Skill UI

This skill executes the DeepSWE benchmark, a comprehensive coding-agent evaluation suite. It uses OpenRouter and mini-swe-agent to score various Large Language Models (LLMs) on 113 tasks, providing reproducible results for model comparison, development assessment, and research benchmarking. Use this when a standardized, deep evaluation of LLM coding capabilities is required.

This skill guides users through complex, multi-step processes such as installation, configuration, or initial setup. It breaks down the process into small, atomic actions, presenting only the current required step and a limited, manageable checklist of remaining tasks. Ideal for onboarding new users or executing lengthy technical setups.

This skill is designed to rewrite a previous, lengthy response into a significantly more concise version. It ensures that the core meaning and all essential information are preserved, making it perfect for users who need a quick summary or a TLDR (Too Long; Didn't Read) version without losing critical details.

This skill fetches raw text transcripts from any YouTube video. It prioritizes using the DeepAPI service for reliable, server-side scraping, which bypasses local bot detection issues. If DeepAPI fails or the API key is unavailable, it gracefully falls back to using the local `yt-dlp` tool. The output is always saved as a clean, unformatted .txt file.

This skill builds and maintains a persistent index and dependency graph for all Architecture Decision Records (ADRs). It handles both v3-style and YAML plugin-style ADR formats, persisting records into the adr-patterns namespace and relationships (like supersedes, amends) into adr-edges. It is essential for bootstrapping ADR tracking, synchronizing the knowledge graph, or ingesting records from external projects, ensuring a single source of truth for architectural history.

This comprehensive guide outlines best practices for building reliable and robust AI agent harnesses. It covers optimizing action spaces, defining structured tools, managing observation formats, and implementing essential error recovery contracts. Use these principles to dramatically improve agent completion rates, manage context budgets effectively, and transition AI prototypes into stable, production-grade autonomous systems.

This framework guides AI agents to critically assess their own output across five key axes: Accuracy, Completeness, Clarity, Actionability, and Conciseness. It provides a structured, evidence-based scorecard and concrete suggestions for improvement, turning every task completion into a deep learning opportunity.

Generates a precise, evidence-based installation plan for a project's component catalog (ECC). Instead of loading a full, noisy bundle, this skill analyzes the repository's actual code structure, dependencies, and CI/CD evidence to classify skills, rules, and commands into 'DAILY' (essential) and 'LIBRARY' (referenceable) buckets. It ensures only necessary components are active, optimizing the development environment.

This comprehensive guide outlines industry best practices for designing robust and consistent REST APIs. It covers proper resource naming conventions, standardized HTTP method semantics, detailed HTTP status codes (e.g., 201, 404, 422), structured response formats (including pagination and links), and advanced strategies like cursor-based pagination. Essential for building scalable and maintainable APIs.

This tool automatically generates comprehensive API documentation directly from source code, supporting TypeScript and JavaScript. It scans all public exports (functions, classes, interfaces) and extracts detailed JSDoc annotations. Furthermore, it can generate structured OpenAPI 3.0 specifications for HTTP endpoints, ensuring all public APIs are documented and compliant with industry standards.

This function executes a full, autonomous iteration of the Claude Code workflow. It systematically checks the current status, predicts the optimal next action (e.g., editing code, running tests, spawning agents), executes the task, logs the results, and schedules the next wakeup. It discovers and processes tasks from team lists, swarm lists, and file checklists, ensuring continuous and automated progress toward a defined goal.

A comprehensive guide covering scalable backend architecture patterns, including Repository, Service, and Middleware design. It details best practices for building robust APIs using Node.js, Express, and Next.js. Topics covered include RESTful API structuring, database query optimization (preventing N+1 issues), implementing database transactions, and integrating caching strategies like Redis. This guide is essential for developing high-performance, maintainable server-side applications.