Skill UI

This tool audits the token consumption across all components (agents, skills, rules, MCP servers) within a Claude Code session. It analyzes token overhead, identifies bloat (e.g., overly long descriptions, redundant components), and generates a detailed context budget report. Use it to maintain optimal context window usage, improve performance, and prevent context saturation.

This skill systematically probes an AI model's core behavioral patterns across six critical dimensions, including reasoning, refusal, formatting, and grounding. It generates a comprehensive, styled HTML report with radar charts and actionable insights, allowing users to understand a model's true capabilities, compare different models, or perform compliance and red-team assessments without needing an external API key.

AKF is the native metadata format specifically designed for tracking AI-generated or AI-modified content. It allows users to stamp files (including DOCX, PDF, images, and code) with detailed trust scores, source provenance, and compliance metadata. This tool is essential for professional workflows requiring strict auditing, compliance assurance, and verifiable data lineage, especially concerning regulations like the EU AI Act, HIPAA, and SOX.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.

This skill provides comprehensive analysis and structured data extraction for academic research papers hosted on Hugging Face and arXiv. It can read paper content in markdown format, retrieve key metadata (authors, abstracts, GitHub repos), and identify linked assets such as associated models, datasets, and Spaces. Use this when the user provides a Hugging Face or arXiv URL, or explicitly requests a summary or explanation of an AI research topic.

Permanently deletes the context-mode knowledge base and resets session statistics. Users can choose between a project-wide wipe (deleting all indexed content and session data) or a targeted session wipe, ideal for isolating polluted data without losing project history. Note that this action is highly destructive and irreversible.

A comprehensive guide and function for discovering, searching, and securely installing modular agent skills from an open ecosystem. It helps users identify specialized capabilities needed for domains like web development, testing, or DevOps, utilizing the `npx skills` CLI. The skill emphasizes best practices, including checking skill reputation and requiring explicit user confirmation before running code.

This skill acts as an advanced extension layer, implementing a self-evolution protocol, maintaining session state continuity (Compaction), and providing KPI-style performance summaries. It allows users to simulate continuous professional development, track metrics, and master complex role-playing and interaction skills across multiple sessions, ensuring performance baselines are continuously adjusted and internalized.

This skill enables the generation of high-quality, cinematic videos using a highly structured prompt format (JSON). Users define detailed cinematic elements—including background, characters, camera movements, dialogue, and audio—and can optionally provide a reference image to guide the visual style or content of the final output. It automates the complex AIGC video generation workflow.

This utility compresses natural language documents (notes, preferences, memory files) into a highly condensed, 'caveman-speak' format. Its primary function is drastically reducing input tokens for efficiency while meticulously preserving all technical substance, including code blocks, URLs, file paths, and markdown structure. Ideal for optimizing memory consumption when interacting with AI models.

Conducts comprehensive AI prompt safety reviews that analyze bias, misinformation, injection, and privacy risks while delivering effectiveness evaluations, security guidance, and best-practice recommendations for prompt engineers.

This skill guides users on setting up and running LLM-as-judge evaluations using Arize. It covers defining evaluators (including prompts, classification choices, and models) and executing tasks against real data spans or experiments. It supports key metrics like hallucination, faithfulness, and correctness, and facilitates continuous monitoring for comprehensive LLM performance analysis.