Skill UI

This skill reviews git diffs or specific file scopes for code reuse, quality, efficiency, clarity, and standards compliance. It optionally applies safe, behavior-preserving fixes. It uses parallel read-only sub-agents to inspect code while the main agent applies changes. Suitable for auditing changes, simplifying code, or refactoring.

This skill enables parallel read-only multi-agent review of git diffs or specific file scopes. It identifies behavioral regressions, security risks, performance issues, and coverage gaps using four specialized sub-agents. The main agent synthesizes findings to filter and summarize critical issues without modifying code. Ideal for ensuring code quality and safety before merging changes.

Coordinate multi-layer security scanning and hardening across application, infrastructure, and compliance controls using defense-in-depth strategy. Orchestrates specialized security agents for comprehensive assessments, layered controls, and continuous monitoring. Follows DevSecOps principles with shift-left security, automated scanning, and compliance validation to create a resilient security posture addressing vulnerabilities and future threats.

This skill enables AI agents to search, import, and analyze video content using Memories.ai's Large Visual Memory Model. It provides persistent video intelligence, allowing videos to be indexed once and queried repeatedly. Supports social media import from TikTok, YouTube, and Instagram, meeting summarization, knowledge base building, and cross-video Q&A via Memory Augmented Generation. Ideal for analyzing video URLs, researching social media trends, and summarizing recordings.

Audit-first SEO/AEO growth workflow for coding agents: discover the project, audit technical and content issues, implement fixes, research search intent, build foundational pages, set up measurement, deploy, verify, and optionally monitor weekly.

This skill reviews code differences (diffs) to identify opportunities for improvement in clarity, reusability, efficiency, and overall quality. It employs multiple specialized sub-agents (e.g., Code Reuse, Efficiency, Quality) to audit changes. It can provide high-confidence, behavior-preserving fixes when requested, ensuring the code adheres to best practices without introducing regressions. Best used when refactoring, cleaning up, or auditing a specific code scope.

SkillCheck validates SKILL.md files against the agentskills specification and Anthropic best practices. It performs read-only checks on structure, semantics, naming, and quality to catch errors before publishing. The tool provides a score, grade, and detailed report with fix suggestions for critical issues to ensure high-quality skill authoring.

This tool audits the activation metadata of coding skills used by AI agents. It grades skills (A-F) based on description clarity, simulates which skill a prompt will trigger, and identifies metadata collisions where one skill might silently overshadow another. It is essential for ensuring robust, predictable, and reliable AI agent behavior during development and CI processes.

Freezes PRD, technical design, and acceptance criteria before medium-to-large Codex work, then coordinates multiple agents with explicit file ownership and judges delivery from diffs, tests, and evidence-backed acceptance.

Coordinates a pre-task intelligence protocol that activates all relevant agents, synthesizes parallel analyses, estimates real execution time, maps probable risks, and produces a contingency-aware plan before executing the user’s request.

Detects which APIs of a Python project's pinned dependencies changed after a coding model's training cutoff, locates the code using them, and writes short evidence-tagged notes into AGENTS.md or CLAUDE.md.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.