Skill UI

This skill covers splitting extracted text into chunks suitable for LLM context windows or RAG ingestion. It includes configuring chunk size, overlap, and selecting chunker types like markdown, yaml, or semantic. Users can use the standalone chunk command or enable inline chunking during extraction. It supports token-based sizing for specific models and provides configuration options via CLI, config files, or Python API.

A constraint-based protocol designed to professionalize existing code by normalizing comments and performing safe, non-semantic cleanup. This skill converts informal, outdated, or missing documentation into clear, concise, and professional standards without altering the core logic or behavior. Ideal for preparing code for code review, open-sourcing, or formal documentation.

An intelligent context management specialist for capturing, serializing, and retrieving comprehensive project context across AI workflows. Supports semantic extraction, vector database integration, knowledge graphs, and multi-session collaboration.

This skill specializes in advanced AI context engineering, managing dynamic information flows, vector databases, and knowledge graphs. It assists in building intelligent memory systems, optimizing context windows, and orchestrating multi-agent workflows. Ideal for enterprise-scale AI applications requiring coherent state management, RAG implementation, and semantic retrieval strategies to enhance system performance and reliability.

This skill provides comprehensive guidelines for writing idiomatic and efficient C++ code. It covers memory management, modern types, move semantics, templates, error handling, concurrency, and common anti-patterns to ensure safe and maintainable software development.

This tool diagnoses and validates complex cron expressions (e.g., crontab, Kubernetes CronJob) to prevent silent scheduling failures. It identifies five common pitfalls: impossible dates, misunderstood OR semantics, resource spikes, uneven step drift, and leap year issues. Users can check the syntax, understand the true meaning, and calculate future execution times reliably.

This skill is designed for generating structured, insightful, and boundary-aware short answers, such as reader replies, editor responses, or consultation advice. It structures analysis by defining fact boundaries, providing structural judgment, offering necessary critique, and suggesting safe, low-risk next steps. It is primarily activated by the CrossFrame Suite, ensuring the output maintains a canonical Chinese semantic layer while providing professional, actionable advice.

This skill focuses on reversing JavaScript-based custom DSL and Virtual Machine interpreters, commonly found in risk-control and captcha engines. It involves identifying IIFE and switch-based opcode dispatch patterns, extracting opcode tables, and capturing runtime semantics. The workflow includes static analysis for variable mapping and opcode classification, as well as runtime injection techniques to understand exported functions. It excludes standard WASM and typical Webpack bundles.

This skill provides structured patterns for combining vector similarity search with traditional keyword-based search. It is essential for building advanced Retrieval-Augmented Generation (RAG) systems, sophisticated search engines, or any retrieval task requiring high recall. Use this when pure semantic understanding is insufficient, and precise matching of specific terms (like names or codes) is necessary.

Cut LLM costs and latency with three caching layers: exact-match Redis cache, semantic similarity cache (GPTCache/Qdrant), and provider-side prompt caching. Ideal for FAQ bots, support tools and high-volume APIs.

This skill helps reduce LLM API and infrastructure costs by 50-90% using strategies like model selection, prompt caching, batching, quantization, and self-hosting. It covers cost tracking, right-sizing models for tasks, provider-side caching, and semantic caching to avoid redundant calls. Suitable for developers managing AI spend and optimizing cloud infrastructure.

Deploy a unified API gateway for LLM traffic with load balancing, rate limiting, key management, semantic caching, fallback routing, and cost tracking.