Contextplus
Repository: contextplus
Author: ForLoopCodes · Source status: Clear source
Semantic Intelligence for Large-Scale Engineering.
Score basis:Clear source · Low risk signals · Universal
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
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
serena
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
rag_demos
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
OMNI — All-In-One Master Skill
Execution risk:High
Threat tags:unexpected code execution, identity privilege abuse, data exfiltration
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | serena | rag_demos | OMNI — All-In-One Master Skill |
|---|---|---|---|
| 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, identity privilege abuse, data exfiltration |
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, Secrets, Command |
Evidence confidence | 67% | 67% | 67% |
| Source & provenance | |||
Provenance | oraios/serena | dappros/rag_demos | openclaw/skills |
Category | Knowledge & RAG | Knowledge & RAG | Data & Analytics |
Freshness | |||
| Risk & trust | |||
Trust score | 79 | 82 | 88 |
Audit signals | No explicit signals | No explicit signals | needs credentials, network access, runs shell, writes files |
| Install & compatibility | |||
Supported tools | Universal | Universal | Claude, Codex, OpenClaw |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 22.6K | 1 | 0 |
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: 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: scholar-rag
Author: PangHu1020 · Source status: Clear source
A beginner-friendly and extensible Agentic RAG project that demonstrates the full pipeline of document parsing, retrieval, reranking, workflow orchestration, tool calling, and answer generation, designed for both learnin
Score basis:Clear source · Low risk signals · Universal
Repository: awesome-LLM-resources
Author: WangRongsheng · Source status: Clear source
🧑🚀 全世界最好的LLM资料总结(多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型) | Summary of the world's best LLM resources.
Score basis:Clear source · High risk signals · Universal
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 · Low risk signals · Universal
2026-04-01 |
2026-04-02 |
Permission hints |
|---|
repository clone, local runtime dependencies |
repository clone, local runtime dependencies |
verify source provenance before install |
40 |
40 |
65 |