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Vendor-agnostic lab automation framework. Use when controlling multiple equipment types (Hamilton, Tecan, Opentrons, plate readers, pumps) or needing unified programming across different vendors. Best for complex workflows, multi-vendor setups, simulation. For Opentrons-only protocols with official API, opentrons-integration may be simpler.

Efficient storage and retrieval of genomic variant data using TileDB. Scalable VCF/BCF ingestion, incremental sample addition, compressed storage, parallel queries, and export capabilities for population genomics.

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model.

Creative research ideation and exploration. Use for open-ended brainstorming sessions, exploring interdisciplinary connections, challenging assumptions, or identifying research gaps. Best for early-stage research planning when you do not have specific observations yet. For formulating testable hypotheses from data use hypothesis-generation.

Genomic file toolkit. Read/write SAM/BAM/CRAM alignments, VCF/BCF variants, FASTA/FASTQ sequences, extract regions, calculate coverage, for NGS data processing pipelines.

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.

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 pathology datasets for AI training or research. No authentication required. Query by metadata, visualize in browser, check licenses.

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.

Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, cloud-native workflows (STAC, COG, Planetary Computer), and 8 programming languages (Python, R, Julia, JavaScript, C++, Java, Go, Rust) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.

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.

Before telling the user a coding task is done, actually verify it — build, run the relevant tests/linter, and confirm the change does what was asked with no regressions. Use whenever you are about to claim a change is finished, fixed, or working.

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A skill that cannot be invoked by the model.

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Smart commit: bump version, find related issues, commit with Co-authored-by, push. Use when the user says 'commit' or wants to finalize changes.

为智能体编写文档。当你正在创建或编辑技能，或者修改 AGENTS.md / CLAUDE.md 时使用。

等等。上一条消息我没理解：重新解释一下。

将你无法完全回答的决策转化为供他人填写的问卷。

在当前工作区中教会用户一项新技能或概念。

将当前对话压缩为一份交接文档，供另一个智能体继续工作。

对用户的计划、决策或想法进行层层盘问。当用户想要压力测试自己的思考，或使用任何 'grill' 触发短语时使用。

一场无休止的盘问，用来打磨你的计划或设计。

在当前仓库中设置 Husky pre-commit 钩子，集成 lint-staged（Prettier）、类型检查和测试。当用户想要添加 pre-commit 钩子、设置 Husky、配置 lint-staged、或添加提交时的格式化/类型检查/测试时使用。

创建包含 section、problem、solution 和 explainer 的练习目录结构，且能通过 lint 检查。当用户想要搭建练习框架、创建练习模板或设置新的课程章节时使用。

将测试文件从 `as` 类型断言迁移到 @total-typescript/shoehorn。当用户提到 shoehorn、想要在测试中替换 `as`、或需要部分测试数据时使用。

Writing, exploit ： 将原始素材逐段塑形为一篇文章。

Writing, explore ： 挖掘原始碎片，不施加结构。

Writing, exploit ： 将原始素材组装成一段节拍之旅，在节拍使用每个术语之前先将其夯实。

将 dependency-cruiser 接入 TypeScript 仓库，使每个 package 都是一个深度模块：实现隐藏在子文件夹中，只能通过入口点文件访问。由用户调用。

在此 workspace 内就我想构建的工作流规格对我进行盘问。

把当前对话交接给一个全新的后台 agent，让它立刻接手工作。

在单次长会话中协调子智能体并安排日程，持续推进一项长期目标。

生成一个交互式 bash 向导，引导人类完成只有他们能执行的步骤。适用于配置基础设施、设置凭证或 CI 密钥、操作不熟悉的第三方面板、或执行一次性迁移或切换。不要让代理替自己执行本应由人类完成的步骤。

规划一大块工作——超过一次 agent 会话能容纳的体量——在你的 Issue 跟踪器上以共享的决策 ticket 地图形式呈现，逐个解决，直到通往目的地的道路变得清晰。

