Skill UI

This methodology converts verbose, natural language system instructions into an atomic, structured-label format (e.g., trigger:, action:). By structuring rules, it bypasses the limitations of natural language processing, ensuring LLMs allocate full attention to every constraint, thereby maximizing compliance, improving reliability, and reducing token usage.

This tool is designed to help optimize and refine prompts used with Large Language Models (LLMs). By structuring inputs, it ensures that the instructions given to AI systems are clear, comprehensive, and contextually rich, resulting in significantly higher quality and relevance of the generated output. Useful for developers, writers, and researchers.

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.

RecallMax dramatically enhances AI agent memory capabilities by cleanly injecting massive external contexts (500K-1M tokens) without hallucination drift. It automatically summarizes lengthy conversations, preserving tone, intent, and key facts. Furthermore, it compresses multi-turn interaction histories into high-density token sequences, ensuring agents maintain perfect recall across complex and long-running sessions.

An advanced reliability wrapper for AI agents that monitors, manages, and recovers from tool call failures. It automatically handles common issues such as truncated JSON responses, API timeouts, rate limits, and mid-chain process breakdowns. By implementing sophisticated failure classification, checkpointing, and learning mechanisms, Guardian ensures complex, multi-step agent workflows remain stable and resilient.

This skill provides a structured methodology and Python implementation for monitoring and analyzing data leak sites (DLS) associated with ransomware groups. It helps security analysts track victim postings, extract threat intelligence on group tactics, assess sector-specific risks, and understand evolving ransomware threat landscapes for proactive defense.

This tool provides a comprehensive framework for analyzing web server logs (Apache/Nginx) to detect various security threats. It uses advanced regex matching against OWASP attack signatures to identify SQL injection, Local File Inclusion, and Directory Traversal attempts, alongside behavioral analysis for brute-force attacks and web scanner fingerprints. The system also enriches data using GeoIP for source attribution, generating a prioritized report for SOC analysts.

This skill implements an autonomous, multi-round review loop for refining complex research or codebases. It iteratively reviews content using specified external backends (e.g., Codex, manual review), implements necessary fixes based on feedback, and re-reviews until the positive assessment criteria are met or the maximum round limit is reached. It manages state persistence and allows for configurable difficulty levels, making it ideal for projects requiring rigorous, self-correcting quality assurance.

This skill automates the research refinement process by entering an autonomous, multi-round review loop. It leverages any OpenAI-compatible LLM to score, critique, and guide necessary fixes until the work meets a high, expert-defined standard. It manages state persistence across rounds, simulating the rigorous process of professional peer review.

This skill implements an autonomous, multi-round research review loop, simulating the rigorous peer-review process of top-tier conferences. It iteratively reviews, identifies weaknesses, suggests fixes, and re-reviews a research document using the MiniMax API. The system manages state persistence across multiple rounds, ensuring continuous improvement until a 'ready' assessment is achieved or the maximum round limit is reached. Ideal for academic writing, thesis refinement, or comprehensive project documentation.

This skill outlines the development of an automated pipeline to ingest raw Indicators of Compromise (IOCs) from various sources. It performs normalization, defanging (e.g., converting 'http://' to 'hxxp://'), and converts indicators into the standardized STIX 2.1 format. The processed, safe indicators can then be securely distributed via TAXII feeds, MISP, and other threat intelligence platforms.

This skill deploys advanced anomaly detection systems for Operational Technology (OT) and Industrial Control Systems (ICS). It builds multi-dimensional baselines by analyzing SCADA communications, network traffic (Modbus, DNP3, OPC UA), and timing patterns. It detects deviations, rogue devices, and potential intrusions by profiling normal behavior, complementing signature-based IDS.