Skill UI

This utility initializes a brand-new Ruflo project environment. It sets up all necessary underlying components, including MCP tools, hooks, and agent configurations. It is essential for developers starting a project in a fresh repository, allowing selection between standard, minimal, or full presets to bootstrap the entire system.

This utility detects and classifies various anomalies (such as spikes, flatlines, drift, or pattern breaks) within telemetry data streamed from IoT devices. It utilizes Z-score anomaly detection to provide actionable insights. This is crucial for investigating unusual device metrics, ensuring system health before deploying major firmware updates (canary advancement), or triaging fleet-wide operational alerts.

This tool scans ingested OHLCV market data to automatically detect and classify various candlestick patterns, such as dojis, engulfing patterns, and head-shoulders formations. It not only identifies the pattern name but also assigns a type (reversal/continuation), direction, and a quantitative reliability score. It is an essential utility for technical analysis, helping users identify potential market turning points or continued trends.

This utility diagnoses and automatically repairs common issues within the Ruflo installation environment. It performs comprehensive health checks on core dependencies like Node.js, npm, and git, while also validating configurations, daemon status, memory databases, and API keys. It provides targeted commands to resolve issues related to memory initialization, daemon startup, and configuration resets, ensuring a stable operational environment.

A utility designed to generate high-dimensional vector embeddings (384-dim) from various sources including text, code, or documents. This process converts raw data into numerical representations optimized for semantic search, similarity comparison, and clustering, utilizing ONNX and HNSW indexing for efficient retrieval.

This utility is designed to reconcile the ADR (Architecture Decision Record) index when source files have been deleted or modified, preventing the accumulation of stale or dangling reference rows. Unlike simple upserts, this tool performs a hard purge of the entire index namespace (adr-patterns and adr-edges) and then fully rebuilds the index from the files currently present on disk. It is crucial for maintaining data integrity after deletions.

This utility switches the active operational credential profile to manage different brands within a multi-client agency environment. It performs mandatory, deep validation of API keys, OAuth tokens, and environment variables before switching, ensuring that all subsequent platform operations (e.g., ad spending, reporting) are correctly routed to the target brand's account. It is crucial for preventing cross-client data leakage and maintaining audit integrity.

A comprehensive tool for managing and optimizing XML sitemaps. This utility allows users to analyze existing sitemaps for SEO health issues (e.g., 404 errors, stale dates, robots.txt conflicts) and to generate new, structured sitemaps. It adheres to industry best practices across various models (eCommerce, SaaS, etc.), ensuring optimal indexation for search engines.

This skill provides expert guidance for authors submitting manuscripts to the Bulletin of Chinese Academy of Sciences (CAS). It helps assess the strategic altitude, policy relevance, and decision-making utility of the work, ensuring alignment with the journal's high-level focus on national S&T strategy, governance, and major projects. It includes structural checks, common rejection pitfalls, and alternative journal suggestions.

A comprehensive guide for designing rigorous Monte Carlo simulations and empirical illustrations for methodological papers, particularly in econometrics. It details how to stress assumptions, report necessary metrics (size, power, bias), and structure the evidence to prove a method's theoretical validity and practical utility.

A rigorous methodological framework for identifying core risk and uncertainty parameters in academic research. This guide details how to design experiments or structural models to explicitly separate confounds (e.g., utility curvature vs. probability weighting, preferences vs. beliefs) and ensure that the measured parameter is genuinely identified, thereby enhancing the scientific rigor of behavioral finance and econometrics studies.

A comprehensive guide for academic authors on how to strategically position their research contribution within specialized fields like risk, uncertainty, and decision theory. It details how to engage with canonical academic debates (e.g., Expected Utility critiques, Prospect Theory, Ambiguity) by citing primary literature and clearly defining the novelty relative to the closest prior work, enhancing the paper's academic rigor.