Skill UI

This powerful tool calculates detailed cryptocurrency tax obligations by processing transaction history from multiple exchanges. It supports various cost basis methods (FIFO, LIFO, HIFO) to accurately determine capital gains and losses. Key features include identifying staking, airdrop, and mining income, and generating comprehensive reports compatible with Form 8949, simplifying complex tax filing processes.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

This tool applies advanced role mining techniques (including clustering and formal concept analysis) to analyze existing user-permission assignments. It discovers optimal, minimal role sets for Role-Based Access Control (RBAC) systems, helping organizations mitigate 'role explosion' and enforce the principle of least privilege. Ideal for identity governance and access control architecture redesign.

This skill detects covert data exfiltration occurring over DNS tunnels. It analyzes DNS query names by computing Shannon entropy, examining query length distributions, and identifying high subdomain cardinality. It is essential for security operations, incident response, and proactive threat hunting when suspicious or encoded DNS activity is suspected.

This skill automates the process of renting, managing, and terminating cloud GPU instances on vast.ai. It analyzes your AI/ML task—determining VRAM, required GPU count, and estimated runtime—and then searches for the most cost-effective and reliable GPU offers, presenting optimized options for your training workflow.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

A practical decision framework for determining whether to use regular expressions or Large Language Models (LLMs) when parsing structured text (e.g., forms, quizzes, invoices). It recommends starting with deterministic, low-cost regex for 95-98% of structured data, and only invoking expensive LLM calls for identifying and validating ambiguous edge cases, optimizing both cost and accuracy.

A comprehensive guide for authors submitting papers related to web search, ranking, and data mining to WSDM. It provides critical advice on conference fit assessment, proper manuscript re-framing, required evidence (retrieval/ranking baselines, platform-aware evaluation), and how to position the work against competing top venues like SIGIR and KDD.

A comprehensive guide for authors submitting data mining papers to the ACM SIGKDD Conference. It helps authors assess the manuscript's fit, reframe the contribution (e.g., from journal to conference style), and strengthens the evidence bar by focusing on novelty, scale, reproducibility, and clear real-world impact required by top-tier data science venues.

A comprehensive guide for designing rigorous and robust empirical programs in Information Retrieval, Data Mining, and Knowledge Management. It outlines how to meet the stringent evidence requirements for top-tier conferences, ensuring claims are substantiated across multiple evaluation dimensions (retrieval, mechanism, scalability) and incorporating best practices for reproducibility and handling modern challenges like LLM contamination.

This guide details the complex requirements for writing the 'Related Work' section for CIKM submissions. It emphasizes the need for 'three-front coverage' (Retrieval, Mining, and Knowledge Management/Databases) and provides specific instructions on writing the 'boundary-work paragraph.' It also covers critical academic hygiene, such as avoiding venue misattribution, maintaining recency baselines, and accurately stating novel contributions.

This guide helps researchers assess if their work correctly spans the tri-community area of Information Retrieval, Data Mining, and Knowledge Management. It provides a detailed framework for comparing CIKM against major academic venues like SIGIR, KDD, WSDM, and SIGMOD/VLDB, ensuring the paper's scope is optimally matched to the target conference.