alphaear-search
Perform finance web searches and local context searches. Use when the user needs general finance info from the web (Jina/DDG/Baidu) or needs to retrieve finance information from a local document store (RAG).
Best use case
alphaear-search is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Perform finance web searches and local context searches. Use when the user needs general finance info from the web (Jina/DDG/Baidu) or needs to retrieve finance information from a local document store (RAG).
Teams using alphaear-search should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/alphaear-search/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How alphaear-search Compares
| Feature / Agent | alphaear-search | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Perform finance web searches and local context searches. Use when the user needs general finance info from the web (Jina/DDG/Baidu) or needs to retrieve finance information from a local document store (RAG).
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.
### 2. Local RAG
Use `scripts/hybrid_search.py` or `SearchTools` with `engine='local'`.
- **Search**: Searches local `daily_news` database.
## Dependencies
- `duckduckgo-search`, `requests`
- `scripts/database_manager.py` (search cache & local news)Related Skills
gpt-researcher
Run GPT-Researcher multi-agent deep research framework locally using OpenAI GPT-5.2. Replaces ChatGPT Deep Research with local control. Researches 100+ sources in parallel, provides comprehensive citations. Use for Phase 3 industry/technical research or comprehensive synthesis. Takes 6-20 min depending on report type. Supports multiple LLM providers.
deep-research
Web research with Graph-of-Thoughts for fast-changing topics. Use when user requests research, analysis, investigation, or comparison requiring current information. Features hypothesis testing, source triangulation, claim verification, Red Team, self-critique, and gap analysis. Supports Quick/Standard/Deep/Exhaustive tiers. Creative Mode for cross-industry innovation.
brutal-deepresearch
Structured deep research pipeline with confirmation gates and resume support. Generates outline, launches parallel research agents, produces validated JSON results and markdown report.
agent-market-researcher
Expert market researcher specializing in market analysis, consumer insights, and competitive intelligence. Masters market sizing, segmentation, and trend analysis with focus on identifying opportunities and informing strategic business decisions.
agent-data-researcher
Expert data researcher specializing in discovering, collecting, and analyzing diverse data sources. Masters data mining, statistical analysis, and pattern recognition with focus on extracting meaningful insights from complex datasets to support evidence-based decisions.
agency-researcher
Find and qualify real estate agencies in a given suburb
add-search-engine
Integrate a new LLM search provider into Mentha
academic-search
Search academic paper repositories (arXiv, Semantic Scholar) for scholarly articles in physics, mathematics, computer science, quantitative biology, AI/ML, and related fields
academic-benchmark-researcher
When the user requests information about academic benchmarks, datasets, or research papers, particularly in machine learning, deep learning, or logical reasoning domains. This skill enables systematic research of academic benchmarks by searching web sources, downloading and analyzing arXiv papers, extracting key metadata (number of tasks, training availability, difficulty levels), and compiling comparative summaries. It triggers on requests involving dataset comparisons, benchmark analysis, or academic paper research for table creation.
content-research-writer
Assists in writing high-quality content by conducting research, adding citations, improving hooks, iterating on outlines, and providing real-time feedback on each section. Transforms your writing process from solo effort to collaborative partnership.
Automate YouTube Top-Ten Video Creation with OpenAI and Safe Image Search
Integrates OpenAI API for content generation, Bing Image Search API for safe image retrieval, and Pexels API for video footage. Handles authentication via Bearer token, enforces safe search, formats ChatGPT responses into a top-ten list, and includes error handling for API failures.
academic-research-writing
Use when writing CS research papers (conference, journal, thesis), reviewing scientific manuscripts, improving academic writing clarity, or preparing IEEE/ACM submissions. Invoke when user mentions paper, manuscript, research writing, journal submission, or needs help with academic structure, formatting, or revision.