Agentic Uber Skills: A Platform-Agnostic Workflow Protocol for Agentic Coding

This post introduces the agentic-uber-skills project, which aims to address the pain point of significant differences in skill definition and invocation methods across various AI programming assistant platforms. It provides a platform-agnostic SKILL.md skill package protocol, including seven core skills covering the entire lifecycle of agentic coding workflows, supporting cross-runtime migration, learning mechanisms, and privacy protection, and promoting the standardization of agentic workflows.

With the improvement of LLM capabilities, AI programming assistants (such as Claude, Codex, OpenCode, etc.) have evolved from code completion tools to intelligent agents for complex tasks. However, there are huge differences in skill definition and invocation methods between different platforms, forcing developers to re-learn entirely new working modes when switching tools. This pain point gave birth to the agentic-uber-skills project.

SKILL.md is a portable Markdown protocol that includes workflows, constraints, templates, validation scripts, and reference materials, using a progressive disclosure design (loading metadata first, then reading the main content and resources on demand). The project includes seven core skills: Ubergoal (lifecycle orchestration), Uberplan (lean planning), Uberaccept (adversarial acceptance), Uberskillevolver (skill evolution), Ubersimplify (complexity audit), Uberassess (source evaluation), and Deep-RCA (root cause analysis), covering the entire lifecycle of coding workflows.

The core advantage of the project is platform agnosticism—skills are not tied to specific runtimes (e.g., Claude/Codex), only including optional adapter instructions. Installation methods support symbolic links (taking effect immediately after pulling updates) or directory copying (requiring re-copying for updates), and can be adapted to any agent runtime that supports local skills or loading SKILL.md.

The project designs a cross-machine learning mechanism where original learning records are kept private locally. Only after verification and privacy review by Uberskillevolver, the cleaned learning data packages are submitted to a shared inbox, enabling experience sharing while protecting sensitive information.

Developers can call corresponding skills to guide AI in completing complex tasks such as refactoring, new feature design, and code review; teams can use the skill package as an internal coding standard to ensure members follow consistent workflows and quality standards when using AI assistants.

agentic-uber-skills represents an important step toward standardization in the field of AI-assisted programming. As agent capabilities grow, clear, portable, and evolvable workflow protocols will become increasingly important. The project not only provides specific skill implementations but also demonstrates the framework and methodology for agentic workflow design.