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.
Score-basis 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 execution risk · Universal · Evidence completeness 67%
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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
Pre-install score, evidence completeness, 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
ByteRover
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing repo, missing license, broad permissions
Ultramemory
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | NornicDB | ByteRover | Ultramemory |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 81 · Manual review | 79 · Evidence missing | 85 · Evidence missing |
Score basis | Clear source, High execution risk, Universal | Source needs review, High execution risk, Claude | Clear source, High execution risk, Universal |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, identity privilege abuse, data exfiltration | unexpected code execution, identity privilege abuse, data exfiltration |
Control gaps | missing license, broad permissions, shell without guardrails | missing repo, missing license, broad permissions | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Secrets, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 67% | 69% | 69% |
| Source & provenance | |||
Provenance | orneryd/NornicDB | Source needs review | openclaw/skills |
Freshness | 2026-04-07 | 2026-04-08 | 2026-03-30 |
| Risk & permission signals | |||
Audit signals | writes files | needs credentials, network access, runs shell, writes files | needs credentials, network access, runs shell, writes files |
Permission hints | repository clone, local runtime dependencies | review skill metadata before install, check required credentials in skill docs | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Codex, OpenClaw, Claude, Cursor, Universal | Universal |
Install method | script-backed | instruction-only | script-backed |
Install friction | |||
| Community | |||
Stars | 396 | 108 | 0 |
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 execution risk · OpenClaw · Evidence completeness 69%
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 · High execution risk · Universal · Evidence completeness 67%
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 execution risk · Universal · Evidence completeness 65%
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 execution risk · Claude · Evidence completeness 69%
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100 |
65 |
