Skill UI

This skill converts Microsoft Word documents (.docx) into Markdown format. It is essential for accurately analyzing, summarizing, or extracting data from proprietary Word files, as direct parsing is unreliable. It supports single files, batch processing of folders, and is designed to coordinate with sibling skills (PDF and Excel) when handling mixed document types to ensure no data is skipped.

A comprehensive guide for integrating Markstream, a high-performance Markdown renderer, into modern frontend frameworks like React, Vue, Svelte, and Next.js. It handles complex streaming scenarios, ensuring minimal peer dependencies, correct CSS ordering, and proper Server-Side Rendering (SSR) boundaries for dynamic content and AI chat interfaces.

Converts a research paper or PDF into an ARA-structured artifact, validates it, and ingests into OpenViking as a resource. Handles claims, evidence, figures, tables, and full document parsing from PDFs or URLs.

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 implements an advanced reference pattern for integrating with Monitoring Control Platform (MCP) servers. It ensures accurate data retrieval by first reading a domain-specific 'gotchas' reference file, mitigating query failures caused by complex server behaviors, pagination limits, or subtle API quirks. It analyzes issue data, groups errors by root cause, and generates a structured, actionable markdown report for improved system health visibility.

This skill automatically cleans up and prettifies Markdown documents. It fixes structural inconsistencies such as incorrect heading levels and normalizes various list styles. It is useful for ensuring consistent formatting across large markdown files, optimizing readability, and preparing content for documentation or publishing.

Displays a dashboard of all autoresearch experiments, showing results, active optimization loops, and progress. Supports per-experiment detail, domain filtering, and format export (Markdown/CSV) for sharing. Useful for monitoring ongoing experiments.

Converts books, documentation folders, and source collections (PDF, EPUB, DOCX, HTML, Markdown, RST, etc.) into structured agent skills. Extracts named frameworks, principles, techniques, and anti-patterns into a master SKILL.md plus chapter files, glossary, patterns, and a decision cheatsheet. Ideal for turning internal docs or standards into reusable knowledge bases or packaging compiled skills as plugins.

Generate structured Markdown notes from academic papers (PDF, URL, DOI) with deep analysis, argument reconstruction, and figure placeholders. Optimized for Obsidian vaults and research workflows, ensuring high-quality evidence-based summaries in multiple languages.

This workflow implements a bounded, iterative refinement loop for AI agents. It utilizes a required markdown rubric to grade the agent’s output in an isolated context. Based on the grader's verdict, it recommends the next move (sharpen, re-run, or promote). Once the agent passes the rubric, held-back evaluation cases are run in parallel to ensure generalization before final deployment.

A comprehensive guide for creating and maintaining an atomic, index-driven knowledge base (KB) using Markdown. This method enables AI agents to bypass traditional vector search limitations by reading a central index file (INDEX.md) and opening only the most relevant, highly signaled notes, drastically improving context efficiency.

This skill analyzes local Q&A session JSONL files to produce a complete, auditable weekly report in Markdown format. It supports generating routine weekly summaries, comparing performance across consecutive time windows, and identifying key trends and risks. The output is a single, self-contained Markdown file designed to provide sufficient aggregates and structure for downstream documentation rendering (e.g., Feishu or whiteboards), without needing external data sources or APIs.