Skill comparison
Compare agent skills before installing.
Comparing 4 skills
Use this as a shortlist, then open the skill detail page before adopting.
Decision summary
Llm App is the strongest overall pick here because it has a 100/100 readiness score and fits RAG and knowledge.
Strongest overall
Llm App
Use this as a leading candidate, then validate the README and install path in your own agent stack.
Fastest prototype
Llm App
Best first install candidate based on install readiness and adoption.
Freshest repo
Sim
Most recent maintenance signal among this shortlist.
| Signal | RAGHub A community-driven collection of RAG (Retrieval-Augmented Generation) frameworks, projects, and resources. Contribute and explore the evolving RAG ecosystem. | Sim Build, deploy, and orchestrate AI agents. Sim is the central intelligence layer for your AI workforce. | Llm App Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data. 🐳Docker-friendly.⚡Always in sync with Sharepoint, Google Drive, S3, Kafka, PostgreSQL, real-time data APIs, and more. | Haystack Open-source AI orchestration framework for building context-engineered, production-ready LLM applications. Design modular pipelines and agent workflows with explicit control over retrieval, routing, memory, and generation. Built for scalable agents, RAG, multimodal applications, semantic search, and conversational systems. |
|---|---|---|---|---|
| Quality | 100/100 Excellent | 100/100 Excellent | 100/100 Excellent | 100/100 Excellent |
| Decision verdict | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. | 100/100 Production-ready Use this as a leading candidate, then validate the README and install path in your own agent stack. |
| Adoption | 1.9K stars 0 installs | 29K stars 0 installs | 59K stars 0 installs | 26K stars 0 installs |
| Freshness | Jun 20, 2026 | Jul 18, 2026 | Jun 10, 2026 | Jun 12, 2026 |
| Use-case fit | ||||
| Workflow fit | ||||
| Platform hints | RAG, Claude Code | TypeScript, RAG, Claude Code | Jupyter Notebook, RAG, Claude Code | MDX, RAG, Claude Code, OpenAI Agents |
| Warnings | No major risk signals from current metadata | No major risk signals from current metadata | No OpenAgentSkill engagement data yet | No OpenAgentSkill engagement data yet |
| Best for | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals | RAG and knowledge workflows · Claude Code teams · teams that value GitHub adoption signals |
| Not ideal for | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review | teams that need a vendor-supported SLA · high-compliance environments without internal security review |
| OpenAgentSkill engagement | 2 views 0 install copies | 6 views 0 install copies | 0 views 0 install copies | 0 views 0 install copies |
| Install | $ npx skills add Andrew-Jang/RAGHub | $ npx skills add simstudioai/sim | $ npx skills add pathwaycom/llm-app | $ npx skills add deepset-ai/haystack |