Agent Skills Hub

implementation

Structured, template-driven workflow for end-to-end feature development including coding, automated testing, verification, and session-based improvement.

plan

Structured 6-phase workflow for planning and implementing features, skills, and architectural changes with automated tool discovery and safety verification.

clean-code

Pragmatic AI-assisted coding standards focused on clean code, simplicity, and maintainability. Enforces best practices like SRP, DRY, and KISS to prevent over-engineering.

cc-skill-project-guidelines-example

A template skill for creating project-specific AI agent guidelines, defining architecture, file structures, and code patterns for deterministic development.

submodule-push

Pushes local changes in git submodules (hive or core-geth) to their respective remote forks, ensuring repository synchronization.

self-reviewer

Implements an autonomous, critical self-verification layer for AI agents to validate code quality, security, and requirement alignment before task completion.

tasks

Track and execute code implementation using Mighty (mt) tasks, with progress comments, linked evidence, recorded design decisions, and clean closeout workflows.

linear

Linear issue management and synchronization for LobeHub, featuring automated PR referencing, sub-issue tree decomposition, and status tracking.

testops-companion

An AI-powered TestOps platform and MCP server providing automated failure analysis, RCA matching, and intelligent test orchestration for CI/CD pipelines.

memov

AI-assisted version control for code agents. Track prompts, context, and diffs automatically with MemoV to ensure full traceability without polluting your git history.

ride

Analyze codebases to generate evidence-grounded Loa artifacts using Enterprise-Grade Managed Scaffolding for structured reality mapping.

context-detection

Systematic project technology stack detection, framework-specific skill auto-loading, and multi-stack analysis for fullstack projects like React + Go.