Skill UI

This skill guides users on fine-tuning CodeRabbit's review quality. It teaches the AI agent team preferences through PR feedback, integrates custom code guidelines (e.g., security policies), allows customization of the review tone, and implements path filters to drastically reduce false positives and noise. This ensures that all code feedback is highly relevant, actionable, and aligns with specific team standards.

Fine-tune LLMs with TRL’s RLHF toolkit, covering SFT, DPO, PPO/GRPO and reward model training so you can align HuggingFace Transformers with human preferences or feedback in post-training workflows.

Memory provides persistent context across multiple coding sessions. It stores crucial project decisions, user preferences (like preferred package managers or specific libraries), and architectural conventions in a local memory file. This ensures that the AI maintains consistency, recalls past choices automatically, and applies project-specific rules without needing constant reminders, significantly improving workflow continuity and reliability.

This skill provides centralized management for organization-wide Windsurf settings, including editor preferences, AI behavioral policies, tool approvals, and compliance requirements. It is essential for administrators who need to standardize configurations, manage global team policies, and ensure organizational compliance across multiple teams within an enterprise setting.

Access the BRENDA SOAP API to retrieve enzyme kinetic parameters, reaction equations, organism-specific data, and environmental preferences, enabling biochemical research, metabolic pathway reconstruction, and enzyme engineering workflows.

Identifies and exploits insecure local data storage in Android and iOS apps, covering SharedPreferences, SQLite, backups, and keychain misuse to support OWASP M9 and MASVS storage assessments during mobile penetration tests.

Presents modal dialogs for asking clarifying questions, preferences, or confirmations when the user cannot see CLI output, ensuring explicit consent before sensitive or ambiguous actions.

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 utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

This expert system generates a comprehensive and best-practice-oriented .editorconfig file. It analyzes the project structure and user preferences to enforce coding style consistency (e.g., spaces vs. tabs, indentation size, line endings, trailing whitespace) across diverse IDEs and text editors. Essential for maintaining clean, standardized code across development teams and version control systems.

Guides teams through creating a complete GitHub Copilot configuration by outlining required instructions, reusable skills, and agent/workflow files tailored to the chosen tech stack and workflow preferences.

EverOS OpenClaw Plugin integrates persistent, long-term memory capabilities into OpenClaw using the ContextEngine API. It goes beyond simple memory slots by automatically recalling relevant past context before a reply and saving new conversation content after a turn. This allows users to chat naturally while ensuring context, preferences, and key details persist across multiple sessions, eliminating the need for manual memory calls.