Skill UI

This skill is designed to review Chinese skill documentation to ensure it meets publishing standards. It systematically checks the clarity of descriptions and the executability of process steps, providing structured feedback in Chinese while maintaining necessary machine enumeration values for technical integrity.

This skill executes a rigorous, deterministic integrity forensic audit using an external, pinned Anti-Autoresearch framework. It sweeps the submitted paper to detect potential inconsistencies, rule violations, and structural anomalies. The resulting verdict is converted into a typed policy gate (BLOCK/WARN/NO_NEW_BLOCKER) and an append-only obligations ledger, providing a final layer of assurance before submission or publication.

This tool performs a comprehensive audit of the project's stored auto-memory. It analyzes learned knowledge to identify actionable patterns for promotion to core rules, flag stale or outdated entries, and suggest opportunities for consolidating redundant knowledge. Use it after major development phases or when the memory knowledge base becomes crowded to maintain system knowledge quality.

This tool provides a comprehensive overview of your project's memory usage across all associated memory systems. It reports metrics such as line counts, topic file counts, capacity limits, stale entries, and actionable recommendations to help maintain memory integrity and optimize performance.

This utility is designed to reconcile the ADR (Architecture Decision Record) index when source files have been deleted or modified, preventing the accumulation of stale or dangling reference rows. Unlike simple upserts, this tool performs a hard purge of the entire index namespace (adr-patterns and adr-edges) and then fully rebuilds the index from the files currently present on disk. It is crucial for maintaining data integrity after deletions.

Ruflo is a powerful, cross-agent orchestration layer designed to manage complex, multi-step AI coding workflows. It supports coordinating large swarms of AI agents, managing persistent memory through semantic search, and executing sophisticated pipelines using 30+ specialized plugins. Ideal for large-scale development tasks, system benchmarking, and deep research requiring coordinated agent effort.

This skill guides users through a comprehensive, structured design process. It helps transform vague ideas or initial concepts into detailed, validated specifications by exploring project context, proposing multiple options with trade-offs, and iteratively refining architectural details. It is essential for defining clear boundaries and requirements before any code is written, ensuring a robust and well-documented development roadmap.

A powerful skill leveraging OpenAI Codex/GPT for autonomous, hands-off code generation, implementation, and complex task execution. It operates in sandboxed modes (Read-Only, Workspace-Write) to allow developers to refactor, fix bugs, and build features with minimal user intervention. Ideal for complex programming tasks, code reviews, and structured workflow automation.

This skill transforms unstructured task lists into a disciplined, protected daily schedule. It implements the Deep Work methodology by classifying tasks into deep and shallow categories, budgeting shallow work time, and creating an energy-first time-blocked plan. It enforces strict limits, such as a 4-hour deep work ceiling, ensuring focus remains the protected resource throughout the day. Includes logging and end-of-day shutdown rituals.

This tool takes vague, rambling descriptions of desired outcomes and transforms them into a single, highly polished, autonomous prompt. Instead of building the deliverable, it crafts the comprehensive prompt (including quality bars, tool mandates, and verification loops) that a fresh AI session can execute completely on its own. Ideal for structuring complex, multi-step AI tasks.

This skill provides a comprehensive, end-to-end workflow for addressing GitHub issues. It guides the user through crucial development stages: understanding the issue, researching prior art in the codebase, planning the fix, implementing changes in small commits, rigorous testing (including unit and regression tests), and finally, submitting a structured Pull Request (PR) using the GitHub CLI. Ideal for any developer maintaining codebases.

Executes a multi-agent, structured code review workflow for GitHub Pull Requests (PRs). It analyzes changes from multiple perspectives (e.g., bug scans, compliance with project guidance, git history, etc.), provides confidence scoring, and generates actionable, highly formatted feedback directly on the PR. Use this when a thorough, professional code review is required.