搜索结果
ing
找到 904 个相关结果 / 研究学习
研究学习 / 检索整理
biopython
biopython
Comprehensive molecular biology toolkit. Use for sequence manipulation, file parsing (FASTA/GenBank/PDB), phylogenetics, and programmatic NCBI/PubMed access…
研究学习 / 检索整理
research-ideation
research-ideation
End-to-end research ideation pipeline: literature grounding → multi-track idea generation (3 personas: innovator/pragmatist/critic) → iterative refinement →…
研究学习 / 检索整理
modal
modal
Cloud computing platform for running Python on GPUs and serverless infrastructure. Use when deploying AI/ML models, running GPU-accelerated workloads, serving…
研究学习 / 检索整理
geopandas
geopandas
Python library for working with geospatial vector data including shapefiles, GeoJSON, and GeoPackage files. Use when working with geographic data for spatial…
研究学习 / 检索整理
creative-thinking-for-research
creative-thinking-for-research
Applies cognitive science frameworks for creative thinking to CS and AI research ideation. Use when seeking genuinely novel research directions by leveraging…
研究学习 / 检索整理
人性化中文
humanize-chinese
检测并人性化AI生成的中文文本。20+规则检测类别 + 统计特征 + 场景感知LR融合(规则×0.2 + LR×0.8),基于三种场景训练:通用/学术/长文本(≥1500字)。统一CLI:./humanize {detect,rewrite,academic,style,compare}。8种风格转换( casual/知乎/小红书/微信/学术/文学/微博/小说)。多段落重写 + best-of-N人性化。165种替换模式 + 同义词词林38873。学术论文AIGC降重,适配CNKI/VIP/Wanfang(知网/维普/万方 AIGC检测降重)。纯Python,零依赖,离线运行。v5.0.0 — HC3融合95%,学术-65 / 工作汇报-83 / 长篇博客-55。当用户提及以下关键词时触发:"去AI味"、"降AIGC"、"人性化文本"、"humanize chinese"、"AI检测"、"AIGC降重"、"去除AI痕迹"、"文本改写"、"论文降重"、"知网检测"、"维普检测"、"AI写作检测"、"让文字更自然"、"detect AI text"、"humanize text"、"reduce AIGC score"、"make text human-like"、"去ai化"、"改成人话"、"去机器味"、"降低AI率"、"过AIGC检测"、"长文本改写"、"小说改写"
研究学习 / 检索整理
scanpy
scanpy
Standard single-cell RNA-seq analysis pipeline. Use for QC, normalization, dimensionality reduction (PCA/UMAP/t-SNE), clustering, differential expression, and…
研究学习 / 检索整理
ln-610-docs-auditor
ln-610-docs-auditor
Use when auditing project documentation through the evaluation platform with mandatory research, coordinated audit workers, and structured summaries.
研究学习 / 检索整理
pyhealth
pyhealth
Build clinical/healthcare deep-learning pipelines with PyHealth — loading EHR/signal/imaging datasets (MIMIC-III/IV, eICU, OMOP, SleepEDF, ChestXray14,…
研究学习 / 检索整理
pydicom
pydicom
Python library for working with DICOM (Digital Imaging and Communications in Medicine) files. Use this skill when reading, writing, or modifying medical…
研究学习 / 检索整理
scvi-tools
scvi-tools
Deep generative models for single-cell omics. Use when you need probabilistic batch correction (scVI), transfer learning, differential expression with…
研究学习 / 检索整理
bioservices
bioservices
Unified Python interface to 40+ bioinformatics services. Use when querying multiple databases (UniProt, KEGG, ChEMBL, Reactome) in a single workflow with…
研究学习 / 检索整理
storing-and-querying-vectors
storing-and-querying-vectors
>-
研究学习 / 检索整理
pydeseq2
pydeseq2
Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq…
研究学习 / 检索整理
canghe-comic
canghe-comic
Knowledge comic creator supporting multiple art styles and tones. Creates original educational comics with detailed panel layouts and sequential image…
研究学习 / 检索整理
geomaster
geomaster
Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains.…
研究学习 / 检索整理
pysam
pysam
Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing…
研究学习 / 检索整理
clinical-decision-support
clinical-decision-support
Generate professional clinical decision support (CDS) documents for pharmaceutical and clinical research settings, including patient cohort analyses…
研究学习 / 检索整理
imaging-data-commons
imaging-data-commons
Query and download public cancer imaging data from NCI Imaging Data Commons using idc-index. Use for accessing large-scale radiology (CT, MR, PET) and…
研究学习 / 检索整理
pufferlib
pufferlib
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent…