Skill UI

Provides comprehensive guidelines for meeting modern academic publishing standards, particularly those required by educational psychology journals. It outlines mandatory requirements for ensuring research reproducibility, including depositing data, materials, and analysis code in trusted repositories with persistent identifiers (DOIs), adhering to JARS reporting standards, and documenting preregistration status.

This comprehensive guide assists in executing and documenting rigorous empirical finance research, tailored for top-tier journals like JFQA. It covers the entire pipeline, including complex financial data construction (CRSP, Compustat), advanced data cleaning (winsorizing, outlier handling), and sophisticated econometrics (fixed effects, clustered standard errors, Fama-MacBeth). The core focus is achieving absolute reproducibility, ensuring that all filters, robustness checks, and analytical steps are meticulously documented for archival and peer review.

A comprehensive guide to ensuring the quantitative rigor and full reproducibility of econometrics manuscripts, particularly for submissions to high-tier journals like QE. Covers structural estimation (GMM, MSM), moment construction, data cleaning best practices, and comprehensive validation checks (e.g., multi-start, Monte Carlo recovery). It ensures that all results can be regenerated from raw data using pinned environments and documented scripts.

A comprehensive guide to ensure scientific manuscripts meet the highest standards of reproducibility. It mandates documenting the deposition of all necessary data (e.g., GenBank, PDB, GEO), archiving custom analysis code with DOIs (e.g., Zenodo), and detailing sharing plans for unique reagents and materials. Crucial for primary data underlying research conclusions.

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.

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.

Creates task-oriented technical documentation with progressive disclosure for READMEs, API docs, architecture docs, and markdown. Also generates formal security reports (reverse engineering, penetration testing, CTF, malware/APT analysis) in the user's project directory with Evidence-Finding-Path chain.

This skill parses standardized C-CDA/CCD XML clinical documents exported from EHRs. It extracts both human-readable section narrative text and coded entries, mapping them by LOINC codes and template IDs. It is essential for preparing clinical free-text data for subsequent NLP analysis and supports XML-aware de-identification while preserving document structure.

Turn interpretive-ambiguity audits of legal texts into polished deliverables: multi-page websites, single-page interactive sites, Word documents, PowerPoint decks, or LaTeX/PDF. Handles structured stress-test output, semi-structured reports, or raw prose, and renders consistent scenario-based legal analysis presentations.

Expert French legal assistant for legal research, consultations, contract and document analysis, drafting, counter-arguments, reference verification and harmonization. Supports French, EU and European convention law and produces structured conversational answers or Word documents. Includes a strict anti-hallucination citation protocol.