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MISP (Malware Information Sharing Platform) is an open-source platform designed for gathering, storing, and correlating Indicators of Compromise (IOCs). This skill guides users through deploying MISP using Docker, configuring various threat feeds (MISP, TAXII, CSV), and utilizing the PyMISP Python API. It enables the building of automated pipelines to aggregate diverse threat data, such as IP addresses, domains, and hashes, for enhanced security operations.

This skill provides systematic methods and advanced techniques to deobfuscate multi-layered PowerShell malware. It utilizes Abstract Syntax Tree (AST) analysis, dynamic execution tracing, and specialized tools to reveal hidden payloads, C2 infrastructure, and the original malicious logic. It is essential for security analysts, red teamers, and incident response teams performing deep malware analysis.

IAM Permission Boundaries are an advanced AWS security feature used to set the maximum permissions an identity-based policy can grant to an IAM entity (user or role). This allows security teams to safely delegate role creation to developers while strictly enforcing least-privilege principles, preventing unintended privilege escalation.

Audits a domain’s email authentication by querying SPF, DKIM, and DMARC DNS records using dnspython, validating syntax/counts, flagging common spoofing risks, and producing remediation guidance.

This guide demonstrates how to programmatically create, validate, and exchange complex structured threat intelligence objects (Indicators, Malware, Campaigns) using the STIX 2.1 standard and the stix2 Python library. It covers the full lifecycle of intelligence sharing, including defining patterns and publishing data via TAXII 2.1, making it essential for building sophisticated Threat Intelligence Platforms (TIPs) and integrating with SIEM/SOAR systems.

This skill provides a comprehensive guide to integrating STIX (Structured Threat Information eXpression) and TAXII (Trusted Automated eXchange of Intelligence Information). It covers using Python to consume and produce cyber threat intelligence feeds, including server discovery, object fetching, and parsing of STIX 2.1 objects for integration into SIEM and TIP platforms.

Processes STIX 2.1 threat intelligence bundles delivered via TAXII 2.1 servers. It normalizes various threat intelligence objects (indicators, actors) into platform-native schemas, enabling automated ingestion, validation, and routing to SIEM, TIP, and EDR systems. Ideal for integrating new ISAC feeds or building bi-directional intelligence pipelines.

Provides end-to-end guidance for fine-tuning and serving Physical Intelligence’s OpenPI pi0 family (pi0, pi0-fast, pi0.5) with both JAX and PyTorch backends, covering dataset prep, norm stats, checkpoint conversion, and low-latency WebSocket policy inference for ALOHA, DROID, and LIBERO tasks.

This skill enforces a rigorous, 5-phase debugging protocol (Reproduce, Isolate, Root Cause, Fix, Verify) for AI coding agents. It forces a disciplined approach, preventing agents from guessing fixes or making premature edits. Use this when debugging complex, intermittent, or hard-to-isolate bugs to ensure the root cause is fully confirmed before any code changes are applied, maximizing debugging accuracy.

This tool transforms natural language requirements into high-quality, structured Mermaid diagrams. It supports over 20 diagram types, including Flowcharts, Sequence Diagrams, Class Diagrams, ER Diagrams, and Gantt Charts. The process includes parsing requirements, generating raw Mermaid code, and verifying syntax using industry-standard tools, ensuring accurate and professional visualizations for technical documentation.

This resource is designed to document and guide the implementation of various architectural patterns and best practices when developing backend applications using the NestJS framework. It helps developers structure their code for maximum scalability and maintainability.

A comprehensive workflow for the full lifecycle management of AI skills. This tool guides users through creating skills from scratch, modifying existing ones, and rigorously benchmarking performance. It supports iterative refinement, quantitative evaluation, and optimizing skill descriptions for maximum triggering accuracy.