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: 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%
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
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
openpencil
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
qdrant-loader
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
create-an-asset
Execution risk：High
Threat tags：data exfiltration, memory context poisoning, human approval gap
Control gaps：missing license, broad permissions, network without allowlist
| Dimension | openpencil | qdrant-loader | create-an-asset |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 79 · Manual review | 81 · Manual review | 80 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude |
Execution risk | High | High | High |
Threat tags | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, data exfiltration, memory context poisoning | data exfiltration, memory context poisoning, human approval gap |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, broad permissions, network without allowlist |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Network, Command |
Evidence completeness | 68% | 67% | 67% |
| Source & provenance | |||
Provenance | ZSeven-W/openpencil | martin-papy/qdrant-loader | anthropics/knowledge-work-plugins/tree/main/sales/skills/create-an-asset |
Category | Knowledge & RAG | Knowledge & RAG | Knowledge & RAG |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | writes files | No explicit signals |
Permission hints | repository clone, local runtime dependencies | repository clone, local runtime dependencies | registry access, remote metadata pull, runtime dependencies may be required |
| Install & compatibility | |||
Supported tools | Universal | Universal | Claude, Codex, Cursor, Universal |
Install method | script-backed | script-backed | registry-install |
Install friction | |||
| Community | |||
Stars | 2K | 37 | 6.6K |
Repository: claude-code-templates
Author: davila7 · Source status: Clear source
Memory is the cornerstone of intelligent agents.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 65%
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 · High execution risk · Universal · Evidence completeness 69%
Repository: prompt-in-context-learning
Author: EgoAlpha · Source status: Clear source
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
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%
Repository: haystack
Author: deepset-ai · Source status: Clear source
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-05 |
2026-04-06 |
2026-02-04 |
40 |
40 |
50 |
