Skill UI

This meta-skill runs three independent code reviewers—safety, test coverage, and style—in parallel over the current uncommitted diff. It then arbitrates a single, authoritative verdict (BLOCK, BLOCK_WITH_OVERRIDE, or PASS_WITH_NOTES) based on strict priority rules, offering a comprehensive second opinion before committing code.

This tool is designed to generate a concise and academically rigorous abstract for completed research papers. By taking the revised body, the function synthesizes the core problem, methodology, key results, and overall significance. It adheres strictly to defined style preferences, audience needs, and outputting a pure LaTeX fragment, ensuring the summary is accurate and professional.

This specialized tool revises fragmented drafts of academic papers (including Introduction, Method, Results, and Discussion) into a single, cohesive, and professionally structured LaTeX body. It ensures strict consistency across terminology, metrics, and tone, while adhering to specific preferences like audience, venue style, and required citations. It is designed for academic authors preparing the main manuscript body before writing the abstract.

Drafts a complete, publication-ready research paper section (e.g., Introduction, Method, Results) as a robust LaTeX fragment. This tool ensures the section adheres strictly to a predefined writing plan, maintaining narrative coherence, managing citation budgets, and slotting seamlessly into a larger academic manuscript.

This specialized tool is designed to curate and structure academic source material for long-form research papers. It takes paper topics, user preferences, search results, and bibliography entries as input. The output is a highly structured source pack, clearly identifying primary, supporting, and excluded sources, along with coverage notes to ensure research depth and methodological rigor.

This tool automates the diagnosis of production errors tracked by Sentry. By leveraging the Composio CLI, it retrieves comprehensive issue details, full stack traces, breadcrumbs, and associated commits. It maps stack frames to local source code, allowing agents to propose fixes and triage issues directly, eliminating manual copy-pasting.

A comprehensive guide to authoring, optimizing, and validating Power Query M expressions used within semantic model partitions. This resource covers crucial topics such as maintaining query folding for performance, applying early filtering and column pruning, and leveraging the Power Query API for robust data validation and debugging.

This skill provides a comprehensive guide for building scalable Retrieval-Augmented Generation (RAG) pipelines and persistent agent memory using Pinecone as the vector database. It covers index setup, embedding large corpuses, implementing dense and hybrid semantic search, and advanced metadata filtering for real-world production systems. Use this for building searchable knowledge bases and context-aware AI agents.

A comprehensive reference guide providing quantitative benchmarks and formulas for system architects. It covers user-to-RPS conversion, storage estimation, bandwidth calculation, known scale limits of major technologies (databases, queues, caches), and capacity planning for various user loads (1K DAU to 10M DAU). Essential for designing scalable, high-availability applications.

This skill optimizes articles for Answer Engine Optimization (AEO), ensuring content is structured for maximum extractability by AI models like ChatGPT and Perplexity. It rewrites vague headings into direct questions, inserts self-contained 50-80 word answer capsules, audits and trims long paragraphs, and highlights trust signals, making your content highly readable and citable by AI search engines.

This comprehensive canvas guides teams through developing robust AI-powered features. It ensures product decisions are rigorous by covering problem definition, model selection (LLM, RAG, etc.), data governance, evaluation metrics, user experience design (including fallbacks), and responsible AI compliance. Ideal for assessing AI readiness and preventing common product failures.

This skill generates a highly customized code review checklist for any pull request. Instead of using a generic template, it adapts the review depth and specific checks based on the target language (e.g., Python, TypeScript), the nature of the change (e.g., bug fix, refactor), and the associated risk level (low, medium, high, critical). It ensures comprehensive coverage, guiding reviewers to focus on critical areas like security, correctness, and architectural integrity.