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Diagnose and fix common Linear API errors.\nUse when encountering Linear API errors, debugging integration issues,\nor troubleshooting authentication problems.\nTrigger with phrases like \

Project and cycle management workflows with Linear.\nUse when implementing sprint planning, managing projects and roadmaps,\nor organizing work into cycles.\nTrigger with phrases like \

Optimize Linear API usage and manage costs effectively.\nUse when reducing API calls, managing rate limits efficiently,\nor optimizing integration costs.\nTrigger with phrases like \

Data synchronization, backup, and consistency patterns for Linear.\nUse when implementing data sync, creating backups,\nor ensuring data consistency across systems.\nTrigger with phrases like \

Comprehensive debugging toolkit for Linear integrations.\nUse when setting up logging, tracing API calls,\nor building debug utilities for Linear.\nTrigger with phrases like \

Deploy Linear-integrated applications and track deployments.\nUse when deploying to production, setting up deployment tracking,\nor integrating Linear with deployment platforms.\nTrigger with phrases like \

Implement enterprise role-based access control with Linear.\nUse when setting up team permissions, implementing SSO,\nor managing access control for Linear integrations.\nTrigger with phrases like \

Install and configure Linear SDK/CLI authentication.\nUse when setting up a new Linear integration, configuring API keys,\nor initializing Linear in your project.\nTrigger with phrases like \

Set up local Linear development environment and testing workflow.\nUse when configuring local development, testing integrations,\nor setting up a development workflow with Linear.\nTrigger with phrases like \

Configure Linear across development, staging, and production environments.\nUse when setting up multi-environment deployments, managing per-environment API keys,\nor implementing environment-specific Linear configurations.\nTrigger with phrases like \

Implement monitoring, logging, and alerting for Linear integrations.\nUse when setting up metrics collection, creating dashboards,\nor configuring alerts for Linear API usage.\nTrigger with phrases like \

Optimize Linear API queries and caching for better performance.\nUse when improving response times, reducing API calls,\nor implementing caching strategies.\nTrigger with phrases like \

Production readiness checklist for Linear integrations.\nUse when preparing to deploy a Linear integration to production,\nreviewing production requirements, or auditing existing deployments.\nTrigger with phrases like \

TypeScript/JavaScript SDK patterns and best practices for Linear.\nUse when learning SDK idioms, implementing common patterns,\nor optimizing Linear API usage.\nTrigger with phrases like \

Secure API key management and OAuth best practices for Linear.\nUse when setting up authentication securely, implementing OAuth flows,\nor hardening Linear integrations.\nTrigger with phrases like \

Refine rough engineering thoughts into structured Linear tickets with GitHub permalinks

This skill fetches open tasks assigned to the user from the Linear API and generates a markdown todo list file in the project root. This skill should be used when the user asks about their work items, wants to see what they need to work on, or requests to load/sync their Linear

Configure and handle Linear webhooks for real-time event processing.\nUse when setting up webhooks, handling Linear events,\nor building real-time integrations.\nTrigger with phrases like \

Linear.app CLI for issue tracking. Use for listing, creating, updating, and searching Linear issues, comments, documents, cycles, and projects. Optimized for LLM agents with JSON output.

Work with LinkdAPI Python SDK for accessing LinkedIn professional profile and company data. Use when you need to fetch profile information, company data, job listings, or search for people/jobs on LinkedIn. The skill uses uv script pattern for ephemeral Python scripts with

Manage bookmarks with Linkding. Use when the user asks to \

LinkedIn automation via browser relay or cookies for messaging, profile viewing, and network actions.

Automate LinkedIn content creation, posting, engagement tracking, and audience growth. Use for posting content, scheduling posts, analyzing engagement metrics, generating content ideas, commenting on posts, and building LinkedIn presence. Requires browser access with LinkedIn

A bird-like LinkedIn CLI for searching profiles, checking messages, and summarizing your feed using session cookies.

Bulletproof LinkedIn inbox monitoring with progressive autonomy. Monitors messages hourly, drafts replies in your voice, and alerts you to new conversations. Supports 4 autonomy levels from monitor-only to full autonomous.

