Skill UI

This is a foundational primitive skill for managing complex browser interactions. It initiates a named, traceable browser session within an RVF cognitive container, recording every user action as a trajectory step. Use it when starting any multi-step web exploration, ensuring the session state, actions, and results are preserved for later analysis, testing, or replay by downstream skills.

Provides comprehensive configuration management for local LLM inference using RuVLLM. Users can select models, perform task-specific fine-tuning with MicroLoRA adapters, or implement continuous, real-time adaptation using SONA. This tool guides the entire advanced LLM workflow, covering status checks, configuration generation, and advanced adaptation loops.

This tool manages the lifecycle of learned intelligence patterns, implementing the crucial DISTILL and CONSOLIDATE phases of the 4-step intelligence pipeline. It enables the capture of successful task knowledge, supports multi-domain adaptation via SONA or MicroLoRA, and consolidates learned patterns into long-term storage to prevent catastrophic forgetting.

This helper tool is designed to assist with a wide variety of tasks and needs. It encourages users to use their own judgment and judgment to handle diverse prompts, making it a flexible and adaptable resource for general problem-solving scenarios. Ideal for brainstorming, general queries, and initial exploration.

This guide provides a comprehensive onboarding process for RuView (WiFi-DensePose). It details three deployment tiers: Docker simulation for quick testing and UI exploration, source code build verification (Rust/Python), and live sensing setup utilizing ESP32 hardware. Ideal for new users or rapid prototyping.

This guide details the rigorous data analysis and reporting standards required for top-tier academic journals, such as AJPS. It focuses intensely on reproducibility, ensuring that all results—from primary findings to exploratory models—are generated by a single, seeded master script. Key requirements include reporting uncertainty intervals, proving robustness, adhering to pre-registration discipline, and accommodating mandatory third-party code verification. Use this when conducting complex empirical research in social sciences or political science, needing to withstand intensive methodolo...

A comprehensive guide for analyzing and reporting longitudinal data in Developmental Psychology manuscripts, ensuring compliance with high academic standards. It emphasizes modeling developmental change (e.g., growth curves, multilevel models, SEM), establishing measurement invariance (configural, metric, scalar) before interpretation, and rigorously reporting effect sizes and confidence intervals (CIs) using JARS guidelines. It guides the distinction between confirmatory and exploratory findings.

This guide helps researchers structure developmental psychology manuscripts by emphasizing the 'theory of change.' It teaches how to articulate age-graded, directional hypotheses by first stating the underlying developmental mechanism (e.g., maturation, scaffolding) and the predicted form of change (e.g., age main effect, growth curve). It is crucial for maintaining methodological rigor and separating confirmatory from exploratory claims, aligning with JARS reporting standards.

This skill guides users in evaluating whether their economics, management, or policy manuscript is a suitable fit for the journal 'Inquiry into Economic Issues.' It analyzes the journal's specific focus (regional development, Southwest opening, policy exploration), checks formatting requirements, and identifies common academic pitfalls and potential submission risks for high-quality Chinese academic publishing.

A guide for structuring rigorous academic manuscripts in educational psychology. It teaches researchers how to state a clear psychological mechanism, derive directional and testable hypotheses, and properly distinguish between confirmatory (preregistered) and exploratory analyses, adhering to high academic standards.

This guide provides advanced methodological principles for conducting theoretically grounded heterogeneity analysis in financial research. It emphasizes that any dimension used for splitting the data (e.g., ownership, bank type, financial constraint) must be predicted by the underlying economic mechanism, rather than being an arbitrary data exploration. It details best practices, statistical rigor (group coefficient difference testing), and common pitfalls to avoid.

A comprehensive guide for structuring rigorous academic research in management studies. It helps researchers align theoretical claims (causal, exploratory, mechanism testing) with appropriate methods—including qualitative, quantitative, and multi-method designs. The guide covers advanced topics such as addressing common-method bias, endogeneity, and cross-level analysis, ensuring methodological rigor for top-tier publications.