Skill UI

Clarity Gate is a pre-ingestion verification system designed for Retrieval-Augmented Generation (RAG) workflows. It ensures documents entering knowledge bases are properly qualified by checking for uncertainty markers and distinguishing assumptions from facts. This skill produces Clarity-Gated Documents (CGD) and validates Source of Truth (SOT) files. It helps prevent hallucinations by enforcing epistemic quality standards before data ingestion. Note that it verifies document form rather than factual truth, requiring human-in-the-loop validation for accuracy.

This skill reviews generated or changed production code against Clean Code principles (SOLID, DRY, KISS, YAGNI) and LLM-specific failure modes. It operates in guard-pass, live, and review modes to ensure code quality before delivery, fixing violations and preventing systematic AI errors.

This skill automates Kimi Code execution in non-interactive mode within a bounded repository. It parses event streams, handles authentication securely, and manages sandbox permissions. Use it to delegate specific coding tasks to Kimi Code while ensuring safety constraints like timeouts and directory limits. It prevents interactive UI launches and avoids credential exposure.

Runs OpenCode headlessly via CLI to analyze repositories and perform coding tasks, returning JSON event streams. Use when delegating work to OpenCode or comparing it with another coding agent.

Executes Pi in a non-interactive sessionless mode, parses its JSON event stream, and handles authentication and tool permissions. Useful for delegating bounded analysis or implementation tasks to Pi.

This skill enables orchestrating agents to delegate bounded coding tasks to the OpenAI Codex CLI. The orchestrator drafts the brief, runs the command, and maintains full responsibility for reviewing diffs and committing changes. It prioritizes safety by preventing automatic commits, requiring explicit user requests and manual gate verification.

Context Kit is an external framework designed to organize and manage personal, durable context data (like identity details, memories, and domain knowledge) into secure Markdown artifacts. It helps users set up robust knowledge bases for coding agents (e.g., Claude Code), guiding them on best practices for data privacy, local storage, and safe integration, while preventing sensitive data leaks.

This skill covers strategies for optimizing Large Language Model context windows to prevent context rot. It includes techniques like summarization, trimming, routing, and prioritization to manage token budgets effectively. Developers can use these methods to build multi-turn conversation systems where context length is a constraint, leveraging tools like tiktoken and LangChain for implementation.

This skill allows you to delegate bounded coding tasks to the GitHub Copilot CLI while retaining review and landing responsibility. You write a brief, dispatch the task via a relay script, wait for completion, review the generated diff against your requirements, and finally commit the changes. It requires the Copilot CLI installed and authenticated, along with Node.js and Git. This approach ensures human oversight in the coding process, preventing silent failures and maintaining control over code quality.

This skill guides you in implementing Command Query Responsibility Segregation (CQRS) patterns for scalable software architectures. It focuses on separating read and write models to optimize query performance and support event-sourced systems. Use this when you need to scale reads independently from writes or handle complex query scenarios. It is suitable for building high-performance reporting systems where detailed read/write data models are required.

This tool diagnoses and validates complex cron expressions (e.g., crontab, Kubernetes CronJob) to prevent silent scheduling failures. It identifies five common pitfalls: impossible dates, misunderstood OR semantics, resource spikes, uneven step drift, and leap year issues. Users can check the syntax, understand the true meaning, and calculate future execution times reliably.

This comprehensive guide covers best practices for building robust database systems. It teaches crucial concepts like schema normalization, effective indexing strategies, selecting the right database (e.g., SQLite vs PostgreSQL) and ORM, and optimizing queries to prevent common performance issues like N+1 problems. It emphasizes making context-aware architectural decisions.