Skill UI

nano-pdf is a robust command-line interface (CLI) tool designed to modify PDF documents. Instead of requiring complex PDF editing syntax, users can provide natural language instructions (e.g., 'Change the title to X and fix Y') to make precise edits to specific pages of a PDF. This drastically simplifies the content modification process for technical users and automates complex document handling tasks.

An internal, language-agnostic probe designed for investigating complex stack traces when the source language is unknown. It analyzes the provided trace to output structured, language-neutral elements, including failure contract checks, minimal reproduction guidance, and specific patch targets. This tool is vital for robust failure analysis and debugging across diverse codebases.

An internal diagnostic tool designed for analyzing Go language panics and complex stack traces. It provides structured guidance, including Go-specific nil/error checks, minimal reproducible `go test` commands, and precise patch targets, enabling highly efficient root cause analysis and debugging for Go projects.

Analyzes any frontend codebase (React, Vue, Svelte, Angular, plain CSS, Tailwind) to document the complete design system. It extracts crucial visual elements—including color palettes, typography, spacing scales, and component patterns—directly from source files, without needing to build, render, or run the application. Ideal for design audits, visual language documentation, or migrating a project's identity into a design tool.

This skill allows users to query the current state of various AWS resources (including EC2, S3, Lambda, RDS, VPC, etc.) using natural language. It translates user intent into strictly read-only AWS CLI commands (e.g., `describe-*`, `list-*`). Crucially, this tool can only view data and is contractually incapable of performing any write, modify, delete, or mutation actions, ensuring maximum safety.

This skill helps users find alternative, equivalent, or related tools for Japanese Natural Language Processing (NLP). By providing a specific GitHub repository URL, owner/repo name, or tool name (e.g., mecab, fugashi), the system mines a bundled dataset and performs advanced semantic scoring based on shared categories, labels, and tokens. It expands the search via web research to provide comprehensive alternatives.

Provides expert guidance for authoring and editing Tabular Model Definition Language (TMDL) files directly within PBIP projects. This skill is useful for making quick text-level fixes (like descriptions, format strings, or adding measures) when specialized tools (like Tabular Editor CLI or Power BI Desktop) are unavailable, especially for Git workflow or manual adjustments.

A comprehensive technical reference detailing idiomatic and efficient coding practices for the C language. It covers critical topics such as safe memory management, pointer handling, robust error checking, and modern string manipulation techniques, helping developers write reliable and performant C code.

This skill provides explicit, pedagogical instruction on complex CrossFrame concepts. It is designed to move beyond simple definitions by structuring the output into plain language explanations, conceptual mapping, common misreading boundaries, real-world signals, and practical exercises. It is ideal for users who need deep, structured, and comprehensive understanding of complex theoretical frameworks.

This skill enforces a high standard for all generated code, prioritizing correctness, clarity, and idiomatic patterns across all languages. It helps minimize code bloat by removing unnecessary abstractions and verbose patterns, while also optimizing the agent's output by using targeted diffs and eliminating excessive conversational overhead. Use this for all code generation, refactoring, or review tasks.

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.

This skill generates complete, structured, and bias-free job descriptions (JDs) for any role. It guides users through providing essential inputs, ensuring the output includes sections like role summary, responsibilities, requirements, and compensation. Crucially, it performs an inclusive language review, advising against biased requirements (like years of experience) and gendered language, thereby improving hiring fairness and attracting diverse talent.