khoj
Repository: khoj
Author: khoj-ai · Source status: Clear source
Score 92Clear source
Your AI second brain.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: Claude-Code-Stock-Deep-Research-Agent
Author: liangdabiao·Source status: Clear source
本研究基于 Claude Code Deep Research 系统： 方法论: 8阶段股票投资尽调框架 智能体: 28个并行研究智能体 工具: WebSearch、WebFetch、综合分析 质量: 多空平衡、明确风险、数据验证。简单使用：/stock-research AAPL, I want a quick overview
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Pre-install score
92 · Manual review
One primary score for ranking and pre-install decisions; Evidence completeness 65%.
Risk decision
Review required
metadata-only
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.
Install guidance
This area handles the execution step: review explicit or inferred tool fit, then decide whether to copy commands.
Explicitly supported
Candidate support (inferred)
Candidate tools are inferred signals, not official compatibility certifications.
Universal
Shown with the common install pattern for this tool.
git clone https://github.com/liangdabiao/Claude-Code-Stock-Deep-Research-Agent.gitReview source and permissions first. If no explicit command is available, start from the repository guidance.
Current risk hints: metadata-only
Audit main stage
This stage turns 15 audit dimensions into one pre-install score; the constellation only explains the gaps instead of becoming a second score.
The purpose is visible, but source, permissions, or install evidence needs checking.
Current advice
Review before install
Review source, permissions, and execution details before copying commands.
Execution profile
High
Check command, file-write, and network behavior first.
Evidence completeness
65%
This shows how much public evidence supports the score; it is not a safety certification.
Start with the overall shape and the weakest three items, then use the list below for full labels, scores, and actions.
Start here
Read the weakest three first, then move into the full 15-item list.
Full 15-dimension list
The default view starts from the lowest scores; switch to risk-first when you want severity before score.
Repository
liangdabiao/Claude-Code-Stock-Deep-Research-Agent
Author
liangdabiao
Community signal
274 stars · 62 forks
Favorites
0
Last updated
2026-03-08
Primary source
liangdabiao/Claude-Code-Stock-Deep-Research-Agent
Source status
Clear source
Install method
script-backed
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Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
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Create a data analysis AI agent with Claude Code.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: langgraph_multi-agent-rag-customer-support
Author: liangdabiao · Source status: Clear source
本项目实现了一个基于多智能体（Multi-Agent）和检索增强生成（Retrieval-Augmented Generation, RAG）技术的客户支持系统。它利用 Python、LangChain 和 LangGraph 构建了一个能够处理各种旅行相关查询的对话式 AI，包括航班预订、租车、酒店预订和行程推荐。还有对接了woocommerce商城进行商品查询，文章查询，表单提交，订单查询等商城功能。
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