Skill UI

Guides engineers through initializing MCUs, crafting RTOS-aware drivers, and tuning power/timing for STM32, ESP32, and bare-metal targets; covers constraint analysis, interrupt safety, resource tracking, and validation with static analysis and real-time debugging.

This skill provides a comprehensive, expert guide to the entire reverse engineering lifecycle. It covers advanced techniques for binary analysis, including static analysis (disassembly, metadata extraction, control flow mapping) and dynamic analysis (API hooking, memory tracing). Mastery includes utilizing industry-leading tools such as IDA Pro, Ghidra, radare2, and specialized frameworks like pwntools and angr. The methodology is structured for secure research, CTF challenges, and vulnerability assessment, emphasizing best practices and ethical use.

COBRApy is a Python library for constraint-based reconstruction and analysis of genome-scale metabolic models, letting you load SBML/JSON/YAML models, perform FBA/FVA/sampling, and study gene or reaction knockouts for systems biology and metabolic engineering.

Zeek is a powerful, open-source network security monitoring framework that generates high-fidelity structured logs and metadata from raw network traffic. This skill covers deploying Zeek, analyzing various protocol logs (HTTP, DNS, TLS, etc.), writing custom detection scripts to identify anomalies, and integrating resulting security insights with SIEM platforms for comprehensive threat detection and forensic investigation.

This comprehensive guide details a multi-step workflow for detecting and extracting covert data hidden within images, audio, and video files. It covers initial metadata analysis, running advanced steganalysis tools (like zsteg, binwalk, stegoveritas), and performing low-level analysis such as Least Significant Bit (LSB) analysis to uncover hidden communication channels used in digital forensics and cybersecurity investigations.

A comprehensive debug bundle designed to collect diagnostic evidence from AppFolio property management API integrations. It captures essential data points including API connectivity status for properties, tenants, and work orders; authentication status using client credentials; recent integration error logs; and environment metadata. This allows support engineers to perform root cause analysis and diagnose issues like connectivity failures or authentication rejections without needing live environment access.

This skill provides a direct, fast shell invocation to capture the current git diff (staged, working-tree, or specific file list) as raw text. It is designed to replace slower LLM agent steps that merely execute 'git diff' commands, significantly speeding up CI/CD workflows and meta-steps that require repository state analysis.

This meta-skill runs three essential quality gates (ruff, mypy, and pytest) concurrently against the staged changes. It provides a comprehensive, CI-like check locally, offering a single BLOCK or APPROVE verdict to ensure code readiness before committing. It combines static analysis, type checking, and unit testing into one workflow.

This meta-skill implements a combinator pattern for deep security analysis. It runs three independent gates—policy governance, secret scanning, and audit logging—over a candidate operation. It enforces a strict arbitration rule (Policy DENY > Scanner WARN > ALLOW) to provide a robust, multi-layered verdict, making it ideal for reviewing critical code changes or environment modifications for safety compliance.

This meta-skill is designed to automate the diagnosis of complex programming errors. When presented with a stack trace, traceback, or failing log, it orchestrates a multi-step investigation. It parses the trace, searches the repository history, checks relevant issues, and analyzes code diffs to provide a comprehensive brief for root cause analysis and debugging.

This comprehensive preflight checklist is designed for authors submitting a commissioned review article to Annual Reviews of Psychology (ARPsych). It guides users through critical compliance checks, ensuring adherence to assigned length, proper reference formatting, securing figure permissions, completing conflict-of-interest disclosures, and verifying the transparency and reproducibility of any embedded meta-analysis or literature search. It serves as the final quality assurance step before formal publishing submission.

A comprehensive guide for structuring the algorithm section of academic papers. It details the crucial steps for deciding on the solution route—whether to use commercial solvers, exact algorithms, or meta-heuristics. It emphasizes the requirement for rigorous pseudo-code, property analysis, and multi-level baseline comparisons to prove algorithmic novelty and contribution.