Compare skills
Pick 2–4 skills and compare what really matters: fit, risk, install effort, and community signal.
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
Repository: spring-ai-playground
Author: JM-Lab · Source status: Clear source
A self-hosted web UI that simplifies AI experimentation and testing for Java developers.
Score basis：Clear source · High risk signals · Universal
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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
NornicDB
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
UnifAI
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
AI-QA-Retrieval-Assistant-API
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | NornicDB | UnifAI | AI-QA-Retrieval-Assistant-API |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Audit grade | C · Review first | C · Review first | C · Review first |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | 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 | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Permission review, Network, Command |
Evidence confidence | 67% | 69% | 67% |
| Source & provenance | |||
Provenance | orneryd/NornicDB | redhat-community-ai-tools/UnifAI | mvsaran/AI-QA-Retrieval-Assistant-API |
Category | Knowledge & RAG | Knowledge & RAG | Knowledge & RAG |
Freshness | |||
| Risk & trust | |||
Trust score | 81 | 81 | 82 |
Audit signals | writes files | runs shell | No explicit signals |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 396 | 35 | 1 |
Repository: openclaw/skills
Author: lookupmark · Source status: Clear source
Semantic search over local files using all-MiniLM-L6-v2 embeddings and ms-marco-MiniLM-L-6-v2 cross-encoder reranking with ChromaDB and parent-child chunking.
Score basis：Clear source · High risk signals · OpenClaw
Repository: langchain4j
Author: langchain4j · Source status: Clear source
LangChain4j is an open-source Java library that simplifies the integration of LLMs into Java applications through a unified API, providing access to popular LLMs and vector databases.
Score basis：Clear source · Low risk signals · Universal
Repository: openclaw/skills
Author: cinience · Source status: Clear source
Use when working with OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches.
Score basis：Clear source · High risk signals · Claude
Repository: openclaw/skills
Author: jared-goering · Source status: Clear source
Structured AI agent memory with temporal versioning, relational tracking, and semantic search.
Score basis：Clear source · High risk signals · Universal
Repository: openclaw/skills
Author: cinience · Source status: Clear source
Use when reranking search candidates is needed with Alibaba Cloud Model Studio rerank models, including hybrid retrieval, top-k refinement, and multilingual relevance sorting.
Score basis：Clear source · High risk signals · Universal
2026-04-07 |
2026-04-06 |
2026-04-01 |
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
40 |
50 |
40 |
