Copilot Instructions for Bombing Run Project
**Project Name:** Bombing Run
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7,774 skills indexed with the new KISS metadata standard.
**Project Name:** Bombing Run
| Rule | Requirement |
This project is a notebook-centric workspace for generating and managing metadata using LLMs (OpenAI, Google Gemini) and MongoDB. All main logic is implemented in Jupyter notebooks, with functions for data loading, sampling, LLM prompt construction, LLM invocation, and MongoDB upload.
AItrucks is a delivery planning and fleet management application built with React, TypeScript, Vite, and Supabase. It helps manage deliveries, vehicles, trips, and route optimization.
This is a **YouTube Audio QA system** that provides an end-to-end local pipeline for downloading audio from YouTube, transcribing it with Whisper, indexing transcripts in a vector database, and enabling question-answering with citations and timestamps. The system is designed to be Windows-friendly a
This repository contains an interactive security essentials presentation for Full Sail University Hall of Fame 2026. The presentation is built using Reveal.js and covers essential security knowledge for technology professionals.
- **ENG CRM** – a customer-relationship management app with email/password auth.
These are the foundational rules for this workspace. They override general best practices.
`mq` is a jq-like command-line tool for Markdown processing. Written in Rust, it allows you to easily slice, filter, map, and transform Markdown files.
Create a professional release using GitHub CLI (gh). Generate SemVer version, clear release notes, and ready-to-run command.
Identify current session's task list and generate commands to start a new Claude session sharing the same tasks. Use when "handing off", "continuing later", "resuming tasks", or preparing work for next session.
Database, API, and integration conventions for Red Cliff Record. Use when working with Drizzle ORM, tRPC routers, database migrations, integration syncs, or media alt-text workflows in this project. Triggers on database queries, API routes, Drizzle v2, tRPC, Zod validation, integration work, or `rcr media` commands.
Process JSON, YAML, CSV, and XML data (jq, yq, awk).
Trade decision audit trail and statistics
Mass spectrometry analysis. Process mzML/MGF/MSP, spectral similarity (cosine, modified cosine), metadata harmonization, compound ID, for metabolomics and MS data processing.
Use when creating HTML email templates with React components - welcome emails, password resets, notifications, order confirmations, newsletters, or transactional emails.
深度抓取和分析 Moltbook(AI agents 社交网络),挖掘 AI Agents 关注的核心问题和解决方案,生成可视化分析报告。理解 AI 社区的集体智慧,发现可复用的问题解决模式。
Refactor HTML/TSX files to use existing UI components, DaisyUI classes, and semantic colors. Use when (1) refactoring React/TSX page components to use reusable UI components, (2) replacing raw HTML elements with component library equivalents, (3) converting primitive Tailwind colors to semantic DaisyUI colors, (4) extracting repeated styling patterns into components.
Refactor HTML/TSX files to use existing UI components, DaisyUI classes, and semantic colors. Use when (1) refactoring React/TSX page components to use reusable UI components, (2) replacing raw HTML elements with component library equivalents, (3) converting primitive Tailwind colors to semantic DaisyUI colors, (4) extracting repeated styling patterns into components.
AI-augmented software engineering workflow based on Addy Osmani's methodology. Implements structured planning, chunked implementation, extensive context provision, strategic model selection, human oversight, granular commits, and continuous learning. Treats LLMs as pair programmers requiring clear direction rather than autonomous agents.
Tech stack: Jekyll 3.9.1 + Bundler + jekyll-pandoc. Static site with Markdown, Liquid, and SCSS.
This document provides comprehensive guidelines for AI agents developing Python packages. Follow these standards to ensure high-quality, maintainable, and professional Python code.
generic skill
실시간 재난/안전 데이터 소스들을 주기적으로 수집하여, 공통 형태의 "이벤트"로 정형화해서 Postgres에 저장하고,