Claude Skills by majiayu000

Register and implement PydanticAI tools with proper context handling, type annotations, and docstrings. Use when adding tool capabilities to agents, implementing function calling, or creating agent actions.

Local Whisper: audio transcription, multi-language, word timestamps, speaker diarization

Local speech-to-text with the Whisper CLI (no API key).

Perform pairwise sequence alignment using Biopython Bio.Align.PairwiseAligner. Use when comparing two sequences, finding optimal alignments, scoring similarity, and identifying local or global matches between DNA, RNA, or protein sequences.

Monitor construction progress using computer vision. Analyze site photos and drone imagery to track work completion, detect safety issues, and compare against BIM models.

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.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Bayesian modeling with PyMC. Build hierarchical models, MCMC (NUTS), variational inference, LOO/WAIC comparison, posterior checks, for probabilistic programming and inference.

Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.

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

Deep learning framework (PyTorch Lightning). Organize PyTorch code into LightningModules, configure Trainers for multi-GPU/TPU, implement data pipelines, callbacks, logging (W&B, TensorBoard), distributed training (DDP, FSDP, DeepSpeed), for scalable neural network training.

Build pytorch model trainer operations. Auto-activating skill for ML Training. Triggers on: pytorch model trainer, pytorch model trainer Part of the ML Training skill category. Use when working with pytorch model trainer functionality. Trigger with phrases like "pytorch model trainer", "pytorch trainer", "pytorch".

IBM quantum computing framework. Use when targeting IBM Quantum hardware, working with Qiskit Runtime for production workloads, or needing IBM optimization tools. Best for IBM hardware execution, quantum error mitigation, and enterprise quantum computing. For Google hardware use cirq; for gradient-based quantum ML use pennylane; for open quantum system simulations use qutip.

Quantum physics simulation library for open quantum systems. Use when studying master equations, Lindblad dynamics, decoherence, quantum optics, or cavity QED. Best for physics research, open system dynamics, and educational simulations. NOT for circuit-based quantum computing—use qiskit, cirq, or pennylane for quantum algorithms and hardware execution.

Convert an ML PRD into prd.json for ML-Ralph. Use when you have an ML PRD and need prd.json. Triggers on: convert this prd, turn this into ml-ralph format, create prd.json from this, ralph json.

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive systems.

Deploy production recommendation systems with feature stores, caching, A/B testing. Use for personalization APIs, low latency serving, or encountering cache invalidation, experiment tracking, quality monitoring issues.

Biological data toolkit. Sequence analysis, alignments, phylogenetic trees, diversity metrics (alpha/beta, UniFrac), ordination (PCoA), PERMANOVA, FASTA/Newick I/O, for microbiome analysis.

Deep learning for single-cell analysis using scvi-tools. This skill should be used when users need (1) data integration and batch correction with scVI/scANVI, (2) ATAC-seq analysis with PeakVI, (3) CITE-seq multi-modal analysis with totalVI, (4) multiome RNA+ATAC analysis with MultiVI, (5) spatial transcriptomics deconvolution with DestVI, (6) label transfer and reference mapping with scANVI/scArches, (7) RNA velocity with veloVI, or (8) any deep learning-based single-cell method. Triggers in...

Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.

Retrieve records from NCBI databases using Biopython Bio.Entrez. Use when downloading sequences, fetching GenBank records, getting document summaries, or parsing NCBI data into Biopython objects.

Find cross-references between NCBI databases using Biopython Bio.Entrez. Use when navigating from genes to proteins, sequences to publications, finding related records, or discovering database relationships.

Use when user needs complex error pattern analysis, distributed system debugging, error correlation, root cause discovery, or predictive error prevention across microservices.

Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors

Search the web with Exa AI — neural search, content extraction, similar page discovery, quick research with citations, and async pro research with structured output. Covers all Exa endpoints (/search, /contents, /findSimilar, /answer, /research/v0/tasks). Triggers on web search, find pages, similar pages, research with citations, Exa, neural search.

Use when theoretical calculations need experimental validation, protocols must be designed with clear hypotheses and success criteria, or resource requirements (equipment, materials, expertise) must be estimated for proposed experiments

Explain whatever the user is pointing at right now in plain language: a pending question, a piece of code, an error, a command output, or an artifact like a plan or findings report. Use when the user asks to \"explain this\", \"what am I being asked\", \"what's happening right now\", \"help me understand this\", \"what does this mean\", \"what does this error mean\", \"what is this code doing\", or \"what do these options mean\".

