Skill UI

This meta-skill coordinates the entire design lifecycle, ensuring that ideas are systematically refined through mandatory stages: brainstorming, risk assessment, and multi-agent review. It acts as a process gatekeeper, enforcing adherence to structured steps and mitigating risks before allowing implementation, thus preventing premature or flawed execution.

Guides implementing Kubernetes NetworkPolicy, RBAC, PodSecurityPolicy, Pod Security Standards, and OPA Gatekeeper constraints to enforce defense-in-depth across clusters, covering multi-tenant protection, admission control, and Istio service mesh policies.

This guide details the deployment and usage of OPA Gatekeeper, a powerful Kubernetes admission controller. Gatekeeper enforces custom policies written in Rego using ConstraintTemplates and Constraints. It validates, mutates, or denies resource requests at admission time, ensuring that non-compliant workloads are blocked before scheduling. Ideal for organizations requiring robust, code-based policy enforcement to meet strict security and compliance standards.

This guide details implementing Policy-as-Code using Open Policy Agent (OPA) and Gatekeeper. It covers writing complex Rego policies, deploying OPA Gatekeeper as a Kubernetes admission controller, and integrating policy testing into CI/CD pipelines to enforce security standards across clusters. Ideal for DevSecOps practitioners requiring robust governance.

This skill acts as a critical gatekeeper after a set of experiments are completed. It systematically evaluates raw experimental metrics and outcomes against predefined research claims. The goal is to determine whether the results adequately support a claim, contradict it, or if critical evidence is missing. It guides the research team towards the next decisive action: pivoting the hypothesis, supplementing the data, or confirming the findings. Use this before writing final reports.

A powerful pre-action hook designed to enforce rigorous checks before AI agents modify code, create files, or execute destructive commands. Instead of relying on superficial self-evaluation, GateGuard forces the LLM to gather concrete facts (like dependencies, data schemas, and impacted modules), significantly improving code reliability and architectural depth. It provides multiple gates for edits, writes, and destructive bash commands.

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.

A comprehensive playbook detailing advanced techniques for bypassing core macOS security features, including TCC, Gatekeeper, SIP, and sandbox restrictions. This guide is essential for authorized red team operations and penetration testing targeting macOS endpoints, covering methods like direct database manipulation, extended attribute removal, and privilege escalation paths.

This skill enforces an algorithm-first discipline, requiring the user to state the time and space complexity (Big-O), the intended data structure, and the core algorithm family before writing any complex code (loops, queries, recursion). It serves as a critical gatekeeper to prevent common performance pitfalls like O(n²) complexity, N+1 database queries, and brute-force implementation errors, significantly improving code robustness and efficiency.

This tool guides researchers on whether their academic topic aligns with the scope of an empirical microeconomics journal, specifically the Journal of Human Resources (JHR). It emphasizes that the research must focus on policy-relevant, causal questions in core fields (e.g., labor, health, education), strictly differentiating it from purely organizational behavior or 'HR management' studies. It serves as a critical gatekeeper before formal submission.

This is a comprehensive, final preflight checklist designed for submitting scientific manuscripts to PNAS. It guides authors through every critical stage, including assessing broad significance, choosing the correct submission track, optimizing the abstract and significance statement, verifying data integrity, handling ethics approvals, and ensuring all required files and metadata are compliant. It acts as a single gatekeeper before submission.

A comprehensive, final quality control checklist designed for submitting manuscripts to PNAS Nexus. It serves as a master gatekeeper, ensuring compliance across fit, open access licensing (APC/CC BY), structural integrity, statistical rigor, data deposition (DOI/accession numbers), and required metadata. It helps writers confirm nothing is missing before final submission.