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
awesome-LLM-resources
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, human approval gap
Control gaps:missing license, broad permissions, shell without guardrails
scholar-rag
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
| Dimension | serena | awesome-LLM-resources | scholar-rag |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, data exfiltration, memory context poisoning |
Evidence confidence | 67% | 65% | 67% |
| Source & provenance | |||
Provenance | oraios/serena | WangRongsheng/awesome-LLM-resources | PangHu1020/scholar-rag |
Freshness | 2026-04-08 | 2026-04-08 | 2026-04-06 |
| Risk & trust | |||
Trust score | 79 | 79 | 82 |
Audit signals | No explicit signals | network access | No explicit signals |
| Community | |||
Stars | 22.6K | 8K | 22 |
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: 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