Github Pathwaycom Llm App
Repository: llm-app
Author: pathwaycom · Source status: Clear source
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
Score basis:Clear source · Risk needs review · Universal
Repository: claude-data-analysis-ultra-main
Author: liangdabiao·Source status: Clear source
让小白都可以一键进行数据分析,搞互联网的,搞电商的,搞各种各样的,那么其实就会用到 互联网的数据分析, 例如互联网会关心 拉新,留存,促活,推荐,转化,A/B test, 用户分析 等等很多有用的数据分析。
Score basis:Clear source · Risk needs review · Universal
Trust level
87 · High trust
Strong recovered source and maintenance signals.
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.
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
65%
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
65%
Repository
liangdabiao/claude-data-analysis-ultra-main
Author
liangdabiao
Community signal
183 stars · 29 forks
Last updated
2025-12-25
Primary source
liangdabiao/claude-data-analysis-ultra-main
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
Candidate support (inferred)
Candidate tools are inferred signals, not official compatibility certifications.
git clone https://github.com/liangdabiao/claude-data-analysis-ultra-main.gitmetadata-only
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.
Strong recovered source and maintenance signals.
Phase 1 only shows installation-aware, source-backed signals. SkillTrust does not execute install scripts for users.
Risk factors
metadata-only
Permission hints
repository clone
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Repository: caveman
Author: JuliusBrussee · Source status: Clear source
🪨 why use many token when few token do trick — Claude Code skill that cuts 65% of tokens by talking like caveman
Score basis:Clear source · Risk needs review · Universal
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Repository: claude-cookbooks
Author: anthropics · Source status: Clear source
A collection of notebooks/recipes showcasing some fun and effective ways of using Claude.
Score basis:Clear source · Risk needs review · Universal
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Repository: GPT_API_free
Author: chatanywhere · Source status: Clear source
Free ChatGPT&DeepSeek API Key,免费ChatGPT&DeepSeek API。免费接入DeepSeek API和GPT4 API,支持 gpt | deepseek | claude | gemini | grok 等排名靠前的常用大模型。
Score basis:Clear source · Risk needs review · Universal
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Repository: career-ops
Author: santifer · Source status: Clear source
AI-powered job search system built on Claude Code.
Score basis:Clear source · Risk needs review · Universal
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Repository: claude-code-router
Author: musistudio · Source status: Clear source
Use Claude Code as the foundation for coding infrastructure, allowing you to decide how to interact with the model while enjoying updates from Anthropic.
Score basis:Clear source · Risk needs review · Universal
Why related: Same task category, Keyword overlap...
Why related: Same task category, Keyword overlap, Similar install method
Repository: claude-data-analysis
Author: liangdabiao · Source status: Clear source
Create a data analysis AI agent with Claude Code.
Score basis:Clear source · Risk needs review · Universal
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 · Risk needs review · Universal
Repository: XHS_Business_Idea_Validator
Author: liangdabiao · Source status: Clear source
小红书收集和分析数据来解析市场需求用户痛点及竞争格局 - 📊 **小红书数据抓取**: 自动抓取相关笔记和评论数据 - 🤖 **AI 内容分析**: 使用 LLM 分析用户痛点和市场需求 - 📄 **自动化报告生成**: 生成专业的市场验证报告
Score basis:Clear source · Risk needs review · Universal
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。
Score basis:Clear source · Risk needs review · Universal