper, listing papers on a topic, tracking recent advances, finding a baseline with code, reading or downloading a paper by URL, searching the local PDF library, or collecting a corpus for survey/ideation. Trigger phrases include: find/search papers, related work, citation analysis, latest research, download paper, save paper, my local library. Do NOT use for generating survey reports (use research-survey), generating research ideas (use research-ideation), writing a Related Work section (use paper-writing), comparing/ranking ideas (use research-ideation), or planning paper structure (use paper-planning).
Guides pre-writing planning for academic papers with 4 structured steps: story design (task-challenge-insight-contribution-advantage), experiment planning (comparisons + ablations), figure design (pipeline + teaser), and 4-week timeline management. Includes counterintuitive planning tactics (write a mock rejection letter to identify weaknesses before writing, narrow before broad claims, design ablations first). Use when: user wants to plan a paper before writing, design story/contributions, plan experiments, create figure sketches, set a writing timeline, or write a pre-emptive rejection letter for planning purposes. Do NOT use for actual writing (use paper-writing), running experiments (use experiment-pipeline), self-reviewing a finished draft (use paper-review), or finding research problems (use research-ideation).
Guides writing effective rebuttals after receiving peer review feedback. Covers review diagnosis (score-driven color-coding), response strategy (champion identification, common-theme consolidation), tactical writing (18 rules), and counterintuitive rebuttal principles. Use when: user received reviewer scores/comments, needs to write a rebuttal or author response, wants to respond to specific criticism (e.g. 'limited novelty', 'missing baselines'), mentions 'rebuttal', 'reviewer comments', 'author response', or 'respond to reviewers'. Do NOT use for pre-submission self-review (use paper-review instead).
Guides self-review of YOUR OWN academic paper before submission with adversarial stress-testing. Core method: 5-aspect checklist (contribution sufficiency, writing clarity, results quality, testing completeness, method design), counterintuitive protocol (reject-first simulation, delete unsupported claims, score trust, promote limitations, attack novelty), reverse-outlining, and figure/table quality checks. Use when: user wants to self-review or self-check their own paper draft before submission, stress-test their claims, prepare for reviewer criticism, or mentions 'self-review', 'check my draft', 'is my paper ready'. Do NOT use for writing a peer review of someone else's paper, and do NOT use after receiving actual reviews (use paper-rebuttal instead).
End-to-end research ideation pipeline: literature grounding → multi-track idea generation (3 personas: innovator/pragmatist/critic) → iterative refinement → ELO tournament ranking → update evo-memory (IDE) → user selects direction → expand into manuscript-quality proposal. Use when: user wants to find a research direction, brainstorm ideas, evaluate idea novelty, design a novel solution, rank/compare research ideas, or generate a research proposal. Do NOT use for finding/searching/reading papers (use paper-navigator), literature survey reports (use research-survey), or planning a paper (use paper-planning).
Generates structured literature survey reports from collected papers using a multi-stage pipeline: outline generation (query-type adaptive) → draft survey → section-by-section expansion → summary section refinement → final assembly. Produces survey-grade output with taxonomy-based method analysis, LaTeX formalizations, comparative tables, and dense citations. Use when: user wants a literature review, research survey, field overview, or systematic synthesis of multiple papers. Do NOT use for finding/searching papers (use paper-navigator), generating research ideas (use research-ideation), or writing a paper's Related Work section (use paper-writing).
Search scientific papers and retrieve structured experimental data extracted from full-text studies via the BGPT MCP server. Returns 25+ fields per paper including methods, results, sample sizes, quality scores, and conclusions. Use for literature reviews, evidence synthesis, and finding experimental details not available in abstracts alone.
Web toolkit powered by Exa, tuned for scientific and technical content. Use this skill when the user needs to search the web or fetch/extract URL content. Covers: web search (semantic lookups, research, current info — with optional research-paper category and academic domain filtering) and URL extraction (fetching pages, articles, academic PDFs in batch). Use this skill for web-related tasks when the user wants high-quality search or scholarly filtering via category=research paper. Triggers on requests to search, look up, fetch a page, or extract an article.
Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Generate comprehensive market research reports (50+ pages) in the style of top consulting firms (McKinsey, BCG, Gartner). Features professional LaTeX formatting, extensive visual generation with scientific-schematics and generate-image, deep integration with research-lookup for data gathering, and multi-framework strategic analysis including Porter Five Forces, PESTLE, SWOT, TAM/SAM/SOM, and BCG Matrix.
Comprehensive biosignal processing toolkit for analyzing physiological data including ECG, EEG, EDA, RSP, PPG, EMG, and EOG signals. Use this skill when processing cardiovascular signals, brain activity, electrodermal responses, respiratory patterns, muscle activity, or eye movements. Applicable for heart rate variability analysis, event-related potentials, complexity measures, autonomic nervous system assessment, psychophysiology research, and multi-modal physiological signal integration.
Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.
Build slide decks and presentations for research talks. Use this for making PowerPoint slides, conference presentations, seminar talks, research presentations, thesis defense slides, or any scientific talk. Provides slide structure, design templates, timing guidance, and visual validation. Works with PowerPoint and LaTeX Beamer.
Core skill for the deep research and writing tool. Write scientific manuscripts in full paragraphs (never bullet points). Use two-stage process with (1) section outlines with key points using research-lookup then (2) convert to flowing prose. IMRAD structure, citations (APA/AMA/Vancouver), figures/tables, reporting guidelines (CONSORT/STROBE/PRISMA), for research papers and journal submissions.
Generate concise (3-4 page), focused medical treatment plans in LaTeX/PDF format for all clinical specialties. Supports general medical treatment, rehabilitation therapy, mental health care, chronic disease management, perioperative care, and pain management. Includes SMART goal frameworks, evidence-based interventions with minimal text citations, regulatory compliance (HIPAA), and professional formatting. Prioritizes brevity and clinical actionability.
Extracts falsifiable scientific hypotheses (if-then form) from multiple PubMed articles, abstracts, or full texts. Synthesizes supporting evidence, contradictions, and experimental validation suggestions into a structured Markdown report for hypothesis-driven research planning.
> Dispatch biomedical research and data analysis tasks to Claude Code with K-Dense Scientific Skills. Use this skill when the user asks to run any bioinformatics, genomics, drug discovery, clinical data analysis, proteomics, multi-omics, medical imaging, or scientific computation task. Also use for literature search (PubMed, bioRxiv), pathway analysis, protein structure prediction, or scientific writing tasks.
> CHARLS (China Health and Retirement Longitudinal Study) database-specific knowledge for reproducing published papers. Use when reproducing or analyzing papers that use CHARLS data, including variable mapping from harmonized to raw questionnaire items, cognitive function scoring (episodic memory, mental status, TICS), CESD-10 depression screening, social isolation index construction, and chronic disease coding. Also use for any CHARLS data cleaning, variable construction, or cohort selection task.
> Systematic methodology for reproducing published academic papers using provided data. Use when the user asks to reproduce, replicate, or verify results from a published paper, including sample selection, descriptive statistics, regression analyses, and generating variable identification/mapping, sample filtering, variable construction, statistical analysis, result comparison, and documentation. Applicable to any observational study, clinical cohort, or survey-based research paper.
Use this skill when users need to search academic papers, download research documents, extract citations, or gather scholarly information. Triggers include: requests to \"find papers on\", \"search research about\", \"download academic articles\", \"get citations for\", or any request involving academic databases like arXiv, PubMed, Semantic Scholar, or Google Scholar. Also use for literature reviews, bibliography generation, and research discovery.
Use this skill whenever the user wants an end-to-end workflow for the COBRE dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data for schizophrenia research. Triggers include: 'COBRE', 'process COBRE', 'COBRE schizophrenia', 'COBRE fMRI', or any request to run the COBRE pipeline. This is the NeuroClaw dataset-orchestration layer for COBRE.
Use this skill whenever the user wants to execute experiments based on a finalized method and record results. Triggers include: 'run experiment', 'experiment controller', 'implement experiment', 'run model', 'execute training', 'experiment-controller', 'record results', 'ablation study', or any request to turn METHOD.md into concrete runs and output to EXPERIMENT.md. This skill is the **mandatory interface-layer experiment executor** in NeuroClaw: it searches literature/GitHub for matching experimental setups and codebases, proposes one scheme + repo after user discussion, uses git skills to download and setup, runs the experiment(s), and iteratively appends every result + observation to EXPERIMENT.md.
