vvm
VVM (Vibe Virtual Machine) is a language for agentic programs where the LLM acts as the runtime. Orchestrate multi-agent workflows, manage state, and build resilient AI pipelines.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
466 skills found
VVM (Vibe Virtual Machine) is a language for agentic programs where the LLM acts as the runtime. Orchestrate multi-agent workflows, manage state, and build resilient AI pipelines.
An AI-powered TestOps platform and MCP server providing automated failure analysis, RCA matching, and intelligent test orchestration for CI/CD pipelines.
Skill for managing MCP-based research, documentation lookups, and coordination between external search tools and plugin-backed memory systems.
AI-powered tax advisor providing expert guidance on 2025 Japanese tax regulations, deductions, and financial planning for freelancers and employees.
AWS DynamoDB engineering assistant for schema design, query optimization, single-table patterns, and infrastructure management using Boto3 and AWS CLI.
Manage GitHub workflows, issues, and pull requests directly from your terminal using the gh CLI within OpenClaw.
Expert Rust development guide based on real-world code reviews. Ideal for idiomatic code, performance tuning, error handling, and avoiding common pitfalls in CLI and production tools.
Execute the implementation planning workflow, generate technical design artifacts, and structure research tasks for Spec Kit projects.
Produce clear, professional technical documentation, blog posts, and tutorials based on real engineering experience, prioritizing value and actionable insights.
Expert guidance for Neo4j Cypher queries and MCP server tools, focusing on schema introspection, graph operations, and efficient database development workflows.
Fetch and parse transcripts from YouTube and Bilibili videos for summarization, QA, and content extraction using yt-dlp.
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.