Skill UI

A comprehensive guide for preparing replication and transparency packages for World Development manuscripts. It provides checklists and best practices for both quantitative (code reproducibility, data provenance, de-identification) and qualitative (evidence-to-claim trail, methods appendix) research. It addresses ethical constraints and the unique challenges of double-anonymized review.

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.

This skill audits a complete security or forensics case package to ensure its readiness for formal report handoff. It verifies scope metadata, evidence record integrity, traceability between Findings, Paths, and Evidence, and checks artifact hash consistency. It is a non-destructive, read-only review process designed to validate the defensibility and completeness of the investigation before documentation.

This skill processes raw, report-only optimization observations generated by Caveman tools. It guides the user through an evidence-first, rigorous evaluation process, requiring explicit operator choice, designing a minimal candidate change, and performing a paired baseline evaluation using identical fixed inputs. It ensures that any proposed code change is validated against quality checks and established performance metrics before implementation.

This skill provides a critical audit layer governing the transition of AI-generated legal output from analysis to real-world consequence. It mandates a rigorous check of evidence sufficiency, institutional authority, procedural adherence, and visible uncertainty before any output can become a reliance, recommendation, or executed commitment. It is essential for high-stakes legal workflows, such as contract review, compliance findings, or drafting final advice.

This skill forces the AI to operate in a non-inferential mode, ensuring every claim—factual, legal, or numerical—is strictly anchored to user-provided materials or verified online sources. It eliminates assumptions, gap-filling, and background knowledge reliance, making it essential for legal research, evidence review, citation checking, and high-stakes document analysis where evidential fidelity is paramount.

This skill governs the cluster-level resolution of complex defect backlogs. It moves beyond surface fixes by identifying and resolving the true causal root of issues. The methodology mandates rigorous evidence checking, creating read-only mechanism maps, and prioritizing destructive fixes (deletion-driven) to ensure clean, verifiable code state and prevent compatibility debt.

Reviews a pull request and posts one consolidated GitHub review with inline comments via the gh CLI. Runs the repo's own checks first, verifies external claims against official docs, then reports correctness, security, and structural findings—each backed by evidence and gated for noise.

This skill helps maintainers verify reported bugs in the mcp-toolbox project. It involves analyzing issue reports, checking versions and configurations, and attempting local reproduction to determine if the bug is real, fixed, or misconfigured. The process requires citing specific evidence like file paths and commit SHAs to support verdicts such as confirmed, already-fixed, or client-side issues. It ensures efficient bug triage without unnecessary infrastructure setup.

Close defects and incidents with a structured BUG RECEIPT output including VERIFIED, PARTIAL, or BLOCKED status. Establishes evidence boundary, traces root cause, makes responsible changes, and closes the proof loop with explicit checks and gaps.

Guide for revising manuscripts and preparing point-by-point responses for Journal of Systems Engineering (Tianjin University), covering issue classification, revision actions, evidence-based replies, and consistency checks.

A skill for designing evidence and validation strategies for academic manuscripts submitted to the Journal of Systems Engineering. It covers standards for numerical experiments, simulations, real data, case studies, and empirical research, ensuring claims are supported by appropriate evidence rather than generic robustness checks.