Skill UI

This tool generates production-grade, spec-compliant agent definition files (.md) adhering to the Anthropic 2026 schema. It supports both the creation of new custom subagents and the validation of existing agent files against marketplace compliance rules. Use it when building complex agent architectures, reviewing agent quality, or ensuring agents meet high industry standards.

This comprehensive checklist guides the safe and robust deployment of Cohere API v2 integrations from staging to production. It covers critical steps including API key management, code quality standards, performance tuning, implementing health checks and circuit breakers, and defining full rollback procedures, ensuring mission-critical AI features are reliable for launch.

This detailed guide outlines a production-ready, layered architecture for building complex AI applications using the Cohere API v2. It covers implementing Retrieval-Augmented Generation (RAG) pipelines, advanced tool-use agents, and multi-model orchestration. Use this structure when designing new Cohere integrations or establishing robust architectural standards for enterprise AI solutions.

This guide provides production-ready code patterns for integrating with the Cohere AI API v2 using TypeScript and Python. It covers essential best practices, including singleton client management, robust retry mechanisms, type-safe chat wrappers, streaming, embedding, reranking, and structured JSON output. Use this when establishing team coding standards or refactoring existing Cohere integrations.

This skill guides the secure handling and lifecycle management of large-scale data artifacts, including training datasets, model weights, and checkpoint snapshots, within GPU cloud environments. It provides best practices for data pipelines, covering secure import via Kubernetes PVCs, compliant export, and stringent data validation, ensuring adherence to encryption standards (AES-256) and regulatory requirements (PII masking, RBAC).

This benchmark evaluates Vision-Language Models (VLMs) on detecting potential safety hazards within indoor environments. It uses static camera frames, simulating real-world fixed security camera monitoring. The test covers 40 scenarios across 5 critical categories, including fire/smoke, electrical risks, trips/falls, child safety, and falling objects, providing a rigorous measure of VLM's practical safety inspection capability.

This guide provides advanced search strategies for tracking Asian music, including K-pop, J-pop, and Filipino genres. It details how to construct highly specific queries by incorporating regional keywords, understanding idol comeback cycles, and utilizing cultural details (such as zodiac or national identity) to narrow down search results and avoid generic noise.

This tool functions as a Command Line Interface (CLI) alias for implementing the specific 'pua:mama' protocol within advanced coding models like Codex or Claude Code. It loads and enforces a structured 'mama skill/protocol,' maintaining rigorous verification standards while adjusting the visible narrative style to a specific 'Chinese-mom nagging mode.' It manages configuration by interacting with `~/.pua/config.json`, ensuring existing user fields are preserved.

This advanced agent simulates a rigorous, multi-perspective peer review process for academic manuscripts, mimicking the standards of an international journal. It dynamically deploys a team of five specialized reviewers—including an Editor-in-Chief, three domain experts, and a Devil's Advocate—to critically evaluate methodology, domain fit, originality, and core arguments. It supports full submission review, revision verification, and reviewer calibration, providing a structured editorial decision and comprehensive revision roadmap.

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

This meta-skill automates the entire process of creating a professional, high-quality AI short drama video from a simple topic or prompt. It intelligently drafts a shot-by-shot shooting script, maintains character and scene consistency across all shots, generates comprehensive visual assets (identity reference, per-shot compositions), and compiles the final MP4 with burned subtitles and title cards. Suitable for content creators, marketers, and storytellers needing rapid, consistent visual narratives.

This professional tool helps developers and researchers identify the optimal AI model for a specific task, such as coding, reasoning, or image recognition. It queries official HuggingFace leaderboards, filters models based on hardware constraints (e.g., available VRAM or RAM), and provides a detailed comparison table including benchmark scores, parameter size, and licensing information.