Skill UI

This skill generates a comprehensive, professional brief designed for influencer marketing campaigns. It systematically structures all necessary components—including defined campaign goals, target audience profiles, specific content deliverables (e.g., Reels, Stories), creative guidelines, and key messaging—making the output ready to send directly to creators, talent agencies, or brand partners.

This skill guides the deployment of decoy files, or "canary tokens," across critical file systems. These files act as tripwires, monitoring for unauthorized changes (modification, deletion, renaming). By tracking these high-value decoys, systems can detect ransomware encryption activity in real-time, providing a low false-positive, high-fidelity detection layer that complements traditional EDR and backup solutions.

This skill details the process of generating comprehensive forensic super-timelines using Plaso's log2timeline tools. It fuses diverse artifacts—including file-system MACB times, registry data, EVTX, and browser histories—into a single, normalized chronological view. This is critical for reconstructing the full sequence of events on compromised or forensically imaged systems during Digital Forensics and Incident Response (DFIR) investigations, helping analysts detect anti-forensic activity.

This comprehensive guide covers the process of creating, analyzing, and securing Software Bills of Materials (SBOMs). It utilizes Syft to generate standards-compliant SBOMs (CycloneDX or SPDX) from container images and filesystems. The workflow details correlating these SBOMs with known CVEs using Grype for vulnerability analysis, and subsequently signing the artifacts with Cosign. This process is crucial for establishing supply chain trust, ensuring dependency visibility, and integrating vulnerability gating into CI/CD pipelines.

Trivy is an open-source, comprehensive security scanner designed for DevSecOps. It assesses container images, Infrastructure-as-Code (Terraform, K8s), filesystems, and SBOMs for vulnerabilities (CVEs), misconfigurations, hardcoded secrets, and license compliance. It is essential for implementing 'shift-left' security gates in CI/CD pipelines, ensuring that code and infrastructure are secure before deployment.

This advanced protocol forces the AI to conduct a thorough context audit before attempting any complex task. It mandates reading every relevant file, identifying architectural gaps, and providing a detailed orientation statement. It is essential for multi-file modifications, system design, and debugging to prevent acting on assumptions and maintain high code accuracy. The AI cannot proceed until all unknown variables are resolved.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

This skill guides users through an in-depth interview process to achieve maximum project context completeness (100% "Gold Code") for their .faf file. By systematically asking questions about project goals, architecture, and technology stack, it ensures all critical context slots are filled, making the project fully structured and highly readable by AI systems.

This skill is designed to identify subtle and complex logic bugs within a single code file or function. It employs a semi-formal execution tracing methodology, systematically breaking down the suspected bug into five stages: Premises (expected state), Trace (execution path), Divergence (actual deviation), Trigger (reproduction case), and Remedy (fix). It is ideal for scenarios where users suspect code is wrong but cannot specify a concrete failure point.

KubeEye is a comprehensive cluster inspection tool designed for Kubernetes environments, particularly within KubeSphere. It systematically detects issues across workloads, nodes, and configurations by integrating multiple policy engines, including OPA/Rego, PromQL, file integrity checks, kernel parameter validation, and systemd health checks. Users can define custom inspection rules (InspectRule), schedule checks (InspectPlan), and retrieve detailed reports (InspectResult) to ensure cluster compliance and stability.

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

Automates the creation of Architecture Decision Records (ADRs). This tool manages the entire lifecycle of an ADR: determining the next sequential ID, generating a standardized Markdown file, storing the record in the AgentDB graph, and automatically linking it to related decisions. Essential for documenting significant architectural choices, API designs, and data model changes to maintain system knowledge coherence.