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Production Test Automation Skills Library

lambdatest-agent-skills

sickn33/agentic-awesome-skills

A comprehensive index of 46 production-grade test automation skills, covering E2E, unit, mobile, BDD, and cloud testing across 15+ languages. This skill equips AI coding assistants to act as senior QA architects, generating structured, real-world test code for major frameworks like Playwright, Selenium, Cypress, and Jest. It supports project setup, CI/CD integration, and cross-platform debugging.

YYLO Ledger Task Management

ledger-tasks-yylo

sickn33/agentic-awesome-skills

Manage tasks with the YYLO Ledger CLI: create, list, search, get, mark, update, archive and merge tasks with dependency tracking, ready ordering, cold archive packs and opt-in cross-project routing.

Linear Automation Toolkit

linear-automation

sickn33/agentic-awesome-skills

Automates Linear issues, projects, cycles, labels, comments, and GraphQL workflows through Rube MCP’s Composio toolkit so teams can manage states, tasks, and custom queries without juggling direct APIs.

Linear Issue Manager

linear-claude-skill

sickn33/agentic-awesome-skills

Manage Linear issues, projects, and teams efficiently. This skill provides tools and workflows for creating, updating, and tracking issues, managing project progress, and coordinating team efforts within Linear.

Lore Project Memory

sickn33/agentic-awesome-skills

Framework-agnostic long-term project memory for AI agents. Persists architecture decisions, conventions, and context as plain Markdown files in .lore/, enabling any agent to record, recall, audit, sync, and query project knowledge.

Lovable Project Cleanup Tool

lovable-cleanup

sickn33/agentic-awesome-skills

Removes Lovable.dev scaffolding from Vite + React projects including dependencies, branding, placeholder assets, and generated documentation. Helps developers ship clean, ownable codebases after exporting from Lovable. Audits package.json, vite.config.ts, index.html, and source files for Lovable fingerprints.

SwiftPM macOS App Packaging Without Xcode

macos-spm-app-packaging

sickn33/agentic-awesome-skills

A comprehensive toolset to scaffold, build, sign, and package SwiftPM-based macOS applications entirely without requiring an Xcode project. It guides users through the end-to-end process, including setting up the project, running the app, performing code signing, notarization, and generating update appcasts for robust, reproducible releases.

Makepad Basics Guide

sickn33/agentic-awesome-skills

Get started with the Rust makepad-widgets crate: project setup, Cargo.toml, live_design! DSL, app_main! entry point, widget access, event handling, and platform requirements for building cross-platform GPU-accelerated apps.

Market Sizing Analysis Methodologies

market-sizing-analysis

sickn33/agentic-awesome-skills

This skill provides comprehensive methodologies for calculating Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM). It guides users through top-down, bottom-up, and value theory approaches for startup strategy, fundraising, and business planning. Ideal for validating market opportunities and defining realistic growth projections.

Vue2 Markstream CLI Integration

markstream-vue2-cli

sickn33/agentic-awesome-skills

Integrate markstream-vue2 into Vue CLI or Webpack 4 projects with export-map-safe CSS, CDN worker fallbacks, and safe streaming markdown rendering defaults.

Markstream Vue 2 Vite Integration

markstream-vue2-vite

sickn33/agentic-awesome-skills

Integrate markstream-vue2 into a Vue 2 plus Vite project with bundled worker imports, CSS layer ordering, Composition API compatibility, and safe streaming markdown defaults for chat or history rendering.

Semantic Memory for AI Agents

sickn33/agentic-awesome-skills

A self-hosted, semantic memory service designed for AI agents. It allows agents to save worklogs, decisions, and notes, and recall this knowledge across sessions based on meaning rather than exact keywords. Using pgvector and structured workspaces, it maintains persistent, context-aware knowledge bases, making it ideal for collaborative AI projects.

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