Skill UI

Perform bounded, local exploratory analysis on supported scientific files like CSV, JSON, and HDF5. Inspect metadata, check missingness, and audit outliers without executing code or accessing networks. Generates deterministic reports for data validation before modeling.

Geniml validates genomic interval workflows, BED files, and model compatibility for ML/statistical analysis. Plan Region2Vec/scEmbed runs, inspect tokenizers, and assess consensus universes locally without network requests. Requires Python 3.10+ and uv for reproducible bioinformatics ML pipelines.

Create, analyze, and visualize complex networks in Python with NetworkX 3.x. Build graphs from data, run algorithms like shortest paths, PageRank and centrality, detect communities, generate synthetic networks, and read/write GraphML, JSON or edge-list files.

A comprehensive, advanced playbook for conducting deep network traffic analysis and PCAP forensics. Covers essential skills like PCAP repair, advanced Wireshark filtering (HTTP, DNS, TLS), data extraction (file carving, credential harvesting), and detecting covert channels. It guides users through sophisticated techniques, including TLS decryption workflows and DNS tunneling identification.

A comprehensive guide for structuring and defending the methodological design of social science manuscripts, particularly for high-impact journals like AJS. It covers diverse traditions—including quantitative, comparative-historical, ethnographic, and network analysis—ensuring the design rigorously supports the theoretical claim, rather than just presenting data. Use this when justifying case selection, identifying assumptions, or linking field observations to major theoretical arguments.

This guide outlines best practices for ensuring the scientific reproducibility and artifact availability of network measurement research. It distinguishes between analysis reproducibility (recreating figures from released data) and method reproducibility (re-running the measurement). It mandates thorough documentation of provenance, including vantage points, timing, and tool versions, ensuring research claims are verifiable and robust.

This skill provides comprehensive guidelines for submitting manuscripts to the journal *Science of Science and Management of S&T*. It helps researchers assess the article's fit, structure, required evidence (e.g., patent quality, network analysis), and common pitfalls associated with technology policy and innovation systems research, ensuring academic rigor.

NetworkX is a powerful Python package designed for creating, manipulating, and analyzing complex networks and graphs. It supports various graph types (Directed, Undirected, MultiGraphs) and provides extensive functionality for graph algorithms, including shortest path calculation, centrality measures (like PageRank), community detection, and connectivity checks. Users can generate synthetic networks, read/write data from multiple formats (GraphML, CSV, Pandas), and visualize results using Matplotlib. Ideal for social network analysis, biological research, and system modeling.

A comprehensive, multi-phase workflow guide for authorized reverse engineering of complex network protocols. Ideal for analyzing proprietary formats such as custom TCP/UDP binary protocols, Protobuf, gRPC, WebSocket frames, and reconstructing state machines from raw PCAP data. This guide covers data collection, field layout restoration, serialization recovery, and secure replaying.

JurisRank is a computational tool that analyzes citation networks of Argentine Supreme Court (CSJN) jurisprudence using a peer-reviewed PageRank algorithm with temporal decay. It measures the jurisprudential authority of legal precedents, allowing users to rank competing cases by influence, trace the evolution of legal doctrine, and detect potential constitutional drift. Ideal for intensive legal research, due diligence, and academic analysis of legal trends in Argentina.