Skill UI

A comprehensive guide for diagnosing and resolving slow search performance in Qdrant. This resource covers issues related to low QPS (throughput), high latency for single queries, and slow filtered searches. Solutions include tuning HNSW parameters, managing memory pressure, implementing quantization, optimizing indexing, and configuring cluster settings for maximum efficiency.

A dedicated skill for performing manual data synchronization and knowledge recall with OpenViking. It allows users to explicitly trigger chat transcript syncing from OpenClaw sessions and perform targeted memory searches via command line, without disrupting normal conversation flow or occupying the standard context engine slot.

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 fetches the top academic papers from the arXiv API in a specified category. It generates a structured digest for each paper, renders this digest into a professional PowerPoint (PPTX) presentation (one slide per paper), and finally saves the comprehensive digest to long-term memory for future reference. Designed for daily 'morning briefings' of academic research.

A comprehensive meta-skill for generating auditable compliance reports, suitable for frameworks like GDPR or SOC2 self-assessments. This bundle executes a multi-step process: deep research with citations, generating a signable .docx report, archiving a read-only .pdf version, and saving the evidence chain to memory for future retrieval. It ensures a complete, traceable audit trail.

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.

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 a hybrid, persistent, and searchable knowledge memory system specifically designed for enhancing AI agents' capabilities. It allows developers to record, retrieve, and search key knowledge, architectural decisions, and operational memories automatically. Use it to build agents with long-term context and reliable knowledge management, ensuring findings are synced back into project documentation.

This tool performs a deep, recursive, and parallel investigation, building a comprehensive graph-structured dossier around a specified seed entity. It expands outward across diverse sources, including web search, internal memory stores, codebases, and knowledge graphs. It captures all connected entities and the provenance of every claim, making it ideal for mapping the full context or relationship network of a subject.

This framework manages the ingestion, organization, synchronization, and search of knowledge across multiple storage layers, including local files, persistent memory, vector stores, and Git repositories. It provides a structured approach for ensuring knowledge durability, deduplication, and maintaining a 'single source of truth' across complex development and operational contexts.

This memory bridge ingests Claude Code's local auto-memory markdown files and structures them into AgentDB. It generates high-fidelity ONNX embeddings, handles deduplication using cosine similarity, and indexes the memories in a scalable namespace. This enables seamless, unified semantic search across all historical sessions and projects, significantly enhancing RAG capabilities.

A state-of-the-art memory search engine providing multi-faceted retrieval strategies. It supports default dense semantic matching, advanced hybrid search (sparse + dense with RRF), deep multi-hop Graph RAG for complex reasoning, and 'Smart' search utilizing query expansion, recency boosting, and MMR diversity reranking. Ideal for retrieving nuanced, diverse, and time-sensitive information from large, complex knowledge bases across multiple namespaces.