Skill UI

A comprehensive guide for integrating Microsoft Graph SDK into applications. It covers accessing Microsoft 365 data (users, mail, calendar, Teams, SharePoint) from various languages (.NET, Python, TypeScript). Key topics include robust authentication patterns (Managed Identity, OBO), advanced data retrieval techniques like pagination, delta queries, batch requests, and setting up change notifications via webhooks. Learn best practices for secure and efficient API interaction.

This engine aggregates news from multiple RSS sources (e.g., TechCrunch, The Verge) and performs advanced sentiment analysis using AI (Claude). It ranks articles by industry impact, generates structured summaries, and provides actionable commentary, making it ideal for daily tech/AI briefing preparation and market monitoring.

This skill searches GitHub and the web to discover the latest Japanese Natural Language Processing (NLP) resources, tools, and libraries (e.g., NER, LLM, TTS) that are not yet included in the awesome-japanese-nlp-resources list. It is designed for developers and researchers who want to track the cutting edge of Japanese language technology or prepare a contribution.

This advanced skill provides comprehensive patent prior-art and landscape intelligence. Instead of generic searches, it forces the user to commit to one of five specialized sub-use-cases: Novelty Search, Freedom-to-Operate (FTO), Competitive Landscape, Acquisition Diligence, or Litigation Prior-Art. It searches multiple global databases (Google Patents, Espacenet, USPTO) and outputs a detailed, actionable DOCX report with strategic recommendations, risk assessments, and technical verdicts.

This powerful skill pauses deep technical discussions to conduct a comprehensive, multi-dimensional reassessment. It analyzes conceptual drift, identifies unverified assumptions, and detects potential cognitive biases (e.g., confirmation bias, sunk cost fallacy). Use it when the conversation is becoming overly detailed, or when a strategic check-in is needed to ensure the focus remains on the core objective and the correct problem is being solved.

A detailed technical workflow for inspecting complex 3D animation, rigging, and motion states within Blender. This skill moves beyond visual inspection by extracting structured data—such as bone transformations, world-space bounding boxes, ground contacts, and axis conventions—to accurately diagnose issues like foot sliding, rotation errors, or retargeting mismatches in professional animation pipelines.

This tool generates an extensive, long-form cheat sheet for specialized Machine Learning or Large Language Model (LLM) topics. It provides comprehensive technical deep dives, including theoretical formulas with derivations, from-scratch PyTorch code implementations, detailed comparison tables, and 25 high-frequency interview questions spanning L1 (Must-Know) to L3 (Expert Lab) difficulty.

A comprehensive coaching layer designed to elevate users into power users of AI models. Upon first activation, it provides a structured glossary of high-impact techniques tailored to user needs. In subsequent turns, it actively monitors prompts, surfacing single, actionable tips to optimize interactions and maximize the AI's capabilities, ensuring continuous, guided learning.

This technical skill is designed to verify the compatibility of the Codex CLI with skills compiled from MDA source. Loading this skill confirms that the CLI loader successfully parses the SKILL.md format, adhering to the agentskills.io v1 envelope subset. It is primarily used for testing build pipelines, metadata integrity, and ensuring robust compatibility between MDA compilation and runtime ingestion.

This demo skill verifies the compatibility of MDA-compiled SKILL.md files within the Claude Code environment. It confirms that the skill's frontmatter, metadata structure, and allowed tools are correctly parsed and honored by the loader, ensuring smooth integration of compiled content into the agent workspace. It is a technical utility for development validation.

This tool accesses a persistent FTS5 knowledge base containing previously indexed project documentation, local content, and session memory. Instead of feeding entire raw sources into the conversation context, it allows users to perform targeted, efficient searches using specific queries and source scoping. This is ideal for quickly retrieving technical details, project specifications, or historical context without lengthy re-reading.

This skill converts complex, narrative technical documents, research excerpts, or natural language problem descriptions into a highly structured Mathematical Problem Specification (MPS). It systematically extracts variables, constraints, objectives, and uncertainty parameters. By adhering to a strict Zero-Inference Protocol, it ensures all extracted elements are strictly grounded in the source text, making it invaluable for formalizing real-world optimization, data science, and engineering problems.