Skill UI

This expert playbook details advanced techniques for exploiting stack-based vulnerabilities, including classic buffer overflows, Return-Oriented Programming (ROP), ret2libc, and specialized gadgets like ret2csu and SROP. It provides comprehensive guidance on bypassing modern memory mitigations (NX, ASLR, PIE) to hijack program control flow and achieve reliable remote code execution in userland binaries.

This skill facilitates a rigorous, documentation-anchored design review session. It challenges a proposed plan by cross-referencing it against the project's established domain glossary (CONTEXT.md), historical architectural decisions (ADRs), and the current codebase. It forces the user to resolve ambiguities, define precise terminology, and validate design trade-offs using concrete scenarios, ensuring all decisions are captured and documented inline.

This skill provides comprehensive management for Opencode's permission system. It allows users to review currently always-allowed commands, suggest safe read-only commands, and configure granular access controls using allow/deny/ask patterns. It is essential for optimizing security, performing system audits, and managing the operational scope of the agent.

A structured presentation deck designed for rigorous review of safety incidents, security risks, and policy compliance. It utilizes distinct visual cues, including hazard stripes, L1/L2/L3 severity tiers, and dedicated code blocks for policy review (e.g., YAML). Ideal for red team exercises, post-incident reviews, and high-stakes technical briefings.

This skill provides a comprehensive, evidence-based inventory and overlap audit workflow for enterprise automation. It helps users determine which jobs, hooks, connectors, or wrappers are currently live, broken, redundant, or missing before making critical changes, ensuring reliable system architecture and clean code practices.

A structured workflow designed to prevent 'reinventing the wheel.' Before writing custom code, this skill guides the user through analyzing needs, conducting parallel searches across multiple channels (npm, PyPI, GitHub, MCP), evaluating candidate solutions based on maintainability and community support, and making a decision (Adopt, Extend, or Custom Build). It ensures that solutions leverage existing, proven libraries and patterns.

A comprehensive guide for configuring and deploying the RuView wireless sensing system. It covers firmware build-time settings (sdkconfig), runtime parameters (NVS provisioning), multi-node mesh setup, and integrating advanced edge intelligence modules (ADR-041). Essential for tuning system behavior and capabilities without modifying core code.

Provides comprehensive guidelines for authors submitting papers to Acta Automatica Sinica (AAS). It details how to organize and present anonymous, reproducible code, data, and experimental artifacts to enhance the credibility and rigor of research in control, automation, and pattern recognition, supporting the double-blind review process.

A comprehensive guide for researchers in electronics and signal processing preparing submissions to Acta Electronica Sinica (AES). It outlines best practices for proactively structuring, documenting, and disclosing research artifacts—including code, datasets, and hardware designs—to maximize the paper's credibility and chances of acceptance, even without mandatory journal badges. Covers anonymization, reproducibility standards, and proper disclosure in the manuscript.

This guide provides comprehensive best practices for ensuring scientific reproducibility in technical papers, particularly for electronics and information processing fields. It covers the critical pillars of documentation: environment setup, data sourcing and preprocessing, full algorithmic disclosure (including pseudo-code and hyperparameters), and robust statistical evaluation. By adopting these standards, authors can transform their work from potentially unverifiable results into credible, verifiable scientific contributions suitable for top-tier journals.

This comprehensive guide outlines best practices for achieving rigorous scientific reproducibility, especially for research involving storage systems and hardware dependencies. It mandates documenting not only the code but also the specific device models, firmware versions, operating states, and full measurement protocols to ensure verifiable results by peer readers.

This guide outlines the rigorous standards for achieving robust reproducibility in robotics research papers, especially those involving physical hardware. It details a two-tier approach, defining results as either fully *rerunnable* (code/simulation) or fully *auditable* (hardware logs/protocols). Key requirements include creating comprehensive hardware specification ledgers, logging detailed sensor data (rosbags), separating deterministic from stochastic claims, and structuring the entire repository for peer review.