Use this skill whenever the user wants to process structural MRI data (T1w, T2w, FLAIR, etc.) with FreeSurfer, especially for cortical/subcortical segmentation, surface reconstruction, parcellation, cortical thickness, volume statistics, or full recon-all pipeline. Triggers include: 'freesurfer', 'recon-all', 'segment MRI', 'FreeSurfer processing', 'cortical segmentation', 'subcortical segmentation', 'run recon-all', 'freesurfer T1', 'process brain MRI with freesurfer', 'aseg aparc', or any request to run FreeSurfer on NIfTI MRI data for research analysis.
Use this skill when users need to build, populate, or extend a domain-specific knowledge graph from literature and structured databases. Triggers include: 'build knowledge graph', 'extract claims from papers', 'ingest data into graph', 'batch extract claims', 'knowledge graph construction', 'populate graph from PubMed', 'extract structured claims', 'ingest atlas data', or any request involving knowledge graph population from scientific literature or biomedical databases. Covers both structured data ingestion (Phase 1) and LLM-based claim extraction from papers (Phase 2).
Use this skill whenever the user wants to formalize a network architecture and derive theoretical components from a research idea. Triggers include: 'method design', 'design method', 'network architecture', 'formula derivation', 'method-design', 'theoretical framework', 'derive equations', or any request to transform IDEA.md into a detailed METHOD.md. This skill is the **mandatory interface-layer method formalizer** in NeuroClaw: it reads IDEA.md, designs concrete network structures (layers, modules, connections), performs mathematical derivations (equations, loss functions, proofs), and always outputs a structured METHOD.md.
Use this skill whenever the user wants an end-to-end workflow for the OASIS (Open Access Series of Imaging Studies) dataset, including BIDS validation, multimodal processing of sMRI, and phenotype extraction for aging and Alzheimer's disease research. Triggers include: 'OASIS', 'OASIS-1', 'OASIS-2', 'OASIS-3', 'process OASIS data', 'Alzheimer', or any request to run the OASIS pipeline.
Use this skill whenever the user wants to generate a full academic paper draft from existing research materials. Triggers include: 'write paper', 'generate manuscript', 'draft paper', 'paper-writing', 'hierarchical drafting', 'manuscript composer', 'create LaTeX paper', 'write research paper from IDEA METHOD EXPERIMENT', or any request to transform IDEA.md + METHOD.md + EXPERIMENT.md into a typeset-ready manuscript. This skill is the **mandatory interface-layer writer** in NeuroClaw: it strictly follows the hierarchical manuscript drafting and iterative refinement process (section 4.4 + provided flowchart), never generates the full paper in one shot, saves every intermediate step as a separate file, and produces either clean plain-text or LaTeX output.
Use this skill whenever the user wants to generate or refine a research idea through literature search and discussion. Triggers include: 'research idea', 'brainstorm idea', 'generate idea', 'research-idea', 'idea generation', 'discuss new direction', or any request to explore literature and output to IDEA.md. This skill is the **mandatory interface-layer idea generator** in NeuroClaw: it calls networking search skills to retrieve recent papers, identifies gaps/trends, then iteratively discusses with the user to finalize a structured idea, always saving the result as IDEA.md.
