Skill UI

This workflow helps standardize and enhance meeting documentation by utilizing customizable templates, predefined recipes, and pre-meeting context. It is ideal for ensuring consistent, high-quality meeting notes across sales calls, standups, 1:1 check-ins, and strategic planning sessions, improving organizational knowledge capture.

This comprehensive workflow automates the entire meeting documentation process. It handles live or recorded audio input, performing accurate transcription with speaker diarization. Subsequently, it leverages advanced AI models to generate detailed summaries, extract key action items, draft follow-up emails, and create structured meeting minutes, significantly enhancing organizational knowledge retention and operational efficiency.

A robust internal utility for integrating system status updates with Feishu/Lark. It allows other skills to send structured notifications (e.g., experiment complete, checkpoint waiting, review scores) via webhooks. Supports both 'Push only' (fire-and-forget) and 'Interactive' (bidirectional polling) modes, guaranteeing zero side effects if configuration is missing.

This skill manages a persistent, structured personal knowledge base (Wiki). It provides core operations for ingesting external articles, synthesizing insights from conversations, and querying stored knowledge. By maintaining structured files, index lists, and cross-references, it helps build a comprehensive knowledge graph, ensuring all insights are captured and easily retrievable.

This guide details advanced techniques for searching cricket matches by leveraging precise numerical scoring data. Instead of broad terms, users should utilize specific details—such as team totals (e.g., 168/9), runs off balls, wickets taken, or over details—combined with the match type and year to create highly targeted search queries. This method significantly improves search accuracy and efficiency when researching sports statistics.

This skill transforms an LLM into a disciplined wiki maintainer for Obsidian. It moves beyond simple RAG by incrementally ingesting sources (papers, notes, articles) to build a persistent, interlinked "Second Brain." The LLM manages cross-references, updates core concepts, and maintains an evolving knowledge graph, ensuring that knowledge compounds over time rather than being re-derived on every query. Ideal for deep research, personal knowledge management (PKM), and building comprehensive internal wikis.

This is a mandatory, one-time setup skill designed to interview users interactively to build a personalized knowledge base for advanced email triage. Using a structured, discipline-driven interview process, the skill collects data on email patterns, business context, and priorities, generating 7 critical structured files (e.g., email-taxonomy.md, rate-card.md). This knowledge base powers the companion 'inbox-triage' skill. Run this before initial use or whenever core business processes change significantly.

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 conducts a 15-question interview to deeply capture your professional context, including your role, core topics, trusted sources, and preferred format. It then generates a comprehensive, reusable 'master prompt' that can be pasted into automated routines or scheduled tasks, ensuring you receive personalized, structured intelligence briefings without manual setup.

An interactive skill designed to interview users to capture project vision, core decisions, and technical preferences. It systematically converts raw discussions into structured documentation, maintaining the project's history in ADRs (Architectural Decision Records) and its overarching goal in the README.

This skill is designed to rewrite a previous, lengthy response into a significantly more concise version. It ensures that the core meaning and all essential information are preserved, making it perfect for users who need a quick summary or a TLDR (Too Long; Didn't Read) version without losing critical details.

This skill is designed to polish and refine academic manuscripts intended for the American Historical Review (AHR). It focuses on enhancing the clarity, narrative flow, and authoritative voice of the prose, ensuring the work is accessible and engaging for historians across all subfields. It tightens the overall readability, minimizes unnecessary jargon, and helps frame evidence without altering the core arguments or academic content.