Compare skills
Pick 2–4 skills and compare what really matters: fit, risk, install effort, and community signal.
Selected skills (2/4)
Comparison matrix
Highlights show current best; tooltip explains diff/best rules.
SAS-v2.1 diff rules / risk tag notes
Start with the matrix. Open this section when you need to understand audit grades, top threats, control gaps, and best-value highlights.
Suggested baseline
Search to add skills, or paste 2–4 comma-separated slugs.
How differences are detected
A row is marked different when selected skills have distinct values. Only-differences mode hides rows that are identical.
How best values are highlighted
Audit score, evidence confidence, trust score, and community signal prefer higher values; execution risk and install friction prefer lower values.
How to read risk tags
Risk tags come from SAS-v2.1 public-evidence signals and point to command, network, secret, context, or supply-chain items to review before install.
Selected audit signals
scholar-rag
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
serena
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | scholar-rag | serena |
|---|---|---|
| SAS-v2.1 pre-install audit | ||
Audit grade | C · Review first | C · Review first |
Execution risk | High | High |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, memory context poisoning |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Command |
Evidence confidence | 67% | 67% |
| Source & provenance | ||
Provenance | PangHu1020/scholar-rag | oraios/serena |
Category | Knowledge & RAG | Knowledge & RAG |
Freshness | 2026-04-06 | 2026-04-08 |
| Risk & trust | ||
Trust score | 82 | 79 |
Audit signals | No explicit signals | No explicit signals |
Permission hints | ||
| Install & compatibility | ||
Supported tools | Universal | Universal |
Install method | script-backed | script-backed |
Install friction | ||
| Community | ||
Stars | 22 | 22.6K |
Repository: OpenViking
Author: volcengine · Source status: Clear source
OpenViking is an open-source context database designed specifically for AI Agents(such as openclaw).
Score basis：Clear source · Risk needs review · Universal
Repository: generative-ai-for-beginners-project-based-guide-to-building-rag-agents
Author: hereandnowai · Source status: Clear source
A beginner-friendly, project-driven tutorial on generative AI and LangChain agents.
Score basis：Clear source · Low risk signals · Universal
Repository: Buddy
Author: is-leeroy-jenkins · Source status: Clear source
An AI for federal financial management designed to support Financial Analysts, Managers, and Policy Professionals.
Score basis：Clear source · High risk signals · Universal
Repository: AI-QA-Retrieval-Assistant-API
Author: mvsaran · Source status: Clear source
A complete, end-to-end **Retrieval-Augmented Generation (RAG)** API server built with **Python**, **FastAPI**, **LangChain**, and **ChromaDB**.
Score basis：Clear source · Low risk signals · Universal
Repository: rag_demos
Author: dappros · Source status: Clear source
Examples of RAG (Retrieval-Augmented Generation) with Ethora, LangChain, and OpenAI.
Score basis：Clear source · Low risk signals · Universal
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
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