UnifAI
Multi-agent workflow orchestration over enterprise knowledge sources
All Skills in This Repository (6)
Pure Emerald Level Indicatorspipeline-design-reviewer
Review AI pipeline designs for architectural and integration failures.
pipeline-debugger
Diagnose multi-agent pipeline errors and propose minimal fixes.
pipeline-designer
Generate ADR-formatted technical designs for UnifAI pipeline features from Jira tickets.
pipeline-qa
Write and run pytest tests for pipeline code changes with a QA report.
pipeline-code-reviewer
Review multi-agent pipeline code for hexagonal architecture and production quality.
pipeline-coder
Implement approved multi-agent workflow designs as hexagonal architecture code.
Frequently Asked Questions
FAQPage SchemaHow to install UnifAI?โผ
Run `npx skills add redhat-community-ai-tools/UnifAI --all -g -y` in your terminal to install all skills in this suite globally.
What does UnifAI do?โผ
UnifAI connects internal data sources like Slack, Jira, and documents into a unified vector store, then lets you query them through composable multi-agent workflows defined as YAML blueprints or built visually.
How do the UnifAI codebase skills help developers?โผ
Each domain skill auto-loads when you edit files in its scope, giving your coding assistant architecture rules, component routing tables, endpoint maps, and recipes for tasks like adding new nodes, tools, or providers.
Can UnifAI run workflows locally and in production?โผ
Yes. The same blueprint format runs locally on LangGraph for development or distributed on Temporal for production scaling, switched with a single environment variable.
Does UnifAI support external tool integrations?โผ
Yes. Agents can connect to MCP servers and A2A-compatible remote agents, giving them access to external tools like Jira, GitHub, and databases from any blueprint node.
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