Skill UI

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 provides an in-depth analysis of the CIKM peer review process, which operates under double-blind rules and integrates perspectives from three distinct fields: Information Retrieval, Data Mining, and Knowledge Management. It details how authors can optimize their submissions to satisfy diverse expert standards, how to interpret complex, multi-faceted review reports, and the critical dynamics that determine a paper's acceptance status.

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.

This comprehensive guide provides expert advice on structuring and polishing research manuscripts for multi-disciplinary academic venues like CIKM. It details how to satisfy reviewers from Information Retrieval, Data Mining, and Knowledge Management simultaneously, covering the optimal structure for the opening page, vocabulary discipline, budget compression, and effective title optimization.

A comprehensive guide for packaging and submitting artifacts for Information Retrieval (IR) conferences (like SIGIR). It details the strict requirements for reproducibility, including mandatory components like TREC-format run files, qrels, and index recipes. The process covers repository structure, anonymization for review, handling data provenance, and ensuring that results can be audited by any reviewer using standard community tooling.

A comprehensive guide for ensuring the reproducibility of Information Retrieval (IR) research, essential for SIGIR submissions. It details best practices for pinning data collection versions, index settings, neural model hyperparameters, and managing non-determinism. It helps researchers document artifacts—not just promises—to validate reported gains.

This skill implements an advanced reference pattern for integrating with Monitoring Control Platform (MCP) servers. It ensures accurate data retrieval by first reading a domain-specific 'gotchas' reference file, mitigating query failures caused by complex server behaviors, pagination limits, or subtle API quirks. It analyzes issue data, groups errors by root cause, and generates a structured, actionable markdown report for improved system health visibility.

This utility provides a straightforward mechanism for reading the contents of specified files and returning the data. It is ideal for quick file inspection, data validation, or integrating basic file content retrieval into automated scripts and workflows.

A read-only skill designed to connect with Microsoft Outlook (Office 365/Exchange) via OAuth2. It allows users to read, search for, and download emails and their attachments (such as NDAs, contracts, or documents). Ideal for automating workflows that require accessing email content, performing keyword searches, or chaining email data into other specialized skills for review and analysis.

This Rust SDK provides a client library for Azure Key Vault Secrets, enabling secure storage and retrieval of passwords, API keys, and other sensitive data. It supports core operations like getting, setting, updating, deleting, and listing secrets. Users can manage secret versions and properties using Entra ID authentication. Ideal for developers building secure applications on Azure infrastructure.

Management plane SDK for provisioning and managing Azure Cosmos DB resources via Azure Resource Manager. Covers account creation, SQL databases, containers, throughput configuration, and key retrieval using .NET.

Clarity Gate is a pre-ingestion verification system designed for Retrieval-Augmented Generation (RAG) workflows. It ensures documents entering knowledge bases are properly qualified by checking for uncertainty markers and distinguishing assumptions from facts. This skill produces Clarity-Gated Documents (CGD) and validates Source of Truth (SOT) files. It helps prevent hallucinations by enforcing epistemic quality standards before data ingestion. Note that it verifies document form rather than factual truth, requiring human-in-the-loop validation for accuracy.