Skill UI

This guide outlines the rigorous process for authors responding to a "revise" verdict at top-tier academic conferences. It teaches authors how to treat reviewer feedback as a formal contract, manage a tight revision timeline, structure necessary experiments, and write a highly traceable change document that explicitly addresses every required point.

A comprehensive guide for authors submitting to WACV, detailing the strategic differences between the optional one-page Round 1 rebuttal and the mandatory change summary accompanying a Revise-and-Resubmit. It provides best practices for addressing reviewer concerns, maintaining anonymity, and accurately documenting revisions.

A comprehensive guide for ensuring the scientific reproducibility of research papers that rely on dynamic web data, such as web crawls, platform APIs, or live system traffic. It defines three reproducibility regimes (Frozen, Decaying, Unreplayable) and provides detailed instructions on documenting data decay, temporal splits, environment configurations, and computational seeds to maintain the rigor of published findings.

This guide outlines the mandatory requirements for authors publishing quantitative or mixed-method research in academic journals. It details the process of creating a complete replication package—including raw data, master scripts, seeds, and extensive README documentation—and depositing it in a trusted data repository (like Dataverse). It ensures scholarly rigor by guaranteeing that published findings are fully reproducible and traceable.

This skill guides researchers in transforming raw empirical findings into robust, portable, field-level theoretical contributions suitable for top-tier journals. It provides a structured framework for elevating results by defining explicit communication mechanisms, establishing scope conditions, and articulating observable implications. It ensures the work contributes to the core body of theoretical communication knowledge, moving beyond simple effect documentation.

This skill helps developers and QA engineers transform vague, intermittent, or poorly documented bug reports into a minimal, reproducible, and evidence-backed brief. It guides the user to isolate the exact failure conditions, separate expected outcomes from actual errors, and reduce the issue to the smallest safe failing fixture, ensuring diagnosis occurs before premature repair.

This skill automates the creation of comprehensive Model Cards for OpenMed clinical NER or de-identification models. It gathers data from various OpenMed evaluation reports (e.g., `GateReport`, `fairness_report`, `error_report`) to structure critical governance information, including intended use, quantitative performance metrics, subgroup analysis, limitations, and required medical-device disclaimers. It ensures documentation is reproducible and compliant for clinical AI deployment.

This skill converts unstructured documents (PDF, EPUB, DOCX, etc.) into actionable, structured agent skills. Instead of providing simple summaries, it deep-dives to extract core frameworks, mental models, key principles, techniques, and anti-patterns from educational material. Use this when you need to transform academic knowledge into a reusable, machine-readable toolkit for agents like Copilot CLI, Amp, or Claude Code.

This skill maps chronic conditions extracted from clinical notes to CMS-HCC V28 categories. It estimates the Risk Adjustment Factor (RAF) score, which is crucial for Medicare Advantage payments. It serves as decision support for coders by identifying candidate HCCs, applying complex coding hierarchies, and checking for MEAT documentation evidence for human validation.

A specialized skill for analyzing the discrepancy between static Kubernetes/Compose manifests and the actual live operational state of a container runtime. It traces complex dependencies, such as secrets consumption, sidecar/init container modifications, network routing, and mounted volume paths. Use this when the core challenge involves proving how the live execution path differs from the documented intent, forming a minimal, verifiable evidence chain.

This specialized skill is designed to analyze complex security flaws involving forced authentication (coercion) and identity relay chains. It guides users to trace and document the complete, minimal path from the initial coercion trigger (e.g., a specific service or protocol edge) through intermediate hops, to the final service that accepts the relayed credentials. Use this for deep security research, forensics, and documenting the decisive sequence leading to privilege escalation or code execution.

This advanced skill specializes in detecting and recovering hidden data (steganography) embedded within various media containers, including images, audio, video, and documents. It is crucial for forensic analysis and CTF challenges, allowing users to inspect metadata, LSBs, alpha/palette channels, and appended trailers to reconstruct hidden payloads without brute force.