| 2021年诺贝尔生理学或医学奖得主，PIEZO1/PIEZO2机械力感受器发现者。 以功能性筛选策略鉴定全新的离子通道家族，揭示了触觉、本体感觉等机械力转导的分子基础。 触发词：「Patapoutian」「PIEZO」「mechanosensation」「mechanotransduction」「压力感受器」「触觉分子机制」。 信息源：诺奖官网、Nature/Science/Cell论文、PNAS/Quanta Magazine/Kavli Prize、Scripps/HHMI官方资料。 调研时间：2026-04-06。
| Craig C. Mello (2006年诺贝尔生理学或医学奖) 的思维框架与决策视角。 核心镜片：简单模型的力量、RNA作为信息货币、跨学科对话。 调研来源：诺奖官网、学术论文、STAT News、NBC News等一手素材。 触发词：「Mello视角」「RNAi思维」「简单模型思维」「基因沉默」「Mello怎么想」。
| David Julius认知框架蒸馏 — 2021年诺贝尔生理学或医学奖得主，温度与触觉受体发现者。 以"自然界的分子工具"为核心方法论，从辣椒素出发开创了整个疼痛感知研究领域。 适用于：科学探索方法论、逆向工程思维、从日常现象发现深层机制的决策框架。 触发词：「Julius视角」「分子工具思维」「从现象到机制」「辣椒素范式」
| 2025年诺贝尔生理学或医学奖得主Fred Ramsdell的思维框架。 聚焦：免疫耐受机制发现、从单基因突变到疾病治疗的全链条思维、工业界科研的价值。 调研来源：7篇一手论文 + Nobel Prize官方资料。信息量有限（极低调的科学家），心智模型基于有限推断。 触发词：FOXP3、Treg、免疫耐受、自身免疫、IPEX、Fred Ramsdell、诺贝尔医学奖2025。
| 诺贝尔医学奖得主Jeffrey C. Hall的思维视角。2017年因发现昼夜节律分子机制获奖。 核心镜片：基础研究的长期主义、模型生物的非直觉力量、负反馈回路的哲学。 调研来源：14条（诺奖官网一手采访3篇、学术论文6篇、权威媒体5篇）。 心智模型：4个。触发词：「Hall视角」「果蝇哲学」「节律思维」「基础研究」。
| Robert G. Edwards (1925-2013) 的思维框架与决策模式。2010年诺贝尔生理学或医学奖得主，体外受精(IVF)之父。 基于12个一手/二手来源的深度调研，提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用Edwards的视角分析问题——特别是在科学创新、伦理争议、长期主义和跨学科协作场景中。 当用户提到「用Edwards的视角」「IVF之父怎么看」「Edwards模式」「Robert Edwards perspective」时使用。
| 诺贝尔奖得主山中伸弥（Shinya Yamanaka）的认知框架。iPS诱导多能干细胞发现者，2012年诺贝尔生理学或医学奖。 核心镜片：临床痛点驱动的减法科学家——从24个因子削减到4个，从外科手术室走向诺贝尔奖。 触发词：「山中伸弥」「Yamanaka」「iPS细胞」「减法思维」「临床驱动研究」「化繁为简」。 调研来源：15+一手来源（Nobel官方、Cell论文、CiRA官网、多个采访），6个研究文件。 心智模型：4个 | 决策启发式：7条 | 诚实边界：5条
| 2024年诺贝尔生理学或医学奖得主Victor Ambros的思维框架与表达方式。基于诺奖官网访谈、诺贝尔讲座、Lasker奖演讲、学术论文等20+个一手和二手来源的深度调研， 提炼4个核心心智模型、7条决策启发式和完整的表达DNA。 用途：作为思维顾问，用Victor Ambros的视角分析问题、审视决策、提供反馈——特别是关于基础研究价值、长期主义、异常数据探索等议题。 当用户提到「用Ambros的视角」「Ambros会怎么看」「microRNA思维」「基础研究价值」「长期主义科学」时使用。
>- Critically review, score, compare, and rank one or more AI scientist outputs for biology, bioinformatics, computational life science, or adjacent research tasks. Trigger when the user asks to evaluate notebooks, code, figures, analyses, manuscripts, software, or final reports produced by AI scientists; compare multiple AI scientists on the same task; judge publication readiness; or audit rigor, reproducibility, novelty, and task completion. Do not use this skill to perform the original research task itself unless the user is explicitly asking for a reviewer-style audit of already produced outputs.
Search arXiv preprints through the official arXiv API and turn arXiv IDs into local Markdown summaries. Use when you need CS, math, physics, or quantitative biology preprints, especially recent submissions that may not yet appear in peer-reviewed literature indexes.
Evaluate scientific rigor, methods, biases, and evidence quality for claims, papers, and study designs.