Skill UI

This skill helps developers create highly focused, folder-specific documentation (`CLAUDE.md` and `AGENTS.md`). It systematically analyzes all files in a target directory to extract project purpose, operational constraints, naming conventions, and critical decisions. This deep context ensures that future AI agents (like Claude or Codex) operate with localized knowledge, preventing them from relying on global, generalized instructions and improving their accuracy in that specific domain.

This skill acts as a specialized expert for transitioning codebases across various frameworks, languages, versions, and platforms. It generates detailed migration plans, automated migration scripts, comprehensive risk assessments, and rollback strategies. Use it when upgrading technology stacks or moving code between disparate systems to ensure minimal disruption and maintain core functionality.

This skill acts as an expert CI/CD diagnostician for GitHub Actions. It analyzes failed workflow logs and pipeline definitions to identify root causes like missing secrets, environment mismatches, or syntax errors. It provides precise YAML or code changes to fix the pipeline, optimize performance, and update deprecated actions while ensuring security best practices.

Design Google Forms and wire Apps Script onFormSubmit triggers for email alerts, spreadsheet logging, and conditional handling — no custom integration or code editor setup required.

This skill creates or updates a shared Apple design context document for other HIG skills. It auto-discovers project details from code and assets, then gathers information on product overview, platforms, technology stack, design system, accessibility, and user context. The output is a structured markdown file used to tailor subsequent design guidance.

Maps, explains, and lints repository-scoped coding-agent instruction files such as AGENTS.md, CLAUDE.md, Copilot and Cursor rules. Run local read-only scans to inventory instructions, audit recursive imports, and export JSON or SARIF diagnostics before changing code.

Locate, select, and verify evidence in documents or code inventories. Supports source-span extraction, passage reranking, and claim checks. Preserves citations and handles no-match outcomes using graph tools for exact code lookup. Ideal for AI agents performing evidence-based reasoning.

Lambda is a compact, machine-native communication protocol specifically designed for agent-to-agent (A2A) messaging. It functions as a shared, unambiguous vocabulary built from 'atoms' (2-character codes), completely bypassing the need for human natural language interpretation. It is ideal for orchestrators, task delegation, and logging structured coordination signals (like heartbeats or state changes) in multi-agent systems. Crucially, Lambda is intended only for machine communication, not for user-facing dialogue.

This workflow guides developers to build and fix existing Laravel applications effectively. It emphasizes root-cause diagnosis, repository-native implementation, regression coverage, and risk-based verification. Suitable for feature implementation, bug fixing, and maintaining architectural conventions within established codebases.

This skill produces a step-by-step execution trace for specific code inputs, resolving names and tracking type transitions. It clarifies code behavior when users are confused about outputs or logic, highlighting non-obvious behaviors like implicit coercions and side effects without evaluating code quality. It helps users understand exactly what the code does.

This skill performs an autonomous repository-wide audit and fix pipeline. It audits code health, reviews logic, locates issues, explains findings, applies fixes, verifies diffs, and iterates until clean. It requires initial consent but runs hands-free afterwards, suitable for comprehensive code quality improvement and bug resolution.

Luna is an automated code reviewer focused on correctness, security, and reliability. She evaluates code against blueprints and checklists, identifying critical vulnerabilities like injection flaws, authentication bypasses, and race conditions. Luna acts as a quality gate, ensuring no unresolved high-risk issues proceed to QA or deployment. She excludes style preferences, focusing only on measurable risks to maintainability and safety.