Claude Skills by BioTender-max

Comprehensive academic literature search and synthesis across 15+ sources.

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.

Draft patent applications for scientific inventions, covering claims, specification, and prior art analysis.

Create publication-quality academic presentations (.pptx) for group meetings, thesis defenses, conference talks, and posters.

Write clear, reproducible experimental protocols and Standard Operating Procedures (SOPs) for any scientific discipline.

Real-time XR video vs. protocol text matching and deviation detection. Aligns first-person XR headset video streams frame-by-frame against structured protocol steps, flags procedural deviations, scores compliance, and delivers corrective audio/visual overlays — enabling one-person lab operation with zero-missed-step guarantees.

Generates short, imperative guidance prompts for the next experimental step from current video frame and protocol context. Output is optimized for voice broadcast (TTS) or AR overlay — concise, actionable, command-style — to guide researchers in real time, correct deviations, or resume experiments without breaking flow.

Prepare regulatory submissions for drugs, biologics, devices, and diagnostics.

Ensure research is reproducible, transparent, and meets open science standards.

Use this skill when the user asks to write a literature review, review article, or 综述 based on an outline. Trigger keywords: "写综述", "write review", "综述写作", "按大纲写", "逐节写", "review section", "写第N节". This skill orchestrates the ENTIRE review writing process from outline to finished manuscript. **This skill calls `academic-literature-search` skill for all search and citation operations. Read that skill first if not already loaded.** **Tool routing**: PubMed operations → MCP tools (`pubmed_search_...

Converts natural language or PDF protocol text into executable step sequences for Opentrons or PyLabRobot. Parses protocol descriptions to extract pipette volumes, well positions, temperatures, incubation times, and transfer patterns; outputs Python code snippets or JSON instruction lists ready for robot execution or simulation.

Translate complex scientific findings into engaging content for non-specialist audiences.

AI-powered scientific illustration generation using Gemini Image models. Creates publication-quality mechanism diagrams, pathway illustrations, and scientific figures.

Comprehensive statistical methodology for scientific research. Covers test selection, assumption verification, power analysis, effect size reporting, and reporting standards.

Publication-quality scientific figure generation. GENERAL: language-agnostic (R, Python, Julia, or any tool).

Maps natural language voice commands to concrete LabClaw skill invocations. Parses ASR output, identifies intent, selects target skill, fills parameters from context, and provides prompt templates — enabling hands-free, voice-driven anywhere-lab experiences where researchers control analysis, guidance, and data export by speaking.

Scientific manuscript writing across all formats, fields, and journals.

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 t...

CJK (中日韩) 字体检测与 matplotlib 配置。任何涉及中文标签、标题、图例的 可视化任务启动前必须先执行本 skill 的字体检测流程，确保不会出现方块乱码。 适用于 matplotlib / seaborn / plotly 静态导出等场景。

每次科研任务开始时，生成一个临时的本地 dashboard，动态展示任务关键信息和产物预览。

Send rich interactive cards with embedded images in Feishu group chats. Use when reporting progress, sharing analysis results, or presenting any content that benefits from mixed text+image layout in Feishu. Combines SVG UI templates (or matplotlib/PIL charts) with Feishu Card Kit API.

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 reproduction reports (Markdown + LaTeX PDF). Covers the full pipeline: data exploration, variable identification/mapping, sample filtering, variable construction, statistical analysis, result comparison, and documentation. Applicable...

Generate professional SVG UI panels for structured information display. Use when presenting lists, task checklists, pipeline/dependency status diagrams, or rich-text report layouts as SVG images. Covers four templates - list-panel, checklist-panel, pipeline-status, richtext-layout. Style is professional, business-oriented, academic-grade with Material Design color palette.

Use this skill whenever the user wants an end-to-end workflow for the ABCD Study dataset, including download via NIMH Data Archive, BIDS organization, and multimodal processing of sMRI, fMRI, and dMRI. Triggers include: 'ABCD Study', 'ABCD data', 'process ABCD', 'ABCD fMRI', 'ABCD sMRI', 'ABCD diffusion', or any request to run the ABCD multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for ABCD.

Use this skill whenever the user wants an end-to-end workflow for the ABIDE (Autism Brain Imaging Data Exchange) dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data. Triggers include: 'ABIDE', 'ABIDE data', 'process ABIDE', 'ABIDE fMRI', 'ABIDE sMRI', 'autism imaging', or any request to run the ABIDE pipeline. This is the NeuroClaw dataset-orchestration layer for ABIDE.

