Skill UI

Query Microsoft Fabric and Power BI semantic models with DAX to build trustworthy dashboards: reuse governed measures, design KPIs with target and trend, wire crossfiltering, and test queries with an execute-inspect-fix loop.

This skill ensures AI models generate complete, unabridged code and files without placeholders, TODOs, or skipped sections. It enforces strict completeness for deliverables like full implementations or exhaustive lists. It prevents shortcuts like '...' or descriptions instead of code, ensuring every requested item is fully realized within token limits.

Install, check, restyle, and remove the agy-powerline statusline for Antigravity CLI. Shows model, quotas, reset times, context window, git status, and tokens.

This skill detects and recovers text accidentally typed using the Taiwanese Bopomofo keyboard while the input method was stuck in English mode. It maps garbled keystrokes like su3cl3a8 back to Traditional Chinese characters using a decoder script and context analysis. It is designed for Taiwanese developers who forget to switch input methods when prompting AI, ensuring requests are understood without retyping. It excludes technical strings like code or URLs.

Statistical gate for agent, prompt, model and tool changes: repeated trials, per-case paired comparison against a baseline, trajectory grading, flake detection and blocking exit codes before shipping.

This tool provides a unified cost view for AI agents like Claude Code, Codex, and Grok. It reads local logs to calculate spend, token usage, and cache hit ratios per provider and model. It flags unpriced models and supports filtering by time range, project, and provider. Output can be JSON or Markdown. All data stays local, ensuring privacy. Ideal for auditing AI usage costs and comparing provider pricing.

Calculate the cost per million tokens for running AI models locally on your own hardware like Ollama or llama.cpp. It factors in electricity prices, hardware costs, power consumption, and generation speed to determine the break-even point against hosted API models. Useful for deciding whether to deploy models locally or use cloud services based on actual usage and financial constraints.

Actively build and sharpen a project's domain model by challenging terminology, recording glossary terms, and writing ADRs when decisions are hard to reverse and surprising.

This skill covers building, extending, and reviewing Model Context Protocol (MCP) servers using the Python FastMCP library. It includes guidance on managing API versions, defining tools, resources, and prompts, handling errors, and configuring transports. It also details how to evaluate agent workflows and test server capabilities against client requirements.

This skill acts as an interactive brainstorming engine for the Elephant & Goldfish Model (EGM). It transforms raw concepts into structured hypotheses through clarifying questions and automated multi-lens reviews. The process iteratively refines ideas into RFC summaries ready for human review or detailed design. It emphasizes stress-testing, avoiding bias, and delivering artifacts first to ensure robust product development.

This skill runs drift detection on design documents to verify consistency with actual code. It identifies missing features, incomplete models, and architectural divergences. It generates a detailed report listing discrepancies between specifications and implementation. Use this when checking if code matches design documents or READMEs in a directory.