Skill UI

This skill provides a generic entry point for delegating self-contained, complex tasks to various LLM-powered sub-agents (Codex, Claude Code, Pi). It is designed to be used in meta-skill DAG steps for tasks like feature creation, PR reviews, code refactoring, or document generation, providing full tool surface access. It favors non-interactive CLI modes for reliable background execution.

Plan experiments before data is collected: choose a design, randomize and block units, avoid pseudoreplication, and generate reproducible layouts for factorial, screening, response-surface, crossover, split-plot, and cluster-randomized studies.

Max is a specialized agent designed for safe, non-behavioral code improvement. It systematically refactors existing, working code by optimizing time complexity (Big-O), abstracting duplicated logic, removing dead code, and enhancing readability. It operates under a strict constraint: no change may alter the original functionality, requiring all refactored code to pass existing test suites flawlessly.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

This skill extracts precise, pixel-level data from various chart types (bar, line, scatter, pie) and graphs presented in images. It generates a comprehensive structured data table, including confidence levels, axis details, and assumptions, making the raw data ready for spreadsheets or further analysis. Ideal for digitizing charts, transcribing graph values, or turning visual data into actionable formats.

This skill generates a highly customized code review checklist for any pull request. Instead of using a generic template, it adapts the review depth and specific checks based on the target language (e.g., Python, TypeScript), the nature of the change (e.g., bug fix, refactor), and the associated risk level (low, medium, high, critical). It ensures comprehensive coverage, guiding reviewers to focus on critical areas like security, correctness, and architectural integrity.

This meta-skill provides a comprehensive framework for the entire lifecycle of AI agent skills. It supports creating, refactoring, rigorously evaluating (e.g., Skill OS 2.0 Gates), and securely packaging skills from various sources like workflows, prompts, and documents. It ensures standardized, team-ready, and governed deployment of advanced AI capabilities.

A comprehensive guide for defense contractors handling Controlled Unclassified Information (CUI). It covers preparing for CMMC Level 2 certification, implementing 110 NIST SP 800-171 controls, calculating the SPRS score using DoD methodology, and managing the POA&M and SSP. Essential for DFARS compliance and external assessments.

A comprehensive skill for monitoring dark web forums, marketplaces, and leak sites to detect mentions of organizational assets, leaked credentials, and ongoing threat actor activities. Used for proactive threat intelligence gathering, incident response enrichment, and establishing continuous dark web OSINT coverage.

A comprehensive tool designed to discover and map adversary-controlled assets, including C2 servers, phishing domains, and exploit kits. It utilizes sophisticated pivoting techniques across passive DNS records, Certificate Transparency logs, Shodan/Censys scans, and network fingerprints (JARM/JA3S) to generate actionable threat intelligence, perfect for CTI investigations and security operations.

This guide details how to deploy OpenCTI, a powerful, open-source threat intelligence platform, and utilize the pycti Python client. It teaches analysts how to model complex cyber threat intelligence, including threat actors, campaigns, and indicators, into a structured STIX 2.1 knowledge graph. Use this for centralized threat-intel correlation, building adversary graphs, and generating STIX bundles for detection engineering.

KAPE (Kroll Artifact Parser and Extractor) is a specialized, rapid triage tool for incident response. It collects specific, volatile forensic artifacts (like registry hives, event logs, and browser data) from compromised Windows systems. It is designed for remote, at-scale artifact collection when full disk imaging is impractical, enabling analysts to quickly build a defensible, parseable dataset for initial investigation and containment.