Skill UI

A persistent knowledge base that transcends standard RAG by integrating three core layers: raw source storage, a structured knowledge graph, and a dynamic wiki layer. It ingests documents once, automatically extracting entities and relations to build a comprehensive, cross-referenced, and verifiable knowledge graph. Ideal for enterprise research, complex Q&A, and deep knowledge accumulation.

This comprehensive guide details advanced techniques for circumventing Content Security Policies (CSP). It covers exploiting weaknesses in critical directives (e.g., base-uri, object-src), abusing nonces, leveraging trusted CDN endpoints, and executing code despite strict controls like script-src 'self' or 'strict-dynamic'. Essential knowledge for deep security auditing and penetration testing.

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 comprehensive skill designed to process and organize incoming emails. It classifies messages using a predefined user taxonomy, researches new senders, generates decision recommendations, drafts replies (without sending), and updates a knowledge base with learnings. Ideal for running on a recurring schedule (1-3 times daily) or on demand to maintain an organized inbox.

This skill automatically enriches research wiki pages that are left as bare scaffolds after initial ingestion. It reads multiple external sources (like arXiv/alphaxiv) and fills in 10 structured `_TODO._` sections (e.g., Problem/Gap, Key Results, Open Questions) with detailed prose summaries. Use this skill to transform incomplete research scaffolds into a comprehensive, structured knowledge base.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

This tool indexes local files or directories into a persistent FTS5 knowledge base. This allows subsequent search operations to efficiently retrieve highly relevant, focused snippets from the project source without needing to re-read the raw files every time. It is crucial for managing and searching large, complex codebases or detailed project documentation.

This tool accesses a persistent FTS5 knowledge base containing previously indexed project documentation, local content, and session memory. Instead of feeding entire raw sources into the conversation context, it allows users to perform targeted, efficient searches using specific queries and source scoping. This is ideal for quickly retrieving technical details, project specifications, or historical context without lengthy re-reading.

This meta-skill provides a complete pipeline to bootstrap a domain knowledge base from any single seed input (URL, PDF, Git repository, or free text). It automatically classifies the source type, ingests the content using a multi-search engine, persists the summary to memory, and finally generates a structured XLSX index file, making the information immediately searchable and usable.

A comprehensive workflow designed to convert unstructured conversational data, meeting notes, and ad-hoc decisions into highly structured, linkable entries within Notion. This tool ensures that all captured knowledge—whether an FAQ, a 'how-to' guide, or a formal decision log—is properly documented, categorized, and linked to maintain a cohesive, searchable knowledge base for easy reuse across teams.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

This skill provides advanced connection-discovery and semantic search across your personal Apple Notes. It moves beyond simple keyword matching to find related ideas, identify non-obvious 'bridges' between concepts, and synthesize comprehensive summaries of your thoughts. Ideal for knowledge workers looking to recall, analyze, or map their personal knowledge base.