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
neurodivergent-memory
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
Control gaps:missing license, broad permissions, shell without guardrails
haystack
Execution risk:High
Threat tags:unexpected code execution, data exfiltration, memory context poisoning
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 | neurodivergent-memory | haystack | scholar-rag |
|---|---|---|---|
| Source & provenance | |||
Provenance | jmeyer1980/neurodivergent-memory | deepset-ai/haystack | PangHu1020/scholar-rag |
Freshness | 2026-04-05 | 2026-04-08 | 2026-04-06 |
| Risk & trust | |||
Trust score | 81 | 81 | 82 |
Audit signals | runs shell | No explicit signals | No explicit signals |
| Install & compatibility | |||
Install friction | 50 | 40 | 40 |
| Community | |||
Stars | 6 | 24.8K | 22 |
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: serena
Author: oraios · Source status: Clear source
A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent Topics: agent, ai, ai-coding, claude, claude-code, codex, ide, jetbrains, language-server, mcp-server, pr
Score basis:Clear source · Low risk signals · Universal
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: MCPLuceneServer
Author: mirkosertic · Source status: Clear source
MCP Lucene Server is a Model Context Protocol (MCP) server that exposes Apache Lucene's full-text search capabilities through a conversational interface.
Score basis:Clear source · Low risk signals · Universal
Repository: rag_demos
Author: dappros · Source status: Clear source
Examples of RAG (Retrieval-Augmented Generation) with Ethora, LangChain, and OpenAI.
Score basis:Clear source · Low risk signals · Universal