Alphaear Search

A unified search tool that handles both web finance queries and local document retrieval through a single interface. You get three web engines (Jina, DuckDuckGo, Baidu) plus a local RAG system that searches a daily_news database. The smart caching is the interesting bit: it includes an agentic relevance prompt that lets Claude check the cache and decide if previous results are still useful before firing off new searches. Use aggregate_search when you want results from multiple engines at once. Honestly most useful if you're building a finance research assistant that needs to pull from both fresh web data and your own document store without managing two separate search systems.

skills.sh installs: 545
GitHub stars: 3.1K
last updated: Aug 21, 2026

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Install

$ npx -y skills add rkiding/awesome-finance-skills --skill alphaear-search --agent claude-code

Installs into .claude/skills of the current project.

Files

AlphaEar Search Skill

Overview

Unified search capabilities: web search (Jina/DDG/Baidu) and local RAG search.

Capabilities

1. Web Search

Use scripts/search_tools.py via SearchTools.

Search: search(query, engine, max_results)
- Engines: jina, ddg, baidu, local.
- Returns: JSON string (summary) or List[Dict] (via search_list).
Smart Cache (Agentic): If you want to avoid redundant searches, use the Search Cache Relevance Prompt in references/PROMPTS.md. Read the cache first and decide if it's usable.
Aggregate: aggregate_search(query)
- Combines results from multiple engines.