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 ADHD-200 dataset, including download, BIDS organization, and processing of sMRI and rs-fMRI data. Triggers include: 'ADHD-200', 'ADHD200', 'process ADHD data', 'ADHD fMRI', or any request to run the ADHD-200 pipeline. This is the NeuroClaw dataset-orchestration layer for ADHD-200.

Use this skill whenever the user wants an end-to-end workflow for ADNI data (fMRI + T1), including BIDS preparation, fMRIPrep preprocessing, and DK68 ROI pipeline. This is the NeuroClaw dataset-orchestration layer for ADNI.

Use this skill whenever the user wants an end-to-end workflow for the AIBL (Australian Imaging, Biomarkers and Lifestyle) dataset, including data access guidance, BIDS organization, and multimodal processing of sMRI and PET (PiB, FDG, tau). Triggers include: 'AIBL', 'AIBL data', 'process AIBL', 'AIBL PET', 'AIBL MRI', or any request to run the AIBL multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for AIBL.

Use this skill whenever the user wants an end-to-end workflow for the AOMIC (Amsterdam Open MRI Collection) dataset, including data access, BIDS organization, and multimodal processing of sMRI, rs-fMRI, and task-fMRI. Triggers include: 'AOMIC', 'AOMIC data', 'process AOMIC', 'AOMIC fMRI', 'AOMIC resting state', or any request to run the AOMIC multimodal pipeline. This is the NeuroClaw dataset-orchestration layer for AOMIC.

Use this skill whenever the user wants to process Arterial Spin Labeling (ASL) perfusion MRI data including CBF (cerebral blood flow) quantification, ASL preprocessing (motion correction, partial volume correction, M0 normalization), or ASL-based brain perfusion analysis. Triggers include: 'ASL', 'ASL processing', 'CBF', 'cerebral blood flow', 'perfusion MRI', 'arterial spin labeling', 'pCASL', 'CASL', 'PASL', or any request involving ASL perfusion data.

Use this skill after a conversation or task is completed when the user wants a clean, beautiful HTML chat log. It keeps only direct NeuroClaw <-> User dialogue, filters out tool calls / internal traces / SKILL.md reading notes, and renders distinct colored message cards for each side.

Use this skill whenever the user wants to automatically organize raw neuroimaging data (DICOM, NIfTI, EEG, etc.) into a valid BIDS (Brain Imaging Data Structure) dataset. Triggers include: 'organize to BIDS', 'BIDS organizer', 'convert to BIDS', 'BIDS conversion', 'bidsify', 'create BIDS dataset', 'raw data to BIDS', or any request to structure data according to BIDS specification.

Use this model doc whenever the user wants to run BrainNetworkTransformer for fMRI phenotype prediction, including data loading, training, and evaluation. BNT uses dense FC matrices (no PyG dependency) with DEC pooling + interpretable transformer encoder.

Use this skill whenever the user wants an end-to-end workflow for the BOLD5000 dataset, including download, BIDS organization, and processing of task-fMRI data with visual image stimuli. Triggers include: 'BOLD5000', 'BOLD 5000', 'process BOLD5000', 'visual fMRI', or any request to run the BOLD5000 pipeline. This is the NeuroClaw dataset-orchestration layer for BOLD5000.

Use this skill whenever the user wants to visualize neuroimaging analysis results, including 3D brain connectivity networks, atlas-based regional activation summaries, or FreeSurfer cortical surface meshes with anatomical colors. Triggers include: 'brain visualization', 'visualize connectome', '3D brain network', 'zALFF visualization', 'brain activation map', 'FreeSurfer PLY export', 'surface mesh rendering', or any request to turn neuroimaging outputs into interpretable figures or 3D models.

Use this model doc whenever the user wants to run BrainGNN for fMRI phenotype prediction, including graph construction, training, and evaluation. This document focuses on model-level usage and delegates upstream preprocessing to fmri-skill (and optionally hcpya-skill for HCP data).

