Openviking
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 · Risk needs review · Universal
Repository: lark-aws-knowledge-assistant
Author: zhang1980s·Source status: Clear source
The Lark AWS Knowledge Assistant is an advanced integration tool that enables users to interact with AWS Support Center and Amazon Q (the official AI-powered assistant for AWS knowledge) through a familiar Lark (Feishu)
Score basis:Clear source · Low risk signals · Universal
Trust level
70 · Review first
Usable, but inspect source, install method, and risk hints before adoption.
Risk decision
No explicit risk signals
No explicit risk signal is available.
Install readiness
script-backed · copy-only command
SkillTrust only shows install guidance and copy actions; it never executes installs.
Before you install
Review source, permissions, and execution risk first, then alternatives. Scores prioritize review; they do not replace manual judgment.
Review weakest dimensions and next actions before copying commands.
Evidence or risk signals are incomplete; compare alternatives first.
Audit grade
C · Review first
Execution risk
High
Evidence confidence
67%
SAS-v2.1 radar
SAS-v2.1
Audit grade
C · Review first
Execution risk
High
Top threats
unexpected code execution, data exfiltration
Control gaps
missing license, broad permissions
Evidence confidence
67%
Repository
zhang1980s/lark-aws-knowledge-assistant
Author
zhang1980s
Community signal
1 stars · 0 forks
Last updated
2025-10-07
Primary source
zhang1980s/lark-aws-knowledge-assistant
Source status
Clear source
Install method
script-backed
Command & code execution
34Focus: Whether it runs commands or scripts
Next action: Manually confirm command-running skills in an isolated directory.
High-risk action confirmation
38Focus: Whether destructive or external actions require confirmation
Next action: Avoid directly installing high-risk skills without confirmation controls.
Network & data egress
43Focus: Whether it may send data out
Next action: If unsure, restrict network access or allow only known domains.
Supported tools can change install steps; Universal entries need source review.
Explicitly supported
git clone https://github.com/zhang1980s/lark-aws-knowledge-assistant.gitNo explicit risk signals recorded
Review source and permissions before copying install commands.
Evidence or risk signals are incomplete; compare alternatives first.
Focus: Who published it and whether it is traceable
Next action: Review repository, author, and README first; do not install directly when source is pending.
Focus: Whether install steps can be reviewed
Next action: Prefer candidates with install docs and repository evidence.
Focus: Whether tool descriptions may hide instructions
Next action: Read README, rules, and tool descriptions before install.
Focus: What it can access
Next action: Grant only task-required permissions and prefer Ask/manual confirmation.
Focus: Whether it runs commands or scripts
Next action: Manually confirm command-running skills in an isolated directory.
Focus: Whether file reads/writes can escape scope
Next action: Check working directory and file access scope before running.
Focus: Whether it may send data out
Next action: If unsure, restrict network access or allow only known domains.
Focus: Whether it handles tokens, private keys, or agent identity
Next action: Do not provide long-lived tokens or private keys to source-pending skills.
Focus: Whether external content can steer behavior
Next action: For browser/RAG/rules skills, review permissions and confirmation controls first.
Focus: Whether memory or retrieved context can be poisoned
Next action: Try RAG/memory skills in a low-privilege environment first.
Focus: Whether external tools and MCP access are clearly bounded
Next action: Confirm which external tools it will connect to before install, and start with the smallest possible set.
Focus: Whether destructive or external actions require confirmation
Next action: Avoid directly installing high-risk skills without confirmation controls.
Focus: How far impact can spread when something goes wrong
Next action: If unsure, test in an isolated project first.
Focus: Whether actions can be traced
Next action: Prefer candidates with logs or previews.
Focus: Whether it is maintained and reusable
Next action: Check license and maintenance before organizational use.
Usable, but inspect source, install method, and risk hints before adoption.
Phase 1 only shows installation-aware, source-backed signals. SkillTrust does not execute install scripts for users.
Risk factors
No explicit risk signals.
Permission hints
repository clone, local runtime dependencies
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
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
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
Repository: neuron-ai
Author: neuron-core · Source status: Clear source
The PHP Agentic Framework to build production-ready AI driven applications.
Score basis:Clear source · Low risk signals · Universal
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
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
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
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
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
Repository: AI-QA-Retrieval-Assistant-API
Author: mvsaran · Source status: Clear source
A complete, end-to-end **Retrieval-Augmented Generation (RAG)** API server built with **Python**, **FastAPI**, **LangChain**, and **ChromaDB**.
Score basis:Clear source · Low risk signals · Universal
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method