Skill UI

A protocol that provides LLM agents with persistent structural memory of a codebase. It maps the project as a directed graph, tracking not only dependencies (what imports what) but also the 'reasons' (why it connects). This allows agents to understand context, perform impact analysis, and navigate large repositories without loading the entire source code into the context window.

Frontend Blueprint is an AI design consultant that structures discovery before coding, asking targeted questions, collecting references, and validating direction to deliver pixel-faithful UI components only when the context is clear.

Uses the GitHub CLI to inspect failing PR checks in GitHub Actions, collect log snippets, draft a fix plan, and implement changes only after explicit approval so CI failures are resolved with clear context.

Attempts to expose an open-source context resource, but the current skill metadata only yields a 404 response, indicating the referenced content is unavailable or missing from the system.

This guide outlines best practices for creating high-quality, structured pull requests (PRs) using Git and GitHub CLI. It ensures that every PR description captures not only what changed, but also the 'why' and necessary context for reviewers, significantly improving the code review process and adherence to engineering standards.

This tool builds a tamper-evident, append-only log chain using SHA-256 hash chaining. By incorporating the previous entry's hash into the current entry, any modification to an old log entry will invalidate all subsequent hashes, enabling pinpoint tamper detection. It supports various log formats (syslog, JSON, plain text) and is essential for compliance, digital forensics, and verifying log authenticity in security operations.

This skill parses raw, verbose Simplicité log files from TXT format, filtering out noise and extracting only essential fields (e.g., timestamp, level, body). By converting logs into structured JSON, it significantly reduces the token size required for AI context optimization, making troubleshooting efficient and reliable.

This skill helps developers achieve maximum AI readiness by automatically detecting the project stack and seeding context using a single, precise goal sentence. It systematically fills the essential project context (the 6 Ws), requiring the user only to answer the few remaining knowledge gaps (like the 'why' or 'when'). This creates a portable, structured context file, ensuring AI models have a complete understanding of the project before coding, minimizing guesswork and maximizing efficiency.

This tool translates English or Japanese tech articles and texts into professional, natural, and fluent Chinese. It is designed for users who need high-quality translation of technical content, ensuring technical accuracy while maintaining readability. The output is strictly Chinese only, preserving formatting and keeping key technical terms untranslated.

A specialized downstream skill for complex CTF challenges, focusing on cryptographic analysis, steganography, and mobile system forensics. It guides the user through recovering complex data chains, such as decoding blobs, tracing cryptographic transforms (keys, IVs, nonces), inspecting hidden media payloads, and analyzing mobile trust boundaries (e.g., APK/IPA signing logic, local storage, SSL pinning). This skill is designed to be used only after a main sandbox orchestrator has established the operational context.

This skill is designed for deep forensic analysis of asynchronous processing chains, specifically focusing on 'drift'—behavioral or configuration differences between a synchronous request path and subsequent worker-executed tasks. Use it to trace payloads, inspect worker-only state, follow retry mechanisms, handle dead-letter queues, or connect enqueued items to eventual side effects (e.g., file writes, cache updates, emails). It is a specialized downstream tool, requiring prior orchestration context.