Skill UI

This skill guides the comprehensive workflow for performance measurement in Go. It covers writing robust benchmarks using b.Loop(), running tests with advanced flags (CPU/memory profiles), and deep analysis using tools like benchstat and pprof. Use this when you need to precisely measure bottlenecks, statistically compare multiple optimization variants, or detect performance regressions in CI/CD pipelines.

A comprehensive guide for advanced Go developers, detailing the internals, optimal usage, and performance pitfalls of core data structures like slices, maps, and arrays. It covers capacity growth mechanics, preallocation strategies using `make` and `slices` package functions, and best practices for choosing between various standard library containers (e.g., `strings.Builder` vs `bytes.Buffer`). Essential for writing highly efficient, memory-safe Go code.

A comprehensive guide for optimizing Golang applications, covering systematic approaches to identifying and resolving bottlenecks. It details best practices for reducing memory allocations, improving CPU efficiency, tuning GC, and managing concurrency. The methodology emphasizes profiling first (using pprof, fgprof) and following an iterative cycle: Benchmark → Diagnose → Improve → Benchmark.

A comprehensive, type-safe in-memory caching library for Go, designed for advanced system architecture. It supports nine sophisticated eviction algorithms (like W-TinyLFU, LRU, LFU, ARC) to optimize cache hit rates based on specific access patterns. Key features include TTL management, loader chains for read-through caching (with singleflight deduplication), sharding, and Prometheus metrics. Ideal for high-frequency services needing to reduce database load and latency.

Manage Lark tasks, tasklists, and task agents. Supports creating/updating tasks, assigning members, adding subtasks, uploading attachments, and tracking progress.

A comprehensive tool for managing structured knowledge bases within Lark/Feishu. It allows users to create, list, and organize knowledge spaces, manage the hierarchy of document nodes, and handle membership administration. Ideal for professionals needing to structure, archive, or modify corporate knowledge documentation and collaboration structures.

Provides OpenClaw agents with local persistent memory via MemOS, managing installation, configuration, upgrades, and a memory viewer for secure offline workflows.

Describes how to use the MemOS local memory toolkit to recall past conversations, search shared histories, and choose the right memory tool when automatic recall is insufficient.

Automates working with .docx files via OfficeCLI, covering reading, parsing, editing, and preparing Word reports, letters, memos, and proposals alongside templates, tracked changes, headers/footers, or tables of contents.

This guide details the OpenViking long-term memory plugin, which enhances conversational AI by automatically capturing key facts and preferences from past interactions. It utilizes auto-capture modes (semantic or keyword) to store memories and automatically recalls relevant context before generating a response, ensuring continuity and personalization across long conversations. Provides tools for retrieval, manual storage, and memory deletion.

This skill allows agents to ingest external resources (local files, URLs, repositories) using `ov add-resource`, and to create long-lasting searchable memories from conversations using `ov add-memory`. It is essential for building a comprehensive, persistent knowledge base for the OpenViking context database, ensuring that learned insights and external data remain available across sessions and interactions. Use when external knowledge injection or context persistence is required.

Use the OpenViking CLI to search memories, skills, and resources with semantic and literal queries across directories, surfacing context-aware results whenever agents need stored knowledge or documentation.