Use when the user asks to investigate, explore, understand, explain, or analyze how an existing feature or logic works. Triggers on keywords like "how does", "explain", "what is the logic", "investigate", "understand", "where is", "trace", "walk through", "show me how".

Fetch web pages, PDFs, and documents with automatic fallbacks and content extraction. Use when user says "fetch this URL", "download this page", "crawl this website", "extract content from", "get the PDF", or provides URLs needing retrieval.

面向股票/行业/宏观的金融研究技能，结合结构化金融数据（OpenBB/AkShare）与多源信息验证，生成带引用与免责声明的研究报告。

Break down problems to fundamental truths. Use when conventional solutions fail, you need innovation, or want to challenge assumptions. Not for standard problems with known solutions or minor optimizations.

First principles analysis. USE WHEN first principles, fundamental, root cause, decompose. SkillSearch('firstprinciples') for docs.

Analyze user conversion funnels, identify drop-off points, and optimize conversion rates for conversion optimization and user flow analysis

Analyzes events through futures lens using scenario planning, trend analysis, weak signals, drivers of change, and forecasting methods (exploratory, normative, backcasting). Provides insights on possible futures, emerging trends, disruptive forces, strategic foresight, and alternative scenarios. Use when: Strategic planning, emerging trends, technology assessment, long-term planning, uncertainty navigation. Evaluates: Trends, weak signals, drivers of change, plausible futures, strategic optio...

Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

Call copy number variants using GATK best practices workflow. Supports both somatic (tumor-normal) and germline CNV detection from WGS or WES data. Use when following GATK best practices or integrating CNV calling with other GATK variant pipelines.

Use Gemini CLI for research with Google Search grounding and 1M token context

Deep-dive analysis of GitHub projects. Use when the user mentions a GitHub repo/project name and wants to understand it — triggered by phrases like "帮我看看这个项目", "了解一下 XXX", "这个项目怎么样", "分析一下 repo", or any request to explore/evaluate a GitHub project. Covers architecture, community health, competitive landscape, and cross-platform knowledge sources.

Use this skill for comprehensive research tasks requiring deep investigation, multiple source validation, and citation-backed reports. Triggers on keywords like "research", "investigate", "deep dive", "comprehensive analysis", "literature review", or when needing validated, sourced information.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

High-performance toolkit for genomic interval analysis in Rust with Python bindings. Use when working with genomic regions, BED files, coverage tracks, overlap detection, tokenization for ML models, or fragment analysis in computational genomics and machine learning applications.

Complete HUMMBL Base120 mental models framework with all 120 models across 6 transformations (Perspective, Inversion, Composition, Decomposition, Recursion, Meta-Systems). Includes model selection guidance, evidence standards, quality criteria, and application methodology. Essential reference for HUMMBL-based development, analysis, and problem-solving.

Assess overall hype levels across AI topics by comparing lab researcher enthusiasm against critic skepticism. Use after topic synthesis to identify which topics are overhyped, underhyped, or accurately assessed by the field.

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.

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.

Generate testable hypotheses. Formulate from observations, design experiments, explore competing explanations, develop predictions, propose mechanisms, for scientific inquiry across domains.

Daily industry intelligence scanner. Scans web, social media, news, blogs, and communities for industry-relevant events, trends, and signals. Produces a comprehensive intelligence briefing plus strategic GTM opportunity ideas. Orchestrates existing scraping skills — does not reimplement data collection.

Use this skill only when the user explicitly requests deep research or a detailed report on a topic — phrases like "research X", "give me a detailed report on X", "deep dive into X", "I want a thorough analysis of X", or "write me a research report on X". Do NOT trigger for casual questions, quick lookups, or requests to add/edit entries. This is a heavyweight, multi-step workflow that mines the knowledge base, fills gaps with web research, creates new KB entries along the way, and produces a...

Run LangChain Open Deep Research agent for iterative web research and comprehensive reports. Requires LLM API keys and search API (e.g., OPENAI_API_KEY, TAVILY_API_KEY).

Conduct comprehensive learning needs analysis including stakeholder interviews, performance gap identification, job/task analysis, and competency mapping. Use when starting any learning project to identify what training is actually needed. Activates on "needs analysis", "training needs", "performance gaps", or "what should we teach".