Use this skill whenever the user wants an end-to-end workflow for the Cam-CAN (Cambridge Centre for Ageing and Neuroscience) dataset, including BIDS validation, multimodal processing of sMRI, rs-fMRI, task-fMRI, and MEG, phenotype extraction, and QC integration. Triggers include: 'Cam-CAN', 'CamCAN', 'process Cam-CAN data', 'Cam-CAN MEG', 'Cam-CAN fMRI', or any request to run the Cam-CAN multimodal pipeline.

Use this skill whenever any NeuroClaw skill, sub-agent, or model needs to execute shell commands safely (e.g. source environment scripts, run recon-all, git operations, conda commands, ls, cat logs, etc.). Triggers include: 'run shell', 'execute command', 'shell command', 'tmux claw', 'run in claw session', 'safe shell execution', or any request that requires running terminal commands. This skill is the mandatory gatekeeper for all shell execution in NeuroClaw: it ALWAYS routes commands throu...

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 model doc whenever the user wants to run Com-BrainTF (Community-aware Brain Transformer) for fMRI phenotype prediction. Com-BrainTF uses dense FC matrices with a two-level Transformer (per-community local + global) and DEC pooling. NeuroClaw auto-derives community partitions from atlas naming conventions (Yeo 7-net for Schaefer, lobe-based for AAL).

Use this skill whenever the user wants to create, activate, list, export, update, clone, remove, or otherwise manage conda environments, or when a deep-learning / model skill requires a clean, isolated conda environment (e.g. 'create conda env for torch 2.3 cuda', 'export current env to yml', 'list all my conda envs', 'update packages in neuroclaw-dl', 'remove old env', 'clone env for reproducibility', 'install pytorch in new env'). Triggers include: 'conda create', 'conda env', 'make new env...

Use this skill whenever the user wants to perform advanced functional connectivity (ROI-to-ROI, seed-to-voxel, ICA) or effective connectivity (PPI, gPPI, DCM) analysis using the CONN Toolbox. Triggers include: 'conn', 'CONN toolbox', 'functional connectivity', 'effective connectivity', 'ROI-to-ROI', 'seed-to-voxel', 'PPI', 'gPPI', 'DCM', 'psychophysiological interaction', or any request for connectivity analysis after preprocessing.

Use this skill whenever the user wants to convert DICOM files or folders to NIfTI format (.nii or .nii.gz), extract neuroimaging volumes from clinical DICOM series (MRI, CT, PET, etc.), prepare raw DICOM data for research processing pipelines, anonymize while converting, or batch-convert multiple series/studies. Triggers include: 'DICOM to NIfTI', 'dcm to nii', 'convert dicom to nii.gz', 'dcm2niix', 'extract nii from dicom', 'batch dicom to nifti', 'prepare dicom for freesurfer/fsl/spm', 'ano...

Use this skill whenever a NeuroClaw skill, model, or sub-agent reports a missing dependency (e.g. ImportError, ModuleNotFoundError, command not found), or when the user explicitly requests to install, setup, configure, or fix any library, package, compiler, CUDA toolkit, conda environment, system tool, or git-based repository. Triggers include: 'install', 'setup', 'missing dependency', 'fix import error', 'install torch cuda', 'conda create environment', 'pip install from git', 'install nnU-N...

Use this model doc whenever the user wants to perform neuroimaging signal denoising with classical detrending methods. This is a non-deep-learning preprocessing route focused on removing low-frequency drift and linear trends from time series before downstream analysis.

Use this model doc whenever the user wants to perform resting-state network decomposition using DictLearning. This is a non-deep-learning unsupervised route focused on sparse component extraction, network map discovery, and subject-level time series from resting-state fMRI.

Use this skill whenever any NeuroClaw diffusion MRI / DWI modality skill needs to execute concrete DIPY operations: load DWI (NIfTI+bvals+bvecs), optional masking, DTI fitting, compute FA/MD/AD/RD, and extract ROI statistics. This is the dedicated base/tool skill that contains all specific DIPY code and usage patterns. Never called directly by the user.

Use this skill whenever the user wants an end-to-end workflow for the DMT-HAR-MED dataset (ds006644), including download, BIDS organization, and processing of rs-fMRI data from a psychedelic intervention study. Triggers include: 'DMT-HAR-MED', 'DMT HAR MED', 'ds006644', 'process DMT data', 'psychedelic fMRI', or any request to run the DMT-HAR-MED pipeline. This is the NeuroClaw dataset-orchestration layer for DMT-HAR-MED.