Skill UI

This skill analyzes excessively large Pull Requests (PRs) that exceed established line budgets (e.g., 400 lines). It automatically splits these massive changes into smaller, focused, and sequential PRs using advanced chaining or stacking strategies. This process is crucial for protecting reviewer focus, ensuring reviews can be completed within a manageable time budget (e.g., ≤ 60 minutes), and maintaining high code quality during complex feature integration.

A rigorous, standalone tool designed for explicit, blind, and adversarial code review. It executes the code target through two parallel, independent judges (Judge A and Judge B) with strict rules and limited fix rounds. This process ensures maximum scrutiny, providing a definitive, highly corroborated verdict (APPROVED or ESCALATED) that replaces standard review methods.

This document defines the critical architectural contract for standardizing advisory transport across diverse AI code review runtimes (e.g., Claude Code, OpenCode, Codex). It mandates that all integrations adhere to a shared Go contract, governing how review results, evidence, and validation are processed. This is essential for ensuring system integrity, managing architectural transitions, and preventing the creation of isolated, proprietary review systems.

Apply Martin Kleppmann-inspired rules for designing reliable, scalable, and maintainable data systems. Covers consistency, replication, partitioning, transactions, streams, batch processing, and schema evolution. Use when making architecture decisions for data-intensive applications.

Knap is a command-line tool that renders Markdown templates using structured data like JSON or CSV. It supports variables, filters, and logic to batch-generate notes, format Defuddle output, and validate templates. Ideal for developers automating note creation and content processing workflows.

Design custom laboratory hardware components using parametric Python models with build123d. Export fabrication-ready files like STEP, STL, and DXF for microfluidic chips, mounts, and adapters. Ensure precise interface dimensions against standards for compatibility with lab equipment. Use for physical parts mating with standardized labware, optical tables, or manufacturing processes.

This skill helps maintainers verify reported bugs in the mcp-toolbox project. It involves analyzing issue reports, checking versions and configurations, and attempting local reproduction to determine if the bug is real, fixed, or misconfigured. The process requires citing specific evidence like file paths and commit SHAs to support verdicts such as confirmed, already-fixed, or client-side issues. It ensures efficient bug triage without unnecessary infrastructure setup.

This skill helps identify issues in Superpowers sessions by analyzing transcripts and evidence. It assists in troubleshooting problems like repeated work, ignored plans, or unexpected costs. The process involves locating session files, triaging dimensions, generating detailed reports, and optionally creating GitHub issues or export bundles for maintainers. All findings must cite specific paths and lines.

This deployment skill is designed to be integrated into the Playwright testing workflow. Its primary function is to validate the loading process of shared skills across the entire environment during the startup phase. It ensures that all necessary components and modules are correctly registered and available for reliable End-to-End (E2E) testing execution.

Comprehensive guidelines for DevOps workflows including infrastructure design, CI/CD pipelines, container management with Docker and Kubernetes, health checks, rollback strategies, feature flags, and mobile release processes. Ensures production readiness and security compliance across various environments.

This is a synthetic, instructional fixture designed solely for verifying the delivery and access of community skills within the OpenSquilla live Skill Hub canary environment. It is crucial for testing platform capability (e.g., resource access validation) and should not be executed for actual functionality or command processing.

This skill enables deterministic programmatic PDF operations including text and table extraction, merging and splitting pages, filling form fields, and generating new PDFs from JSON data. It utilizes libraries like pypdf, pdfplumber, and reportlab to handle structured PDF work efficiently. Ideal for automating document processing tasks such as report analysis, form completion, and document consolidation within a Python environment.