Skill UI

A robust toolkit for deterministic, structural PDF manipulation. This skill allows users to perform programmatic tasks such as extracting text and tables from reports, merging multiple PDF files, splitting documents by page ranges, filling out form fields, or generating entirely new PDFs from structured data (e.g., JSON). It is designed for explicit, byte-level operations, distinguishing it from LLM-based natural language rewriting tools.

An internal tool designed to validate candidate meta-skills (SKILL.md) before they are registered in the system. It performs comprehensive linting checks, including structural validation (G1: parsing, reference checks, XML escaping) and a scheduler dry-run (G2) using stub executors, ensuring the skill is deterministic and functional. Output diagnostics in JSON format.

This skill provides a deterministic, git-only method to verify claims made by AI agents (e.g., 'done', 'shipped', 'fixed'). Instead of trusting the agent's self-report, it audits the commit's diff and git history using the DOS kernel (`dos verify`, `dos commit-audit`) to confirm if the claimed change genuinely occurred and is backed by non-forgeable evidence. It acts as a critical quality gate.

This skill provides a rigorous, multi-layered audit for User Interfaces, proving layout balance and usability beyond subjective judgment. It combines a deterministic spatial audit (calculating centroid, optical-center, and pixel-oracle balance via explicit math) with a professional usability review based on Nielsen's 10 heuristics, conducted by a separate 'fresh-eyes' judge. Ideal for catching misaligned or poorly structured AI-generated designs.

This skill outlines a systematic, multi-phase workflow for diagnosing hard bugs and performance regressions in complex codebases. It emphasizes the critical necessity of building a tight, deterministic feedback loop (Phase 1) before moving to hypothesis generation. The process guides users through reproducing the failure, minimizing the scope of the issue, and forming actionable, ranked hypotheses.

A comprehensive guide for defense contractors handling Controlled Unclassified Information (CUI). It covers preparing for CMMC Level 2 certification, implementing 110 NIST SP 800-171 controls, calculating the SPRS score using DoD methodology, and managing the POA&M and SSP. Essential for DFARS compliance and external assessments.

This methodology guides organizations through a comprehensive cybersecurity risk assessment using the NIST SP 800-30 framework. It systematically identifies threat sources, vulnerabilities, and potential threat events. By determining the likelihood and impact of these threats, the process calculates overall risk, resulting in a prioritized, defensible risk register. Essential for compliance (e.g., ISO 27001, HIPAA), system authorization, and strategic risk reporting.

A comprehensive guide to executing the NIST Risk Management Framework (RMF) to achieve an Authorization to Operate (ATO). This covers the full lifecycle, including preparation, system categorization (FIPS 199), control selection and tailoring (SP 800-53 Rev 5), implementation, assessment (SAR), and continuous monitoring. Essential for organizations working under FISMA or FedRAMP.

A comprehensive guide to achieving compliance with the HIPAA Security Rule (45 CFR Part 164). This skill outlines the mandatory steps to protect electronic Protected Health Information (ePHI), including conducting thorough risk analyses, deploying administrative, physical, and technical safeguards, managing Business Associate Agreements (BAAs), and preparing for breach notifications and OCR audits. Essential for Covered Entities and Business Associates.

This skill guides the implementation of dynamic secrets using HashiCorp Vault. It focuses on generating short-lived, ephemeral credentials for services like databases (PostgreSQL, MySQL), AWS IAM, and PKI certificates. By automating credential generation, lease management, and rotation, it eliminates the risk associated with storing long-lived static secrets, ensuring compliance with zero-trust security models (PCI-DSS, NIST).

Specialized skill to rewrite and humanize AI-generated natural language files (like notes or documentation). It removes common 'AI-isms' such as sycophantic openings, stock vocabulary, over-explaining, and excessive signposting. Users can choose between a fast, deterministic regex cleanup or a deep LLM rewrite (default mode) using Claude to enhance burstiness and match a natural human voice, all while strictly preserving code blocks, URLs, and headings.

This skill functions as an optional agent companion for MDPR, a deterministic Markdown-to-presentation runtime. It helps review presentation workflows by providing semantic hints, visual evidence reports, and high-level layout intentions. Crucially, it keeps agent output evidence-based while letting MDPR maintain control over final slide geometry, rendering details, and layout coordinates.