Skill UI

This tool is designed to enhance lead intelligence by processing and analyzing prospect data. It helps users gain deep insights into potential customers, improving targeting accuracy, optimizing sales pipelines, and refining marketing strategies for business growth.

A comprehensive set of specialized tools for deep interaction with source codebases. This context-mode is mandatory for advanced tasks such as running code snippets, analyzing files, batch processing repository commands, indexing knowledge, searching content, and fetching web data. It ensures that interactions are context-aware and highly efficient, moving beyond basic file listing or general chat prompts.

This skill activates the P7 Senior Engineer mode, designed for rigorous, solution-driven execution under P8 supervision. It is used when the user explicitly requests 'P7模式' or when invoked as a sub-task executor by P8. The process involves three key stages: designing a detailed implementation plan and impact analysis, executing the core coding, and finally, completing a mandatory three-question self-review, ensuring high quality and comprehensive verification.

This skill teaches users to apply the scientific method to software debugging. Instead of random changes, it guides the process through five steps: Observation, Hypothesizing, Prediction, Testing, and Analysis. By systematically forming and testing specific, measurable hypotheses, users can efficiently isolate failing components and confirm the true root cause of technical issues.

This advanced workflow automates the process of analyzing the operational health of specific Azure resources. It ingests and analyzes raw logs, telemetry data, and performance metrics from diverse Azure services (e.g., VMs, Web Apps, Databases). The system performs comprehensive root cause analysis, classifies issues (Critical, High, Medium), and generates actionable, step-by-step remediation plans, significantly reducing manual troubleshooting time.

Astropy is a core Python library for astronomy and astrophysics workflows. It provides essential functionality for astronomical research and data analysis, including coordinate transformations, unit and quantity calculations, FITS file operations, cosmological calculations, precise time handling, tabular data manipulation, and astronomical image processing. Use it for implementing or debugging astronomical data analysis code.

A comprehensive Python command-line toolkit for processing and visualizing high-throughput sequencing data including ChIP-seq, RNA-seq, and ATAC-seq. Performs quality control, normalization, sample comparison, and generates publication-quality genomic visualizations with bigWig conversion and heatmap profiles.

A comprehensive toolkit for medical device manufacturers seeking ISO 13485 certification. This skill guides users through gap analysis, creating mandatory Quality Management System (QMS) documentation, and ensuring compliance with global medical device regulations (e.g., EU MDR, FDA QMSR). It provides templates, guidance, and structured processes for foundational QMS documents like Quality Manuals and CAPA procedures.

Build or audit reproducible research workflows with NeuroKit2 for physiological time-series: preprocessing, event/interval analysis, multimodal alignment, variability and complexity on ECG, RSP and EDA signals.

End-to-end analysis toolkit for Neuropixels extracellular recordings using SpikeInterface. Supports loading SpikeGLX/Open Ephys/NWB data, preprocessing, drift correction, spike sorting with Kilosort4 and other algorithms, quality metrics computation, and AI-assisted unit curation for publication-ready neural data analysis.

A Python library for reading, querying, filtering, and writing genomic file formats including SAM/BAM/CRAM, VCF/BCF, FASTA/FASTQ, and tabix data. Supports pileup, coverage analysis, indexing, and CRAM reference operations with HTSlib integration. Ideal for bioinformatics workflows requiring low-level streaming access to genomic data.

A Python library for building and analyzing bounded process-based discrete-event simulations. Supports events, resources, interrupts, and monitoring for modeling queues, logistics, and service operations with reproducible output analysis and validation.