jira-cli
Interactive terminal interface for Atlassian Jira that streamlines issue management, sprint tracking, and ticket workflows directly from the command line.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
512 skills found
Interactive terminal interface for Atlassian Jira that streamlines issue management, sprint tracking, and ticket workflows directly from the command line.
A testing utility for the npm-agentskills framework, designed to validate Nuxt module integration and skill discovery patterns.
Automated quality gate using 5 parallel AI agents to review code changes for correctness, style, and consistency.
Development guide for lemline-core, the stateless Serverless Workflow engine. Manage workflow execution, node navigation, state transitions, JQ expression evaluation, error handling, and parallel fork logic.
Configure and manage Snowflake connections for CLI, Streamlit, and Snowpark environments, including authentication methods like SSO, key pair, OAuth, and profile management.
A configuration and usage guide for the XRequest tool within the Ant Design X SDK, streamlining network integration for streaming AI interfaces.
A terminal-based Chrome DevTools Protocol client designed for AI agents. Provides direct, session-persistent control over browser navigation, DOM manipulation, scraping, and network inspection.
Full-stack SDLC agent workflow managing the entire production lifecycle from intake and planning to automated testing, CI/CD, and infrastructure deployment using MCP tools.
A constitution-driven, spec-first development workflow for Claude Code and Codex, automating feature planning, implementation, and quality assurance through structured agentic loops.
A command-line tool to list, configure, authenticate, call, and inspect Model Context Protocol (MCP) servers via HTTP or stdio.
Advanced workflow orchestration for AI agents, featuring multi-model routing, Codex sandbox iteration, parallel swarm execution, and persistent memory across complex pipelines.
Analyze local system hardware (RAM, CPU, GPU/VRAM) to receive expert recommendations for optimized local LLM models, quantization settings, and performance estimates.