Skill UI

An expert guide for deploying and optimizing Large Language Models (LLMs) entirely on local hardware. Mastering the entire local AI ecosystem, including inference engines like Ollama, vLLM, and llama.cpp. Provides deep knowledge on quantization formats (GGUF, EXL2, AWQ), VRAM calculation, and ensuring privacy-first, offline deployment for state-of-the-art models.

Generate tailored roadmap narratives, release notes, changelogs, and stakeholder updates for executives, engineering, and customer audiences, with structured formats, templates, and checklists for consistent product messaging.

This skill manages the strategic distribution of a single core idea across various social media platforms, including X, LinkedIn, Threads, and Bluesky. Instead of simply cross-posting the same content, it uses advanced content adaptation techniques to adjust the tone, format, and focus for each specific platform. This ensures the message remains consistent while optimizing for native platform constraints, preventing duplication and maximizing engagement.

This skill provides methods for reliably extracting typed, validated, and structured data from Large Language Models (LLMs), moving beyond simple text parsing. It covers best practices across major APIs (OpenAI's response_format, Anthropic's tool_use, Google's responseSchema) and local models. Key workflows include defining schemas (Pydantic/Zod), implementing constrained decoding, and building robust retry loops for production-grade data pipelines.

This skill orchestrates end-to-end autonomous AI research projects using a sophisticated two-loop architecture. It manages the entire research lifecycle, from initial literature review and hypothesis formation to running constrained experiments and synthesizing findings into publishable papers. It acts as a project manager, autonomously driving the process while structuring the workspace and presenting progress to human users.

This skill automates the complex and tedious process of porting academic papers written in LaTeX from one publisher's journal template (e.g., Springer) to another (e.g., MDPI, IEEE). It executes a structured multi-stage workflow, including content extraction, structural mapping, systematic fixing of common LaTeX issues (like float placement and math environments), and final compilation to ensure the output adheres to the target journal's specifications.

This agent provides structured summarization for academic papers, reports, and articles. It goes beyond simple summarizing by extracting key findings, generating comparative matrices across multiple sources, and producing properly formatted citations (APA, IEEE, etc.). It is designed for analysts, product managers, and researchers who need actionable, deep insights from dense documentation.

A comprehensive design specification detailing 98 rules for building Command Line Interface (CLI) tools that are safe, reliable, and easily consumable by AI agents. It covers structured JSON output formats, standardized error handling (to stderr), robust input contracts, safety guardrails (e.g., preventing path traversal), and tiered compliance levels (Agent-Friendly, Agent-Ready, Agent-Native) for seamless integration into AI workflows.

This skill provides a comprehensive framework for ingesting, normalizing, and enriching threat intelligence (CTI) from multiple structured and unstructured sources. It guides users through scoring feeds, converting heterogeneous IOC formats (like OpenIOC, YARA) into the standardized STIX 2.1 format, and enriching indicators using external services (e.g., VirusTotal, PassiveTotal). Ideal for building robust, automated threat detection pipelines for SIEM and EDR systems.

This tool queries a MISP (Malware Information Sharing Platform) instance via PyMISP to compute detailed event statistics, IOC type breakdowns, threat actor galaxy clusters, and tag trends. It generates comprehensive threat landscape reports, visualizing temporal trends. Use it when investigating security incidents, summarizing top threat actors or malware families, or producing a formal Cyber Threat Intelligence (CTI) report.

This skill provides a comprehensive workflow for building vendor-agnostic detection rules using the Sigma format. It empowers security operations center (SOC) engineers to translate raw threat intelligence and MITRE ATT&CK techniques into standardized, portable detection logic. The process guides users through rule validation and conversion into specific queries for major SIEM platforms, including Splunk, Elastic, and Microsoft Sentinel, ensuring robust detection coverage.

This pipeline provides an automated solution for processing raw Indicators of Compromise (IOCs) from various sources, including IPs, domains, URLs, and emails. It performs crucial steps such as normalization, deduplication, and defanging (modifying indicators to prevent accidental execution while preserving readability). The final, processed data is converted into the standardized STIX 2.1 format and can be securely distributed via TAXII servers or MISP instances, streamlining threat intelligence sharing.