Skill UI

This utility serves as a stub experiment generator, primarily for demonstration purposes within meta-paper writing workflows. Given a specific topic phrase, it deterministically produces a consistent CSV file containing simulated experimental results. This function allows users to prototype and test complex paper structures and formats without needing access to real scientific data.

This toolkit provides deterministic, programmatic capabilities for working with PDF documents. Users can extract structured data like text and tables, merge multiple PDFs, split documents by page ranges, fill out PDF form fields using JSON data, or generate entirely new PDFs from scratch using libraries like ReportLab. It is designed for explicit, byte-level operations, making it ideal for automation workflows that require precise document manipulation.

An internal tool designed to validate candidate meta-skills (SKILL.md) before registration. It performs comprehensive structural linting (G1 rules) and a scheduler dry-run (G2) to ensure the workflow is deterministic, references are correct, and templates resolve properly. It outputs detailed JSON diagnostics.

A utility to generate a static, centered title or end card PNG image. It accepts a main headline, an optional subtitle, background color, and dimensions. It utilizes Pillow, ensuring deterministic rendering suitable for video intros and outros. Supports CJK character wrapping and provides robust font fallbacks, making it reliable for media production pipelines.

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.

A crucial post-hoc review skill designed for AI-generated Markdown prose. It audits content against a comprehensive set of 21 stylistic and structural rules using both deterministic mechanical checks and deep semantic judgment. Users can audit a single file to get a detailed scorecard, or compare two drafts (A/B) for regression testing. Furthermore, it can optionally generate a polished, revised copy in a separate file, ensuring the integrity of the original document while significantly improving linguistic quality and adherence to style guides. Ideal for refining AI-written documentation, ...

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.

Stratus Red Team is an open-source tool designed for detection engineering and purple teaming. It programmatically detonates granular, documented offensive techniques across major cloud platforms (AWS, Azure, GCP, and Kubernetes). It validates existing security detections (like CloudTrail/GuardDuty) by simulating real attacker activity in a controlled, reversible environment. The tool ensures deterministic lifecycles, guaranteeing that all created resources are cleaned up after testing.