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 fina...

This skill's code does more than a minimal web search helper: it will perform outbound network requests, may call external LLM providers if API keys exist in your environment, can download transformer/embedding models, and will create a local SQLite database. Before installing or enabling it: - Review and restrict environment variables: do not place high-privilege API keys (cloud credentials, wide-scope LLM keys) in the environment unless you trust the code. Prefer using a dedicated, limited-scope key if needed. - Run in an isolated environment (container/VM) to observe network activity and file writes (the skill writes data/signal_flux.db by default). - If you only need basic web search, ask the author to produce a trimmed build that omits LLM/sentiment/model-router code or to explicitly document all required env vars and Python dependencies. - Be aware of large model downloads (transformers / SentenceTransformer) which may occur at runtime and consume bandwidth/storage. - If you need extra assurance, request from the publisher: (1) an explicit dependency list or install script, (2) a list of environment variables the skill will read/use, and (3) confirmation of no hidden external endpoints. Given the metadata omits the environment/dependency footprint, treat this skill as suspicious until those gaps are clarified.

Type: OpenClaw Skill Name: alphaear-search Version: 1.0.0 The alphaear-search skill bundle is a comprehensive search and Retrieval-Augmented Generation (RAG) toolset designed for financial information retrieval. It integrates multiple search engines (Jina, DuckDuckGo, Baidu), implements a local SQLite-based caching system in `database_manager.py`, and provides hybrid search capabilities (BM25 and vector-based) in `hybrid_search.py`. The code includes legitimate utility functions for content extraction via Jina Reader (`content_extractor.py`) and sentiment analysis using BERT or LLMs (`sentiment_tools.py`). No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the instructions in `SKILL.md` and `PROMPTS.md` are strictly aligned with the stated purpose of optimizing search efficiency and information retrieval.