Skill UI

Automates elasticsearch index manager activities inside advanced DevOps workflows, offering best-practice guidance, code/configuration generation, and validation for kubernetes, terraform, ci/cd, monitoring, and IaC needs.

This guide details the deployment and configuration of MISP (Malware Information Sharing Platform) using Docker. It demonstrates how to aggregate, correlate, and distribute threat intelligence from diverse sources like abuse.ch and AlienVault OTX. Learn automated feed synchronization, STIX/TAXII integration, and connecting the platform to SIEM and SOAR tools like Splunk and Elasticsearch for centralized IOC management.

This skill implements User and Entity Behavior Analytics (UEBA) using Elasticsearch/OpenSearch. It goes beyond static rules to build behavioral baselines (e.g., login times, data volume) for users and entities. By calculating anomaly scores and performing peer group analysis, it detects suspicious activities such as data exfiltration, privilege abuse, and unauthorized access, allowing organizations to proactively identify high-risk insider threats.

This skill guides the setup of a robust, centralized logging pipeline using Fluent Bit and Fluentd. It covers collecting logs from diverse sources (syslog, files, applications), routing, filtering, and enriching them before sending them to destinations like Elasticsearch, S3, or Splunk. Ideal for centralizing observability and building secure logging architectures across distributed infrastructure.

A comprehensive toolkit for building robust, structured logging pipelines in Golang. It provides advanced modules for handler composition (slog-multi), throughput control (slog-sampling), attribute transformation (slog-formatter), and integrates with major backend logging sinks (e.g., Sentry, Loki, Datadog, Elasticsearch). Use this when building complex Go applications that require deterministic log routing, sampling, and data sanitization.

A comprehensive guide for migrating search functionality from alternatives like Elasticsearch, Typesense, or Meilisearch to Algolia. It details the necessary steps, including data migration, query translation (e.g., ES to Algolia), and implementing a zero-downtime swap using the Strangler Fig pattern.

A comprehensive technical guide detailing the process of migrating large-scale enterprise search platforms from sources like Elasticsearch or Algolia to Glean. It covers data export, schema transformation, using bulk indexing APIs, creating data sources, and validating search quality for smooth, large-scale deployment.

A comprehensive guide detailing the complete academic review pipeline for IEEE/ACM Automated Software Engineering (ASE) submissions. It explains unique stages like the early-rejection gate, the criteria-bound revision process, and the difference between ASE's cycle and those of peer conferences (e.g., ICSE, FSE). Essential reading for authors aiming to submit high-quality research in automatic software engineering.

Provides comprehensive guidance for researchers deciding the optimal conference venue for Software Engineering (SE) research. It distinguishes between contributions that focus on *automation* (techniques or tools that automate SE tasks) suitable for ASE, versus those that are broad empirical findings, theoretical results, or pure ML advances, directing them to venues like ICSE, FSE, or ISSTA.

A comprehensive guide for authors submitting to top-tier Software Engineering (SE) conferences (like FSE, ICSE). It details how to structure the Related Work section by focusing on the 'delta'—the unique difference—between your work and prior art. It emphasizes covering the full SE literature landscape, maintaining anonymity, and correctly declaring overlaps with concurrent or prior versions.

This guide helps researchers determine the optimal academic venue for their software engineering work. It compares major conferences (FSE, ICSE, ASE, ISSTA) and journals, advising on contribution shape, evidence maturity, and community fit to maximize publication chances and fit the research into the correct academic community.

This guide details the rigorous process of preparing research artifacts for formal evaluation at major academic conferences like ICSE. It covers achieving ACM badges (Available, Reusable, Reproduced/Replicated) by ensuring proper DOI archiving, open licensing, robust documentation, and verifiable reproducibility for external reviewers, transforming raw research into publishable, trustworthy assets.