Build targeted prospect lists by analyzing LinkedIn profiles, extracting job titles, companies, locations, and recent activity. Identifies decision-makers, tracks job changes for warm outreach, and enriches contact data. Use when users need to find prospects, build lead lists,

Implement Linkerd service mesh patterns for lightweight, security-focused service mesh deployments. Use when setting up Linkerd, configuring traffic policies, or implementing zero-trust networking with minimal overhead.

Check code quality and formatting before committing changes

Automatic quality control, linting, and static analysis procedures. Use after every code modification to ensure syntax correctness and project standards. Triggers onKeywords: lint, format, check, validate, types, static analysis.

Manage linux commands guide operations. Auto-activating skill for DevOps Basics.\nTriggers on: linux commands guide, linux commands guide\nPart of the DevOps Basics skill category. Use when working with linux commands guide functionality. Trigger with phrases like \

Control the Linux desktop GUI using xdotool, wmctrl, and dogtail. Use when you need to interact with non-browser applications, simulate mouse/keyboard input, manage windows, or inspect the UI hierarchy of applications on X11/GNOME. Supports: (1) Clicking/typing in apps, (2)

This skill should be used when the user asks to \

This skill should be used when the user asks to \

Diagnoses common Linux service issues using logs, systemd/PM2, file permissions, Nginx reverse proxy checks, and DNS sanity checks. Use when a server app is failing, unreachable, or misconfigured.

Context-aware routing to iOS 26 Liquid Glass implementation patterns. Use when working with glass effects, GlassEffectContainer, morphing transitions, or iOS 26 visual effects.

List the contents of an npm package tarball before publishing. Use when the user wants to see what files are included in an npm bundle, verify package contents, or debug npm publish issues.

Creates event-driven email listeners that monitor for specific conditions (like urgent emails from boss, newsletters to archive, package tracking) and execute custom actions. Use when user wants to be notified about emails, automatically handle certain emails, or set up email

Conduct comprehensive, systematic literature reviews using multiple academic databases (PubMed, arXiv, bioRxiv, Semantic Scholar, etc.). This skill should be used when conducting systematic literature reviews, meta-analyses, research synthesis, or comprehensive literature

Control Little Snitch firewall on macOS. View logs, manage profiles and rule groups, monitor network traffic. Use when the user wants to check firewall activity, enable/disable profiles or blocklists, or troubleshoot network connections.

Develops reactive Livewire 4 components. Activates when creating, updating, or modifying Livewire components; working with wire:model, wire:click, wire:loading, or any wire: directives; adding real-time updates, loading states, or reactivity; debugging component behavior;

Runs LLM inference on CPU, Apple Silicon, and consumer GPUs without NVIDIA hardware. Use for edge deployment, M1/M2/M3 Macs, AMD/Intel GPUs, or when CUDA is unavailable. Supports GGUF quantization (1.5-8 bit) for reduced memory and 4-10× speedup vs PyTorch on CPU.

Expert guidance for fine-tuning LLMs with LLaMA-Factory - WebUI no-code, 100+ models, 2/3/4/5/6/8-bit QLoRA, multimodal support

Meta's 7-8B specialized moderation model for LLM input/output filtering. 6 safety categories - violence/hate, sexual content, weapons, substances, self-harm, criminal planning. 94-95% accuracy. Deploy with vLLM, HuggingFace, Sagemaker. Integrates with NeMo Guardrails.

Data framework for building LLM applications with RAG. Specializes in document ingestion (300+ connectors), indexing, and querying. Features vector indices, query engines, agents, and multi-modal support. Use for document Qu0026A, chatbots, knowledge retrieval, or building RAG

Large Language and Vision Assistant. Enables visual instruction tuning and image-based conversations. Combines CLIP vision encoder with Vicuna/LLaMA language models. Supports multi-turn image chat, visual question answering, and instruction following. Use for vision-language

Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.

Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.

You are an AI assistant development expert specializing in creating intelligent conversational interfaces, chatbots, and AI-powered applications. Design comprehensive AI assistant solutions with natur