Skill UI

A comprehensive skill designed for finance-related information retrieval. It provides dual search capabilities: real-time web searching across multiple engines (Jina, DDG, Baidu) for general market knowledge, and Retrieval-Augmented Generation (RAG) for querying local, proprietary document stores (e.g., daily news). Ideal for financial analysts needing both broad market context and specific internal data insights.

Provides techniques and indicators for researching music bands from non-Western cultures (e.g., African, Asian, Middle Eastern). This guide helps users move beyond mainstream Western databases, offering search strategies for bands whose information is often found on regional sites or through unique contextual clues like recording conditions or historical milestones.

This comprehensive workflow guides the implementation of a complete Retrieval-Augmented Generation (RAG) pipeline using Cohere's core services. It demonstrates the full process: embedding documents, searching for relevant context, reranking results for precision, and finally generating a grounded answer with precise citations using the Chat API. Ideal for building knowledge-intensive, enterprise-grade Q&A systems.

This workflow demonstrates the core capabilities of the Glean Client API, allowing users to build comprehensive knowledge assistants. It covers advanced full-text search with filters and facets, implements AI-powered chat for contextual Q&A, and provides autocomplete suggestions. Ideal for integrating deep search and conversational AI into enterprise applications.

Zero-context verification system that checks every number, comparison, and scope claim in research papers against raw result files. Uses a fresh cross-model reviewer with no prior context to prevent confirmation bias. Ideal for academic integrity, paper submission, and ensuring paper-to-evidence fidelity before publication.

A comprehensive tool for searching the Model Context Protocol (MCP) across various GitHub locations, including PRs, issues, and discussions. It allows users to track the full lifecycle of protocol specifications, from initial proposals to final merged changes. Furthermore, it facilitates deep dives into pull request reviews and comments, enabling the extraction of key maintainer rationales and design intent for accurate technical understanding.

Cavecrew provides three subagent presets—investigator, builder, and reviewer—that compress tool results into minimal structured output, reducing main-context usage. Ideal for code search, small edits, and diff reviews in long sessions.

This skill builds a persistent knowledge base combining LLM wiki patterns with structured graphs. It ingests documents once to generate wiki pages and extract entities/relations, avoiding re-derivation. It supports agents with evidence-based querying, wiki search, and knowledge accumulation, ensuring cross-references are never lost.

Search, read, and monitor arXiv papers through a Model Context Protocol server. Supports abstracts, LaTeX source sections, citation graphs, local storage, and topic watches.

A comprehensive guide to implementing schema.org structured data using JSON-LD format. Learn to correctly identify page types (Product, Article, Organization, FAQPage) and apply the necessary schema markup to help search engines understand content context. This skill covers initial assessment, core principles (accuracy, validation), and optimizing for rich results like star ratings, site search blocks, and rich snippets, significantly boosting search engine visibility and CTR.

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.