Skill UI

This skill automates the Test-Driven Development (TDD) repair loop. Given a failing unit test, it spawns a bounded, headless LLM agent to automatically modify the source code until the test passes. It closes the development loop by treating the test's pass/fail status as the definitive fitness function, ideal for CI failure triage and local TDD workflows.

This skill guides users through evidence-based terminal operations. It is designed for scenarios requiring direct command execution, Git status checks, debugging CI/build failures, and pushing narrow, verified fixes. It emphasizes following a structured, minimal-scope workflow and accurately reporting the repository's current state (e.g., local changes vs. pushed code).

This skill dispatches computationally intensive quantitative trading jobs (such as multi-year walk-forward analysis, large Monte-Carlo simulations, parameter sweeping, or deep model training) to a managed cloud agent runtime. It is designed for large-scale, production-grade backtesting that exceeds local machine capacity, ensuring robust execution and artifact management via Anthropic's cloud services.

Tree Ring Memory is a local-first, framework-agnostic layer for managing durable knowledge in AI agents. It provides structured methods for recalling past decisions, recording evidence, auditing project state, and deliberately forgetting information, ensuring the core project memory remains clean and actionable without relying on raw conversation transcripts.

A dedicated canvas for reviewing plans and visual artifacts. It facilitates a tight review loop where human users can annotate specific elements, chat, and provide explicit verdicts (Approve/Request Changes) directly in a local browser environment. This tool is ideal for getting detailed, actionable feedback on designs, reports, or complex plans, moving beyond simple text confirmations.

This tool generates a detailed, local 'receipt' or impact report of all Claude Code activity recorded in local session transcripts. It cross-references this data with your local Git history to provide a quantifiable measure of effort and project contribution. Ideal for performance reviews, budget justifications, or self-auditing usage.

A comprehensive guide detailing the entire model lifecycle for RuView. It supports multiple advanced tracking methods including camera-free pose estimation (WiFlow), camera-supervised methods using MediaPipe, robust contrastive embeddings (RuVector), and domain generalization (MERIDIAN). Covers training, evaluation, and deployment across local GPU and cloud platforms.

This comprehensive command orchestrates full campaign localization across multiple target markets. It adapts all assets (emails, ads, social posts, landing pages, video scripts, etc.) beyond simple translation, adjusting cultural references, ensuring regional compliance, and localizing SEO for global market expansion.

This powerful tool monitors target keyword rankings across major search engines (e.g., Google) and tracks the presence and ownership of various SERP features (such as AI Overviews, Featured Snippets, and Local Packs). It establishes performance baselines and generates critical alerts when rankings drop significantly, or when the structure of search results changes, providing comprehensive visibility into organic search health.

This command sets the foundational regional parameters for a brand's global marketing efforts. Users configure critical settings such as timezone, primary language, local currency, compliance regulations (e.g., GDPR, CCPA), and preferred local platforms. These persistent settings ensure that all subsequent marketing campaigns, content generations, and performance reports automatically adhere to the specific regional requirements, eliminating manual adjustments for market differences.

Performs advanced semantic search across all stored brand knowledge, including guidelines, campaign learnings, and competitor insights. By combining vector databases, knowledge graphs, and local indices, it provides comprehensive, context-aware answers to complex natural language questions, ensuring users make decisions based on the brand's entire historical knowledge base.

This skill is deprecated and merged into /digital-marketing-pro:rank-monitor. It tracks comprehensive SERP features (e.g., AI Overviews, Featured Snippets, Local Pack) and ranking positions in a single unified run. Use the recommended command: /digital-marketing-pro:rank-monitor --features for continued functionality.