将 issue 和外部 PR 推过一组分诊角色的状态机——分类、验证、必要时盘问，并写出 agent 就绪的任务简报。

将计划、规范或当前对话拆分为一组 tracer-bullet ticket，每个 ticket 声明其阻塞边（blocking edges），发布到已配置的跟踪器——本地文件中每个 ticket 一个文件以文本表示边，真实跟踪器上以原生阻塞链接表示。

将当前对话转化为 spec 并发布到项目的 issue tracker——不访谈，只综合你们已经讨论过的内容。

测试驱动开发。当用户想以测试先行构建功能或修复 bug、提到 "red-green-refactor"，或想要集成测试时使用。

为本仓库配置工程技能——设置其 issue tracker、分诊标签词汇表和领域文档布局。首次使用其他工程技能前运行一次。

对照高可信度一手来源调查问题，并将发现以 Markdown 文件落库。当用户希望研究某个主题、收集文档或 API 事实、或把阅读跑腿交给后台 agent 时使用。

构建一个可抛弃的原型来回答设计问题。当用户想验证状态模型或逻辑是否合理，或探索 UI 应该长什么样时使用。

扫描代码库寻找深化机会，以可视化的 HTML 报告呈现，然后对你选中的方案进行盘问。

在代码中实现 /to-spec 与 /to-tickets 的产物。

基于 spec 或一组 tickets 实现工作内容。

一场用于打磨计划或设计的严格盘问，并在过程中生成文档（ADR 和术语表）。

构建和完善项目的领域模型。当讨论代码库术语、编写或编辑 GLOSSARY.md，或记录或编辑 ADR 时使用。

设计深度模块的共享词汇。当用户想要设计或改进模块的接口、寻找深化机会、决定 seam 的位置、让代码更可测试或更易被 AI 导航，或者其他 skill 需要深度模块词汇时使用。

从固定点（commit、branch、tag 或 merge-base）开始，沿两条轴线审查变更——规范（代码是否遵循仓库文档化的编码规范？）和规格（代码是否与原始 issue/PRD 的要求一致？）。两条审查线在并行子 agent 中运行，并以并排方式报告结果。当用户想审查一个分支、PR、进行中的变更，或要求"从 X 开始审查"时使用。

询问哪个技能或流程适合你的情况。本仓库内技能的路由器。

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

Create publication-quality scientific diagrams using Nano Banana 2 AI with smart iterative refinement. Uses Gemini 3.1 Pro Preview for quality review. Only regenerates if quality is below threshold for your document type. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.

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.

Therapeutics Data Commons. AI-ready drug discovery datasets (ADME, toxicity, DTI), benchmarks, scaffold splits, molecular oracles, for therapeutic ML and pharmacological prediction.

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

Self-hosted, open-source alternative to Google NotebookLM for AI-powered research and document analysis. Use when organizing research materials into notebooks, ingesting diverse content sources (PDFs, videos, audio, web pages, Office documents), generating AI-powered notes and summaries, creating multi-speaker podcasts from research, chatting with documents using context-aware AI, searching across materials with full-text and vector search, or running custom content transformations. Supports 16+ AI providers including OpenAI, Anthropic, Google, Ollama, Groq, and Mistral with complete data privacy through self-hosting.

Neuropixels neural recording analysis. Load SpikeGLX/OpenEphys data, preprocess, motion correction, Kilosort4 spike sorting, quality metrics, Allen/IBL curation, AI-assisted visual analysis, for Neuropixels 1.0/2.0 extracellular electrophysiology. Use when working with neural recordings, spike sorting, extracellular electrophysiology, or when the user mentions Neuropixels, SpikeGLX, Open Ephys, Kilosort, quality metrics, or unit curation.

Convert files and office documents to Markdown. Supports PDF, DOCX, PPTX, XLSX, images (with OCR), audio (with transcription), HTML, CSV, JSON, XML, ZIP, YouTube URLs, EPubs and more.