Skill UI

This skill guides the comprehensive process of crash triage, enabling the assessment of vulnerability exploitability. It covers advanced techniques using debuggers (WinDbg/GDB), sanitizers (ASAN/MSAN), and interpreting low-level data like stack traces and register dumps. It is essential for vulnerability researchers analyzing crash dumps or fuzzer-generated failures to identify root causes and potential attack vectors.

This skill establishes an autonomous, iterative build loop for continuous website development. It functions as an agent that reads tasks from a 'baton' file, utilizes the Stitch MCP tools to generate structured web pages, integrates the generated HTML assets into the main site structure, and finally updates the baton file with the next required task. It supports end-to-end, programmatic site building and includes optional visual verification using Chrome DevTools.

This skill takes large plans, specifications, or Product Requirement Documents (PRDs) and systematically breaks them down into small, independently verifiable, and actionable GitHub issues. Utilizing the 'vertical slice' (tracer bullet) methodology, it ensures that each generated task cuts through all integration layers (UI, API, Schema, Tests) end-to-end. It manages dependencies, identifies required human interaction (HITL) versus autonomous implementation (AFK), and prepares a structured, executable backlog ready for immediate ticketing.

unslop is a powerful command-line interface (CLI) tool designed for post-processing text. It programmatically strips out common and identifiable patterns associated with AI-generated writing. Use it as a final, deterministic pass before publishing documentation, blog posts, or any content derived from large language models (LLMs), ensuring a more natural and human-like tone.

MockHunter is an advanced skill designed to audit live web pages, providing a comprehensive reality check on visible data. It analyzes every element to determine its true source: whether it is REAL (from the database), MOCK (simulated data), LLM-generated, HARDCODED, BROKEN (broken endpoints), or UNKNOWN. Ideal for auditing AI-generated UIs or reviewing proof-of-concept MVPs before customer sign-off.

StyleSeed is a comprehensive design judgment engine designed to elevate the aesthetic quality of AI-generated user interfaces. It codifies 69 visual rules covering color discipline, spatial rhythm, information hierarchy, and motion feedback. By integrating these principles, developers can ensure that AI output looks professionally designed and polished, rather than simply generated. Includes React components, Tailwind CSS implementation, and actionable design guidelines.

This powerful router analyzes complex document types (scanned, medical, table-heavy, etc.) and determines the optimal conversion pipeline (tools, flags, cleanup) to extract data into various formats like Markdown, JSON, or DOCX. It goes beyond simple text extraction by prioritizing the preservation of document structure, relational data, and high fidelity, ensuring the generated output is accurate for downstream machine processing.

This skill guides the creation and maintenance of a `.geminiignore` file. By systematically filtering out machine-generated noise—such as build artifacts, dependency lock files, logs, and caches—it significantly optimizes the AI agent's context window. This process improves processing speed and, critically, reduces token consumption costs (FinOps).

This skill serves as a compatibility demonstration to verify that the Hermes Agent can successfully parse and load an SKILL.md file generated from an MDA source. It confirms that the compiled structure, including specific frontmatter and reserved metadata namespaces, survives the round-trip compilation process, ensuring structural integrity for the agent's skill registry.

This guide provides a systematic, safe workflow for refactoring rapidly developed or AI-generated fullstack applications. It focuses on identifying and removing dead code, fixing broken imports, and consolidating repetitive logic while strictly maintaining existing API contracts, routes, and core functionality. Use this before launch to ensure a stable, maintainable production baseline, following a 'surgery, not demolition' philosophy.

This skill generates highly structured, multi-shot shooting scripts from a given topic and style. It outputs a machine-parseable shot list, including detailed image prompts, video prompts, voiceovers, and on-screen text for each segment. Ideal for planning and creating AI-generated short dramas, product ads, or educational videos.

This stub experiment generator is designed for the meta-paper-write demo. It takes a topic phrase and deterministically produces a 20-row CSV file containing baseline and generated results. This ensures stable and reproducible results for demonstration purposes, but should not be used for real scientific data.