Design, develop, and package AI agent skills with structured workflows.
npx skills add https://github.com/j-show/ai-everything --skill skill-forge-j-show
Please help me install this Agent Skill. Skill: skill-forge Source: https://github.com/j-show/ai-everything/tree/main/skills/skill-forge Command: npx skills add https://github.com/j-show/ai-everything --skill skill-forge-j-show
Skill Forge solves the issue of low-quality, unmanageable AI skills by providing a structured framework for architecture, workflow design, and prompt engineering.
Use the skill-forge skill to initialize a new project named my-custom-skill in the current directory.
High-intent search queries and answers about installing and using this skill.
To structure prompt engineering workflows, you need a framework that enforces Iron Laws and uses progressive loading to manage context windows. This approach packages logic into modular scripts and references, ensuring reliable, multi-step agent execution.
Packaging production-grade AI skills requires deterministic script encapsulation and structured pre-delivery checklists. Organizing complex logic into modular, maintainable units minimizes context bloat and validates workflow architecture before deployment.
Preventing context window bloat involves resource management through progressive loading and external references. By modularizing complex procedural knowledge into deterministic scripts, you keep the active context minimal while maintaining full functionality.
Confirmation gates are needed in workflow design when executing multi-step procedural knowledge that requires strict validation. They enforce reliable agent performance by ensuring each critical phase is verified before proceeding to the next.
Claude Code supports modular skill development by integrating external scripts and references. This architecture allows complex logic to be encapsulated deterministically, enabling specialized agents to execute workflows without exceeding context limits.
Inconsistent agent behavior often stems from unstructured prompt engineering and context bloat. Implementing Iron Laws, anti-pattern documentation, and rigorous workflow validation enforces deterministic execution and eliminates unpredictable outcomes.
HechaoYannetGenerate a master architecture document from GDDs, ADRs, and governance constraints.
hydrosolutionsValidate tests, Docker builds, and GitHub Actions before production deployment.
siviter-xyzOptimize AI agent context architectures with progressive disclosure and memory strategies.
AgLyx3Design prompt patterns and evaluation frameworks for LLM-driven workflows.
