Cue Deep Research

Problem Background

In financial research, competitive benchmarking, and compliance checks, general chatbots often produce untraceable claims, rely on thin single sources, slow down manual site browsing, and keep inconsistent report framing. Teams need a repeatable workflow for evidence collection, cross-checking, and conclusion drafting.

How It Works

The skill sends the question to the Cue service, which schedules MCP tools for retrieval instead of crawling locally in the agent. Key capabilities include:
- Multi-source validation: conclusions are supported by independent sources when possible, with conflicts preserved.
- Traceable outputs: key findings include public source links for review.
- Specialized coverage: A-shares, US stocks, HK stocks, non-listed entities, macro, funds, judicial, regulatory, and sanctions compliance data.
- Style mimicry: use --mimic-url or --mimic-file to follow a reference report's structure and tone, without copying content.

Execution is typically a single free-form deep-research command, taking 3-15 minutes, and returns core conclusions, multi-angle analysis, data validation, and risk notes.

Boundaries

It suits one-off exploratory research and is not a fully offline local search tool. Result quality depends on API Key validity, Cue service status, external source freshness, and availability. If public sources have no match, results may be narrower or empty.