Skill UI

This skill provides comprehensive guidance and tools for end-to-end software release management. It automates critical tasks such as generating changelogs from conventional commits, determining semantic version bumps (Major/Minor/Patch), assessing release readiness, and planning complex deployment workflows (e.g., Git Flow). Ideal for teams adopting robust DevOps practices.

A comprehensive guide for managing secrets and environment variables throughout the entire development lifecycle, from local development to production. This skill covers secret leak auditing (detecting committed credentials), implementing robust rotation workflows, and integrating with industry-leading secret stores like HashiCorp Vault, AWS Secrets Manager, and Azure Key Vault. Essential for hardening CI/CD pipelines and ensuring compliance.

This guide outlines security best practices for managing Bright Data zone credentials, API tokens, and webhooks. It covers crucial steps like credential inventory, environment variable security, zone isolation across environments, scheduled credential rotation, and implementing pre-commit hooks to prevent sensitive data leakage, maximizing data security and compliance.

A comprehensive framework for ensuring rigorous scientific reproducibility in systems papers. It details how to establish a full provenance ledger (tracking hardware, software commits, and workloads), manage performance variance through repeated runs and statistical reporting, and implement automated pipelines so that all reported figures can be regenerated from raw data.

This guide outlines the rigorous methodology for ensuring that computational research results, especially those involving hardware architecture and simulation, are fully reproducible. It emphasizes pinning the entire result chain—from simulator commits and configuration files to workloads, sampling recipes, and statistics scripts—and mandates creating a manifest for every figure. Key practices include recording machine state for silicon runs and containerizing the environment to prevent data drift.

A comprehensive guide on achieving scientific reproducibility for complex computer architecture and system simulations. It details how to manage and pin all dependencies—including simulator commit hashes, local patches, workload binaries, hardware states, and post-processing scripts—to ensure that every reported figure and number can be accurately regenerated, regardless of time or environment changes.

A comprehensive guide for microarchitecture researchers on achieving rigorous scientific reproducibility. It mandates tracking every critical artifact—including simulator commits, workload traces, power/area models, and RTL toolchains—and compiling them into a single run manifest, ensuring any reported result can be verified by a third party.

A comprehensive guide for submitting research artifacts to the MobiSys Artifact Evaluation Committee (AEC). It details the process for achieving three independent ACM badges (Available, Functional, Results Reproduced). Emphasis is placed on creating fully reproducible, script-first workflows, designing for hardware-optional environments, and maintaining strict anonymity during review.